diff --git a/AutocorrRegSymmSteps.m b/AutocorrRegSymmSteps.m
new file mode 100644
index 0000000..c37505c
--- /dev/null
+++ b/AutocorrRegSymmSteps.m
@@ -0,0 +1,7 @@
+% autocorrelation for step symmetrie
+function [c, lags] =  AutocorrRegSymmSteps(x) 
+[c,lags] = xcov(x,200,'unbiased');
+c = c/c(lags==0);
+c = c(lags>=0);
+lags = lags(lags>=0);
+
diff --git a/AutocorrStrides.m b/AutocorrStrides.m
new file mode 100644
index 0000000..b49a726
--- /dev/null
+++ b/AutocorrStrides.m
@@ -0,0 +1,43 @@
+function  [ResultStruct] = AutocorrStrides(data,FS, StrideTimeRange,ResultStruct);
+
+%% Stride-times measures
+% Stride time and regularity from auto correlation (according to Moe-Nilssen and Helbostad, Estimation of gait cycle characteristics by trunk accelerometry. J Biomech, 2004. 37: 121-6.)
+RangeStart = round(FS*StrideTimeRange(1));
+RangeEnd = round(FS*StrideTimeRange(2));
+[AutocorrResult,Lags]=xcov(data,RangeEnd,'unbiased');
+AutocorrSum = sum(AutocorrResult(:,[1 5 9]),2); % This sum is independent of sensor re-orientation, as long as axes are kept orthogonal
+AutocorrResult2= [AutocorrResult(:,[1 5 9]),AutocorrSum];
+IXRange = (numel(Lags)-(RangeEnd-RangeStart)):numel(Lags);
+% check that autocorrelations are positive for any direction,
+
+AutocorrPlusTrans = AutocorrResult+AutocorrResult(:,[1 4 7 2 5 8 3 6 9]);
+IXRangeNew = IXRange( ...
+    AutocorrPlusTrans(IXRange,1) > 0 ...
+    & prod(AutocorrPlusTrans(IXRange,[1 5]),2) > prod(AutocorrPlusTrans(IXRange,[2 4]),2) ...
+    & prod(AutocorrPlusTrans(IXRange,[1 5 9]),2) + prod(AutocorrPlusTrans(IXRange,[2 6 7]),2) + prod(AutocorrPlusTrans(IXRange,[3 4 8]),2) ...
+    > prod(AutocorrPlusTrans(IXRange,[1 6 8]),2) + prod(AutocorrPlusTrans(IXRange,[2 4 9]),2) + prod(AutocorrPlusTrans(IXRange,[3 5 7]),2) ...
+    );
+if isempty(IXRangeNew)
+    StrideTimeSamples = Lags(IXRange(AutocorrSum(IXRange)==max(AutocorrSum(IXRange)))); % to be used in other estimations
+    StrideTimeSeconds = nan;
+    ResultStruct.StrideTimeSamples = StrideTimeSamples;
+    ResultStruct.StrideTimeSeconds = StrideTimeSeconds;
+    
+else
+    StrideTimeSamples = Lags(IXRangeNew(AutocorrSum(IXRangeNew)==max(AutocorrSum(IXRangeNew))));
+    StrideRegularity = AutocorrResult2(Lags==StrideTimeSamples,:)./AutocorrResult2(Lags==0,:); % Moe-Nilssen&Helbostatt,2004
+    RelativeStrideVariability = 1-StrideRegularity;
+    StrideTimeSeconds = StrideTimeSamples/FS;
+    
+    ResultStruct.StrideRegularity_V = StrideRegularity(1);
+    ResultStruct.StrideRegularity_ML = StrideRegularity(2);
+    ResultStruct.StrideRegularity_AP = StrideRegularity(3);
+    ResultStruct.StrideRegularity_All = StrideRegularity(4);
+    ResultStruct.RelativeStrideVariability_V = RelativeStrideVariability(1);
+    ResultStruct.RelativeStrideVariability_ML = RelativeStrideVariability(2);
+    ResultStruct.RelativeStrideVariability_AP = RelativeStrideVariability(3);
+    ResultStruct.RelativeStrideVariability_All = RelativeStrideVariability(4);
+    ResultStruct.StrideTimeSamples = StrideTimeSamples;
+    ResultStruct.StrideTimeSeconds = StrideTimeSeconds;
+    
+end
\ No newline at end of file
diff --git a/CalcMaxLyapConvGait.m b/CalcMaxLyapConvGait.m
new file mode 100644
index 0000000..554056b
--- /dev/null
+++ b/CalcMaxLyapConvGait.m
@@ -0,0 +1,118 @@
+function [L_Estimate,ExtraArgsOut] = CalcMaxLyapConvGait(ThisTimeSeries,FS,ExtraArgsIn)
+if nargin > 2
+    if isfield(ExtraArgsIn,'J')
+        J=ExtraArgsIn.J;
+    end
+    if isfield(ExtraArgsIn,'m')
+        m=ExtraArgsIn.m;
+    end
+    if isfield(ExtraArgsIn,'FitWinLen')
+        FitWinLen=ExtraArgsIn.FitWinLen;
+    end
+end
+
+%% Initialize output args
+L_Estimate=nan;ExtraArgsOut.Divergence=nan;ExtraArgsOut.J=nan;ExtraArgsOut.m=nan;ExtraArgsOut.FitWinLen=nan;
+
+%% Some checks 
+% predefined J and m should not be NaN or Inf
+if (exist('J','var') && ~isempty(J) && ~isfinite(J)) || (exist('m','var') && ~isempty(m) && ~isfinite(m))
+    warning('Predefined J and m cannot be NaN or Inf');
+    return;
+end
+% multidimensional time series need predefined J and m
+if size(ThisTimeSeries,2) > 1 && (~exist('J','var') || ~exist('m','var') || isempty(J) || isempty(m)) 
+    warning('Multidimensional time series needs predefined J and m, can''t determine Lyapunov');
+    return;
+end
+%Check that there are no NaN or Inf values in the TimeSeries
+if any(~isfinite(ThisTimeSeries(:)))
+    warning('Time series contains NaN or Inf, can''t determine Lyapunov');
+    return;
+end
+%Check that there is variation in the TimeSeries
+if ~(nanstd(ThisTimeSeries) > 0)
+    warning('Time series is constant, can''t determine Lyapunov');
+    return;
+end
+
+%% Determine FitWinLen (=cycle time) of ThisTimeSeries
+if ~exist('FitWinLen','var') || isempty(FitWinLen)
+    if size(ThisTimeSeries,2)>1
+        for dim=1:size(ThisTimeSeries,2),
+            [Pd(:,dim),F] = pwelch(detrend(ThisTimeSeries(:,dim)),[],[],[],FS);
+        end
+        P = sum(Pd,2);
+    else
+        [P,F] = pwelch(detrend(ThisTimeSeries),[],[],[],FS);
+    end
+    MeanF = sum(P.*F)./sum(P);
+    CycleTime = 1/MeanF;
+    FitWinLen = round(CycleTime*FS);
+else
+    CycleTime = FitWinLen/FS;
+end
+ExtraArgsOut.FitWinLen=FitWinLen;
+
+%% Determine J
+if ~exist('J','var') || isempty(J)
+    % Calculate mutual information and take first local minimum Tau as J
+    bV = min(40,floor(sqrt(size(ThisTimeSeries,1))));
+    tauVmax = FitWinLen;
+    [mutMPro,cummutMPro,minmuttauVPro] = MutualInformationHisPro(ThisTimeSeries,(0:tauVmax),bV,1); % (xV,tauV,bV,flag)
+    if isnan(minmuttauVPro)
+        display(mutMPro);
+        warning('minmuttauVPro is NaN. Consider increasing tauVmax.');
+        return;
+    end
+    J=minmuttauVPro;
+end
+ExtraArgsOut.J=J;
+
+%% Determine m
+if ~exist('m','var') || isempty(m)
+    escape = 10;
+    max_m = 20;
+    max_fnnM = 0.02;
+    mV = 0;
+    fnnM = 1;
+    for mV = 2:max_m % for m=1, FalseNearestNeighbors is slow and lets matlab close if N>500000
+        fnnM = FalseNearestNeighborsSR(ThisTimeSeries,J,mV,escape,FS); % (xV,tauV,mV,escape,theiler)
+        if fnnM <= max_fnnM || isnan(fnnM)
+            break
+        end
+    end
+    if fnnM <= max_fnnM
+        m = mV;
+    else
+        warning('Too many false nearest neighbours');
+        return;
+    end
+end
+ExtraArgsOut.m=m;
+
+%% Create state space based upon J and m
+N_ss = size(ThisTimeSeries,1)-(m-1)*J;
+StateSpace=nan(N_ss,m*size(ThisTimeSeries,2)); 
+for dim=1:size(ThisTimeSeries,2),
+    for delay=1:m,
+        StateSpace(:,(dim-1)*m+delay)=ThisTimeSeries((1:N_ss)'+(delay-1)*J,dim);
+    end
+end
+
+%% Parameters for Lyapunov
+WindowLen = floor(min(N_ss/5,10*FitWinLen));
+if WindowLen < FitWinLen
+        warning('Not enough samples for Lyapunov estimation');
+        return;
+end    
+WindowLenSec=WindowLen/FS;
+
+%% Calculate divergence
+Divergence=div_calc(StateSpace,WindowLenSec,FS,CycleTime,0);
+ExtraArgsOut.Divergence=Divergence;
+
+%% Calculate slope of first FitWinLen samples of divergence curve
+p = polyfit((1:FitWinLen)/FS,Divergence(1:FitWinLen),1);
+L_Estimate = p(1);
+
diff --git a/CalcMaxLyapWolfFixedEvolv.m b/CalcMaxLyapWolfFixedEvolv.m
new file mode 100644
index 0000000..8021140
--- /dev/null
+++ b/CalcMaxLyapWolfFixedEvolv.m
@@ -0,0 +1,134 @@
+function [L_Estimate,ExtraArgsOut] = CalcMaxLyapWolfFixedEvolv(ThisTimeSeries,FS,ExtraArgsIn)
+
+%% Description
+% This function calculates the maximum Lyapunov exponent from a time 
+% series, based on the method described by Wolf et al. in 
+%    Wolf, A., et al., Determining Lyapunov exponents from a time series. 
+%    Physica D: 8 Nonlinear Phenomena, 1985. 16(3): p. 285-317.
+% 
+% Input:
+%   ThisTimeSeries: a vector or matrix with the time series
+%   FS: sample frequency of the ThisTimeSeries
+%   ExtraArgsIn: a struct containing optional input arguments 
+%       J (embedding delay)
+%       m (embedding dimension)
+% Output:
+%   L_Estimate: The Lyapunov estimate
+%   ExtraArgsOut: a struct containing the additional output arguments
+%       J (embedding delay)
+%       m (embedding dimension)
+
+%% Copyright
+%     COPYRIGHT (c) 2012 Sietse Rispens, VU University Amsterdam
+%
+%     This program is free software: you can redistribute it and/or modify
+%     it under the terms of the GNU General Public License as published by
+%     the Free Software Foundation, either version 3 of the License, or
+%     (at your option) any later version.
+% 
+%     This program is distributed in the hope that it will be useful,
+%     but WITHOUT ANY WARRANTY; without even the implied warranty of
+%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%     GNU General Public License for more details.
+% 
+%     You should have received a copy of the GNU General Public License
+%     along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+%% Author
+%  Sietse Rispens
+
+%% History
+%  April 2012, initial version of CalcMaxLyapWolf
+%  23 October 2012, use fixed evolve time instead of adaptable
+
+if nargin > 2
+    if isfield(ExtraArgsIn,'J')
+        J=ExtraArgsIn.J;
+    end
+    if isfield(ExtraArgsIn,'m')
+        m=ExtraArgsIn.m;
+    end
+end
+
+%% Initialize output args
+L_Estimate=nan;ExtraArgsOut.J=nan;ExtraArgsOut.m=nan;
+
+%% Some checks 
+% predefined J and m should not be NaN or Inf
+if (exist('J','var') && ~isempty(J) && ~isfinite(J)) || (exist('m','var') && ~isempty(m) && ~isfinite(m))
+    warning('Predefined J and m cannot be NaN or Inf');
+    return;
+end
+% multidimensional time series need predefined J and m
+if size(ThisTimeSeries,2) > 1 && (~exist('J','var') || ~exist('m','var') || isempty(J) || isempty(m)) 
+    warning('Multidimensional time series needs predefined J and m, can''t determine Lyapunov');
+    return;
+end
+%Check that there are no NaN or Inf values in the TimeSeries
+if any(~isfinite(ThisTimeSeries(:)))
+    warning('Time series contains NaN or Inf, can''t determine Lyapunov');
+    return;
+end
+%Check that there is variation in the TimeSeries
+if ~(nanstd(ThisTimeSeries) > 0)
+    warning('Time series is constant, can''t determine Lyapunov');
+    return;
+end
+
+%% Determine J
+if ~exist('J','var') || isempty(J)
+    % Calculate mutual information and take first local minimum Tau as J
+    bV = min(40,floor(sqrt(size(ThisTimeSeries,1))));
+    tauVmax = 70;
+    [mutMPro,cummutMPro,minmuttauVPro] = MutualInformationHisPro(ThisTimeSeries,(0:tauVmax),bV,1); % (xV,tauV,bV,flag)
+    if isnan(minmuttauVPro)
+        display(mutMPro);
+        warning('minmuttauVPro is NaN. Consider increasing tauVmax.');
+        return;
+    end
+    J=minmuttauVPro;
+end
+ExtraArgsOut.J=J;
+
+%% Determine m
+if ~exist('m','var') || isempty(m)
+    escape = 10;
+    max_m = 20;
+    max_fnnM = 0.02;
+    mV = 0;
+    fnnM = 1;
+    for mV = 2:max_m % for m=1, FalseNearestNeighbors is slow and lets matlab close if N>500000
+        fnnM = FalseNearestNeighborsSR(ThisTimeSeries,J,mV,escape,FS); % (xV,tauV,mV,escape,theiler)
+        if fnnM <= max_fnnM || isnan(fnnM)
+            break
+        end
+    end
+    if fnnM <= max_fnnM
+        m = mV;
+    else
+        warning('Too many false nearest neighbours');
+        return;
+    end
+end
+ExtraArgsOut.m=m;
+
+%% Create state space based upon J and m
+N_ss = size(ThisTimeSeries,1)-(m-1)*J;
+StateSpace=nan(N_ss,m*size(ThisTimeSeries,2)); 
+for dim=1:size(ThisTimeSeries,2),
+    for delay=1:m,
+        StateSpace(:,(dim-1)*m+delay)=ThisTimeSeries((1:N_ss)'+(delay-1)*J,dim);
+    end
+end
+
+%% Parameters for Lyapunov estimation
+CriticalLen=J*m;
+max_dist = sqrt(sum(std(StateSpace).^2))/10;
+max_dist_mult = 5;
+min_dist = max_dist/2;
+max_theta = 0.3;
+evolv = J;
+
+%% Calculate Lambda
+[L_Estimate]=div_wolf_fixed_evolv(StateSpace, FS, min_dist, max_dist, max_dist_mult, max_theta, CriticalLen, evolv);
+
diff --git a/CalculateNonLinearParametersFunc.m b/CalculateNonLinearParametersFunc.m
new file mode 100644
index 0000000..61b6dbe
--- /dev/null
+++ b/CalculateNonLinearParametersFunc.m
@@ -0,0 +1,48 @@
+function [ResultStruct] = CalculateNonLinearParametersFunc(ResultStruct,dataAccCut,WindowLen,FS,Lyap_m,Lyap_FitWinLen,Sen_m,Sen_r)
+
+%% Calculation non-linear parameters;
+
+% cut into windows of size WindowLen
+N_Windows = floor(size(dataAccCut,1)/WindowLen);
+N_SkipBegin = ceil((size(dataAccCut,1)-N_Windows*WindowLen)/2);
+LyapunovWolf = nan(N_Windows,3);
+LyapunovRosen = nan(N_Windows,3);
+SE= nan(N_Windows,3);
+
+for WinNr = 1:N_Windows;
+    AccWin = dataAccCut(N_SkipBegin+(WinNr-1)*WindowLen+(1:WindowLen),:);
+    for j=1:3
+        [LyapunovWolf(WinNr,j),~] = CalcMaxLyapWolfFixedEvolv(AccWin(:,j),FS,struct('m',Lyap_m));
+        [LyapunovRosen(WinNr,j),outpo] = CalcMaxLyapConvGait(AccWin(:,j),FS,struct('m',Lyap_m,'FitWinLen',Lyap_FitWinLen));
+        [SE(WinNr,j)] = funcSampleEntropy(AccWin(:,j), Sen_m, Sen_r);
+        % no correction for FS; SE does increase with higher FS but effect is considered negligible as range is small (98-104HZ). Might consider updating r to account for larger ranges.
+    end
+end
+
+LyapunovWolf = nanmean(LyapunovWolf,1);
+LyapunovRosen = nanmean(LyapunovRosen,1);
+SampleEntropy = nanmean(SE,1);
+
+ResultStruct.LyapunovWolf_V = LyapunovWolf(1);
+ResultStruct.LyapunovWolf_ML = LyapunovWolf(2);
+ResultStruct.LyapunovWolf_AP = LyapunovWolf(3);
+ResultStruct.LyapunovRosen_V = LyapunovRosen(1);
+ResultStruct.LyapunovRosen_ML = LyapunovRosen(2);
+ResultStruct.LyapunovRosen_AP = LyapunovRosen(3);
+ResultStruct.SampleEntropy_V = SampleEntropy(1);
+ResultStruct.SampleEntropy_ML = SampleEntropy(2);
+ResultStruct.SampleEntropy_AP = SampleEntropy(3);
+
+if isfield(ResultStruct,'StrideFrequency')
+    LyapunovPerStrideWolf = LyapunovWolf/ResultStruct.StrideFrequency;
+    LyapunovPerStrideRosen = LyapunovRosen/ResultStruct.StrideFrequency;
+end
+
+ResultStruct.LyapunovPerStrideWolf_V = LyapunovPerStrideWolf(1);
+ResultStruct.LyapunovPerStrideWolf_ML = LyapunovPerStrideWolf(2);
+ResultStruct.LyapunovPerStrideWolf_AP = LyapunovPerStrideWolf(3);
+ResultStruct.LyapunovPerStrideRosen_V = LyapunovPerStrideRosen(1);
+ResultStruct.LyapunovPerStrideRosen_ML = LyapunovPerStrideRosen(2);
+ResultStruct.LyapunovPerStrideRosen_AP = LyapunovPerStrideRosen(3);
+
+end 
\ No newline at end of file
diff --git a/CalculateStrideParametersFunc.m b/CalculateStrideParametersFunc.m
new file mode 100644
index 0000000..df5c40d
--- /dev/null
+++ b/CalculateStrideParametersFunc.m
@@ -0,0 +1,48 @@
+function [ResultStruct] = CalculateStrideParametersFunc(dataAccCut_filt,FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange)
+
+%% Calculate stride parameters
+ResultStruct = struct; % create empty struct
+
+% Run function AutoCorrStrides, Outcomeparameters: StrideRegularity,RelativeStrideVariability,StrideTimeSamples,StrideTime
+[ResultStruct] = AutocorrStrides(dataAccCut_filt,FS, StrideTimeRange,ResultStruct);
+StrideTimeSamples = ResultStruct.StrideTimeSamples; % needed as input for other functions
+
+% Calculate Step symmetry --> method 1
+ij = 1;
+dirSymm = [1,3]; % Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4.
+
+for jk=1:length(dirSymm)
+    [C, lags] = AutocorrRegSymmSteps(dataAccCut_filt(:,dirSymm(jk)));
+    [Ad,p] = findpeaks(C,'MinPeakProminence',0.2, 'MinPeakHeight', 0.2);
+    
+    if size(Ad,1) > 1
+        Ad1 = Ad(1);
+        Ad2 = Ad(2);
+        GaitSymm(:,ij) = abs((Ad1-Ad2)/mean([Ad1+Ad2]))*100;
+    else
+        GaitSymm(:,ij) = NaN;
+    end
+    ij = ij +1;
+end
+% Save outcome in struct;
+ResultStruct.GaitSymm_V = GaitSymm(1);
+ResultStruct.GaitSymm_AP = GaitSymm(2);
+
+% Calculate Step symmetry --> method 2
+[PksAndLocsCorrected] = StepcountFunc(dataAccCut_filt,StrideTimeSamples,FS);
+LocsSteps = PksAndLocsCorrected(2:2:end,2);
+
+if rem(size(LocsSteps,1),2) == 0; % is number of steps is even
+    LocsSteps2 = LocsSteps(1:2:end);
+else
+    LocsSteps2 = LocsSteps(3:2:end);
+end
+
+LocsSteps1 = LocsSteps(2:2:end);
+DiffLocs2 = diff(LocsSteps2);
+DiffLocs1 = diff(LocsSteps1);
+StepTime2 = DiffLocs2(1:end-1)/FS; % leave last one out because it is higher
+StepTime1 = DiffLocs1(1:end-1)/FS;
+SI = abs((2*(StepTime2-StepTime1))./(StepTime2+StepTime1))*100;
+ResultStruct.GaitSymmIndex = nanmean(SI);
+end 
diff --git a/FilterandRealignFunc.m b/FilterandRealignFunc.m
new file mode 100644
index 0000000..d63fa5d
--- /dev/null
+++ b/FilterandRealignFunc.m
@@ -0,0 +1,18 @@
+function [dataAcc, dataAcc_filt] = FilterandRealignFunc(inputData,FS,ApplyRealignment)
+
+%% Filter and Realign Accdata
+
+% Apply Realignment & Filter data
+
+if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
+    data = inputData(:, [3,2,4]); % reorder data to  1 = V; 2= ML, 3 = AP%
+    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
+    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
+    dataAcc = RealignedAcc;
+    [B,A] = butter(2,20/(FS/2),'low');
+    dataAcc_filt = filtfilt(B,A,dataAcc);
+else % we asume tat data is already reorderd to 1 = V; 2= ML, 3 = AP in an earlier stage;
+    [B,A] = butter(2,20/(FS/2),'low');
+    dataAcc = inputData;
+    dataAcc_filt = filtfilt(B,A,dataAcc);
+end
diff --git a/GaitOutcomesTrunkAccFuncIH.m b/GaitOutcomesTrunkAccFuncIH.m
new file mode 100644
index 0000000..7fa4d7a
--- /dev/null
+++ b/GaitOutcomesTrunkAccFuncIH.m
@@ -0,0 +1,210 @@
+function [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps)
+
+% DESCRIPTON: Trunk analysis of Iphone data without the need for step detection
+% CL Nov 2019
+% Adapted IH feb-april 2020
+
+% koloms data of smartphone
+% 1st column is time data;
+% 2nd column is X, medio-lateral: + left, - right
+% 3rd column is Y, vertical: + downwards, - upwards
+% 4th column is Z, anterior- posterior : + forwards, - backwards
+
+%% Input Trunk accelerations during locomotion in VT, ML, AP direction
+% InputData: Acceleration signal with time and accelerations in VT,ML and
+% AP direction.
+% FS: sample frequency of the Accdata
+% LegLength: length of the leg of the participant in m;
+
+
+%% Output
+% ResultStruct: structure coninting all outcome measured calculated
+% Spectral parameters, spatiotemporal gait parameters, non-linear
+% parameters
+% fields and subfields: include the multiple measurements of a subject
+
+%% Literature
+% Richman & Moorman, 2000; [ sample entropy]
+% Bisi & Stagni Gait & Posture 2016,  47 (6) 37-42
+% Kavagnah et al., Eur J Appl Physiol 2005 94: 468?475; Human Movement Science 24(2005) 574?587 [ synchrony]
+% Moe-Nilsen J Biomech 2004 37, 121-126 [ autorcorrelation step regularity and symmetry
+% Kobsar et al. Gait & Posture 2014 39, 553?557 [ synchrony ]
+% Rispen et al; Gait & Posture 2014, 40, 187 - 192 [realignment axes]
+% Zijlstra & HofGait & Posture 2003  18,2, 1-10 [spatiotemporal gait variables]
+% Lamoth et al, 2002  [index of harmonicity]
+% Costa et al. 2003 Physica A 330 (2003) 53–60 [ multiscale entropy]
+% Cignetti F, Decker LM, Stergiou N. Ann Biomed Eng. 2012
+% May;40(5):1122-30. doi: 10.1007/s10439-011-0474-3. Epub 2011 Nov 25. [
+% Wofl vs. Rosenstein Lyapunov]
+
+
+%% Settings
+Gr = 9.81;                         % Gravity acceleration, multiplication factor for accelerations
+StrideFreqEstimate = 1.00;         % Used to set search for stride frequency from 0.5*StrideFreqEstimate until 2*StrideFreqEstimate
+StrideTimeRange = [0.2 4.0];       % Range to search for stride time (seconds)
+IgnoreMinMaxStrides = 0.10;        % Number or percentage of highest&lowest values ignored for improved variability estimation
+N_Harm = 12;                       % Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation
+LowFrequentPowerThresholds = ...
+    [0.7 1.4];                     % Threshold frequencies for estimation of low-frequent power percentages
+Lyap_m = 7;                        % Embedding dimension (used in Lyapunov estimations)
+Lyap_FitWinLen = round(60/100*FS); % Fitting window length (used in Lyapunov estimations Rosenstein's method)
+Sen_m = 5;                         % Dimension, the length of the subseries to be matched (used in sample entropy estimation)
+Sen_r = 0.3;                       % Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation)
+NStartEnd = [100];
+M  = 5;                            % maximum template length
+ResultStruct = struct();
+
+%% Filter and Realign Accdata
+
+% Apply Realignment & Filter data
+
+if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
+    data = inputData(:, [3,2,4]); % reorder data to  1 = V; 2= ML, 3 = AP%
+    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
+    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
+    dataAcc = RealignedAcc;
+    [B,A] = butter(2,20/(FS/2),'low');
+    dataAcc_filt = filtfilt(B,A,dataAcc);
+else % we asume tat data is already reorderd to 1 = V; 2= ML, 3 = AP in an earlier stage;
+    [B,A] = butter(2,20/(FS/2),'low');
+    dataAcc = inputData;
+    dataAcc_filt = filtfilt(B,A,dataAcc);
+end
+
+
+%% Step dectection
+% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed
+
+if ApplyRemoveSteps
+    
+    % In order to run the step detection script we first need to run an autocorrelation function;
+    [ResultStruct] = AutocorrStrides(dataAcc_filt,FS, StrideTimeRange,ResultStruct);
+    
+    % StrideTimeSamples is needed as an input for the stepcountFunc;
+    StrideTimeSamples = ResultStruct.StrideTimeSamples;
+    
+    % Calculate the number of steps;
+    [PksAndLocsCorrected] = StepcountFunc(dataAcc_filt,StrideTimeSamples,FS);
+    % This function selects steps based on negative and positive values.
+    % However to determine the steps correctly we only need one of these;
+    LocsSteps = PksAndLocsCorrected(1:2:end,2);
+    
+    %% Cut data & remove currents results
+    % Remove 20 steps in the beginning and end of data
+    dataAccCut = dataAcc(LocsSteps(31):LocsSteps(end-30),:);
+    dataAccCut_filt = dataAcc_filt(LocsSteps(31):LocsSteps(end-30),:);
+    
+    % Clear currently saved results from Autocorrelation Analysis
+    
+    clear ResultStruct;
+    clear PksAndLocsCorrected;
+    clear LocsSteps;
+    
+else;
+    dataAccCut = dataAcc;
+    dataAccCut_filt = dataAcc_filt;
+end
+
+%% Calculate stride parameters
+ResultStruct = struct; % create empty struct
+
+% Run function AutoCorrStrides, Outcomeparameters: StrideRegularity,RelativeStrideVariability,StrideTimeSamples,StrideTime
+[ResultStruct] = AutocorrStrides(dataAccCut_filt,FS, StrideTimeRange,ResultStruct);
+StrideTimeSamples = ResultStruct.StrideTimeSamples; % needed as input for other functions
+
+% Calculate Step symmetry --> method 1
+ij = 1;
+dirSymm = [1,3]; % Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4.
+
+for jk=1:length(dirSymm)
+    [C, lags] = AutocorrRegSymmSteps(dataAccCut_filt(:,dirSymm(jk)));
+    [Ad,p] = findpeaks(C,'MinPeakProminence',0.2, 'MinPeakHeight', 0.2);
+    
+    if size(Ad,1) > 1
+        Ad1 = Ad(1);
+        Ad2 = Ad(2);
+        GaitSymm(:,ij) = abs((Ad1-Ad2)/mean([Ad1+Ad2]))*100;
+    else
+        GaitSymm(:,ij) = NaN;
+    end
+    ij = ij +1;
+end
+% Save outcome in struct;
+ResultStruct.GaitSymm_V = GaitSymm(1);
+ResultStruct.GaitSymm_AP = GaitSymm(2);
+
+% Calculate Step symmetry --> method 2
+[PksAndLocsCorrected] = StepcountFunc(dataAccCut_filt,StrideTimeSamples,FS);
+LocsSteps = PksAndLocsCorrected(2:2:end,2);
+
+if rem(size(LocsSteps,1),2) == 0; % is number of steps is even
+    LocsSteps2 = LocsSteps(1:2:end);
+else
+    LocsSteps2 = LocsSteps(3:2:end);
+end
+
+LocsSteps1 = LocsSteps(2:2:end);
+DiffLocs2 = diff(LocsSteps2);
+DiffLocs1 = diff(LocsSteps1);
+StepTime2 = DiffLocs2(1:end-1)/FS; % leave last one out because it is higher
+StepTime1 = DiffLocs1(1:end-1)/FS;
+SI = abs((2*(StepTime2-StepTime1))./(StepTime2+StepTime1))*100;
+ResultStruct.GaitSymmIndex = nanmean(SI);
+
+%% Calculate spatiotemporal stride parameters
+
+% Measures from height variation by double integration of VT accelerations and high-pass filtering
+[ResultStruct] = SpatioTemporalGaitParameters(dataAccCut_filt,StrideTimeSamples,ApplyRealignment,LegLength,FS,IgnoreMinMaxStrides,ResultStruct);
+
+%% Measures derived from spectral analysis
+
+AccVectorLen = sqrt(sum(dataAccCut_filt(:,1:3).^2,2));
+[ResultStruct] = SpectralAnalysisGaitfunc(dataAccCut_filt,WindowLen,FS,N_Harm,LowFrequentPowerThresholds,AccVectorLen,ResultStruct);
+
+
+%% Calculation non-linear parameters;
+
+% cut into windows of size WindowLen
+N_Windows = floor(size(dataAccCut,1)/WindowLen);
+N_SkipBegin = ceil((size(dataAccCut,1)-N_Windows*WindowLen)/2);
+LyapunovWolf = nan(N_Windows,3);
+LyapunovRosen = nan(N_Windows,3);
+SE= nan(N_Windows,3);
+
+for WinNr = 1:N_Windows;
+    AccWin = dataAccCut(N_SkipBegin+(WinNr-1)*WindowLen+(1:WindowLen),:);
+    for j=1:3
+        [LyapunovWolf(WinNr,j),~] = CalcMaxLyapWolfFixedEvolv(AccWin(:,j),FS,struct('m',Lyap_m));
+        [LyapunovRosen(WinNr,j),outpo] = CalcMaxLyapConvGait(AccWin(:,j),FS,struct('m',Lyap_m,'FitWinLen',Lyap_FitWinLen));
+        [SE(WinNr,j)] = funcSampleEntropy(AccWin(:,j), Sen_m, Sen_r);
+        % no correction for FS; SE does increase with higher FS but effect is considered negligible as range is small (98-104HZ). Might consider updating r to account for larger ranges.
+    end
+end
+
+LyapunovWolf = nanmean(LyapunovWolf,1);
+LyapunovRosen = nanmean(LyapunovRosen,1);
+SampleEntropy = nanmean(SE,1);
+
+ResultStruct.LyapunovWolf_V = LyapunovWolf(1);
+ResultStruct.LyapunovWolf_ML = LyapunovWolf(2);
+ResultStruct.LyapunovWolf_AP = LyapunovWolf(3);
+ResultStruct.LyapunovRosen_V = LyapunovRosen(1);
+ResultStruct.LyapunovRosen_ML = LyapunovRosen(2);
+ResultStruct.LyapunovRosen_AP = LyapunovRosen(3);
+ResultStruct.SampleEntropy_V = SampleEntropy(1);
+ResultStruct.SampleEntropy_ML = SampleEntropy(2);
+ResultStruct.SampleEntropy_AP = SampleEntropy(3);
+
+if isfield(ResultStruct,'StrideFrequency')
+    LyapunovPerStrideWolf = LyapunovWolf/ResultStruct.StrideFrequency;
+    LyapunovPerStrideRosen = LyapunovRosen/ResultStruct.StrideFrequency;
+end
+
+ResultStruct.LyapunovPerStrideWolf_V = LyapunovPerStrideWolf(1);
+ResultStruct.LyapunovPerStrideWolf_ML = LyapunovPerStrideWolf(2);
+ResultStruct.LyapunovPerStrideWolf_AP = LyapunovPerStrideWolf(3);
+ResultStruct.LyapunovPerStrideRosen_V = LyapunovPerStrideRosen(1);
+ResultStruct.LyapunovPerStrideRosen_ML = LyapunovPerStrideRosen(2);
+ResultStruct.LyapunovPerStrideRosen_AP = LyapunovPerStrideRosen(3);
+
+end
\ No newline at end of file
diff --git a/GaitVariabilityAnalysisIH.m b/GaitVariabilityAnalysisIH.m
new file mode 100644
index 0000000..f40b72c
--- /dev/null
+++ b/GaitVariabilityAnalysisIH.m
@@ -0,0 +1,74 @@
+% Gait Variability Analysis 
+% Script created for BAP students 2020
+% Iris Hagoort
+% April 2020
+
+% Input: needs mat file which contains all raw accelerometer data
+% Input: needs excel file containing the participant information including
+% leg length. 
+
+%% Clear and close;
+clear;
+close all;
+
+%% Load data
+load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData
+load('ExcelInfo.mat');
+Participants = fields(AccData);
+
+%% Settings
+FS = 100; % sample frequency
+LegLengths = excel.data.GeneralInformation(:,5); % leglength info is in 5th column
+LegLengthsM = LegLengths/100; % convert to m
+
+%% Calculate parameters;
+for i = 1:  length(Participants);
+tic;   
+    LegLength = LegLengthsM(i);
+    WalkingConditions = fields(AccData.([char(Participants(i))]));
+    
+    for j = 1: length(WalkingConditions);
+        
+        if strcmp(char(WalkingConditions(j)),'Treadmill')
+            
+            SubConditions = fieldnames(AccData.([char(Participants(i))]).([char(WalkingConditions(j))]));
+            
+            for k = 1: length(SubConditions);
+               inputData = AccData.([char(Participants(i))]).([char(WalkingConditions(j))]).([char(SubConditions(k))]);
+               WindowLength = FS*10;
+               ApplyRealignment = true;
+               ApplyRemoveSteps = true;
+               [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps);
+                OutcomesAcc.([char(Participants(i))]).([char(WalkingConditions(j))]).([char(SubConditions(k))]) = ResultStruct;
+            end
+            
+        elseif strcmp(char(WalkingConditions(j)),'Balance') || strcmp(char(WalkingConditions(j)),'TwoMWT') 
+              disp('Files are not used for current analysis');
+            
+        elseif strcmp(char(WalkingConditions(j)),'InsideStraight')
+            inputData = AccData.([char(Participants(i))]).([char(WalkingConditions(j))]);
+            ApplyRealignment = true;
+            ApplyRemoveSteps = false; % don't remove steps for the straight conditions
+            % function specific for the walking conditions containing a lot
+            % of turns
+            [ResultStruct] = GaitVariabilityAnalysisIH_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);
+            OutcomesAcc.([char(Participants(i))]).([char(WalkingConditions(j))]) = ResultStruct;
+            
+        else
+            inputData = AccData.([char(Participants(i))]).([char(WalkingConditions(j))]);
+            ApplyRealignment = true;
+            ApplyRemoveSteps = true;
+            WindowLen = FS*10;
+            [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps)
+            OutcomesAcc.([char(Participants(i))]).([char(WalkingConditions(j))]) = ResultStruct;
+        end
+    end
+    
+ toc;  
+end
+
+% Save struct as .mat file
+save('GaitVarOutcomes30pril.mat', 'OutcomesAcc');
+
+
+
diff --git a/GaitVariabilityAnalysisIH_WithoutTurns.m b/GaitVariabilityAnalysisIH_WithoutTurns.m
new file mode 100644
index 0000000..21c90bb
--- /dev/null
+++ b/GaitVariabilityAnalysisIH_WithoutTurns.m
@@ -0,0 +1,108 @@
+function [ResultStruct] = GaitVariabilityAnalysisIH_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);
+
+
+% SCRIPT FOR ANAlysis straight parts
+% NOG GOEDE BESCHRIJVING TOEVOEGEN.
+
+%% Realign data
+data = inputData(:, [3,2,4]); % reorder data to  1 = V; 2= ML, 3 = AP
+
+%Realign sensor data to VT-ML-AP frame
+if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
+    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
+    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
+    dataAcc = RealignedAcc;
+end
+
+%% Filter data strongly & Determine location of steps
+
+% Filter  data
+[B,A] = butter(2,3/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly
+dataStepDetection = filtfilt(B,A,dataAcc);
+
+% Determine steps;
+
+%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%%
+
+% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html
+% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,
+% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,
+% such as steps taken while walking, regardless of device orientation.
+
+magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));
+magNoGfilt = magfilt - mean(magfilt);
+minPeakHeight2 = std(magNoGfilt);
+[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2); % for step detection
+numStepsOption2_filt = numel(pks); % counts number of steps;
+
+%%%%%%%%%%%%%%%%%%%%%%%% TOT HIER %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% Determine locations of turns;
+
+diffLocs = diff(locs); % calculates difference in step location
+avg_diffLocs = mean(diffLocs); % average distance between steps
+std_diffLocs = std(diffLocs); % standard deviation of distance between steps
+
+figure;
+findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',5); % these values have been chosen based on visual inspection of the signal
+line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])
+[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',5);
+locsTurns =  [locs(locs_diffLocs),  locs(locs_diffLocs+1)];
+
+%% Visualizing turns
+
+% Duplying signal + visualing
+% to make second signal with the locations of the turns filled with NaN, so
+% that both signals can be plotted above each other in a different colour
+
+magNoGfilt_copy = magNoGfilt;
+for k = 1: size(locsTurns,1);
+    magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
+end
+
+
+% visualising signal;
+figure;
+subplot(2,1,1)
+hold on;
+plot(magNoGfilt,'b')
+plot(magNoGfilt_copy, 'r');
+title('Inside Straight: Filtered data with turns highlighted in blue')
+hold off;
+
+%% Calculation
+% VRAAG LAURENS zie blauwe blaadje
+
+startPos = 1;
+for i = 1: size(locsTurns,1);
+    endPos = locsTurns(i,1)-1;
+    
+    inputData = dataAcc(startPos:endPos,:);
+    WindowLen = size(inputData,1);
+    ApplyRealignment = false;
+    [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); % Naam van deze moet nog aangepast.
+    
+    if i ==1 % only the firs time
+        Parameters = fieldnames(ResultStruct);
+        NrParameters = length(Parameters);
+    end
+    
+    for j = 1:NrParameters % only works if for every bin we get the same outcomes (which is the case in this script)
+        DataStraight.([char(Parameters(j))])(i) = ResultStruct.([char(Parameters(j))]);
+    end
+    startPos = locsTurns(i,2)+1;
+    
+end
+
+clear ResultStruct;
+
+% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside
+% straight;
+
+for j = 1:NrParameters;
+    ResultStruct.([char(Parameters(j))]) = nanmean(DataStraight.([char(Parameters(j))]))
+end
+
+
+end
+
diff --git a/GaitVariabilityAnalysisLD.html b/GaitVariabilityAnalysisLD.html
new file mode 100644
index 0000000..122281a
--- /dev/null
+++ b/GaitVariabilityAnalysisLD.html
@@ -0,0 +1,1709 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
+<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge,IE=9,chrome=1"><meta name="generator" content="MATLAB 2020b"><title>Gait Variability Analysis CLBP</title><style type="text/css">.rtcContent { padding: 30px; } .S0 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(213, 80, 0); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }
+.S1 { margin-bottom: 20px; padding-bottom: 4px;  }
+.S2 { margin: 0px; padding: 10px 0px 10px 5px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(0, 0, 0); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 700; text-align: start;  }
+.S3 { margin: -1px 0px 0px; padding: 10px 0px 10px 7px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(0, 0, 0); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: start;  }
+.CodeBlock { background-color: #F7F7F7; margin: 10px 0 10px 0;}
+.S4 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 1px solid rgb(233, 233, 233); border-bottom: 0px none rgb(0, 0, 0); border-radius: 4px 4px 0px 0px; padding: 6px 45px 0px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S5 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 0px none rgb(0, 0, 0); border-bottom: 0px none rgb(0, 0, 0); border-radius: 0px; padding: 0px 45px 0px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S6 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 0px none rgb(0, 0, 0); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S7 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 20px; min-height: 0px; white-space: pre-wrap; color: rgb(60, 60, 60); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left;  }
+.S8 { margin: 2px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(0, 0, 0); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }
+.S9 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 0px none rgb(0, 0, 0); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 0px; padding: 0px 45px 4px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S10 { color: rgb(64, 64, 64); padding: 10px 0px 6px 17px; background: rgb(255, 255, 255) none repeat scroll 0% 0% / auto padding-box border-box; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; overflow-x: hidden; line-height: 17.234px;  }
+.variableValue { width: 100% !important; }
+.embeddedOutputsMatrixElement,.eoOutputWrapper .matrixElement {min-height: 18px; box-sizing: border-box;}
+.embeddedOutputsMatrixElement .matrixElement,.eoOutputWrapper .matrixElement,.rtcDataTipElement .matrixElement {position: relative;}
+.matrixElement .variableValue,.rtcDataTipElement .matrixElement .variableValue {white-space: pre; display: inline-block; vertical-align: top; overflow: hidden;}
+.embeddedOutputsMatrixElement.inlineElement {}
+.embeddedOutputsMatrixElement.inlineElement .topHeaderWrapper {display: none;}
+.embeddedOutputsMatrixElement.inlineElement .veTable .body {padding-top: 0 !important; max-height: 100px;}
+.inlineElement .matrixElement {max-height: 300px;}
+.embeddedOutputsMatrixElement.rightPaneElement {}
+.rightPaneElement .matrixElement,.rtcDataTipElement .matrixElement {overflow: hidden; padding-left: 9px;}
+.rightPaneElement .matrixElement {margin-bottom: -1px;}
+.embeddedOutputsMatrixElement .matrixElement .valueContainer,.eoOutputWrapper .matrixElement .valueContainer,.rtcDataTipElement .matrixElement .valueContainer {white-space: nowrap; margin-bottom: 3px;}
+.embeddedOutputsMatrixElement .matrixElement .valueContainer .horizontalEllipsis.hide,.embeddedOutputsMatrixElement .matrixElement .verticalEllipsis.hide,.eoOutputWrapper .matrixElement .valueContainer .horizontalEllipsis.hide,.eoOutputWrapper .matrixElement .verticalEllipsis.hide,.rtcDataTipElement .matrixElement .valueContainer .horizontalEllipsis.hide,.rtcDataTipElement .matrixElement .verticalEllipsis.hide {display: none;}
+.embeddedOutputsVariableMatrixElement .matrixElement .valueContainer.hideEllipses .verticalEllipsis, .embeddedOutputsVariableMatrixElement .matrixElement .valueContainer.hideEllipses .horizontalEllipsis {display:none;}
+.embeddedOutputsMatrixElement .matrixElement .valueContainer .horizontalEllipsis,.eoOutputWrapper .matrixElement .valueContainer .horizontalEllipsis {margin-bottom: -3px;}
+.eoOutputWrapper .embeddedOutputsVariableMatrixElement .matrixElement .valueContainer {cursor: default !important;}
+.embeddedOutputsVariableElement {white-space: pre-wrap; word-wrap: break-word; min-height: 18px; max-height: 250px; overflow: auto;}
+.variableElement {}
+.embeddedOutputsVariableElement.inlineElement {}
+.inlineElement .variableElement {}
+.embeddedOutputsVariableElement.rightPaneElement {min-height: 16px;}
+.rightPaneElement .variableElement {padding-top: 2px; padding-left: 9px;}
+.variableNameElement {margin-bottom: 3px; display: inline-block;}
+.matrixElement .horizontalEllipsis,.rtcDataTipElement .matrixElement .horizontalEllipsis {display: inline-block; margin-top: 3px; width: 30px; height: 12px; background-repeat: no-repeat; background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAJCAYAAADO1CeCAAAAJUlEQVR42mP4//8/A70xw0i29BUDFPxnAEtTW37wWDqakIa4pQDvOOG89lHX2gAAAABJRU5ErkJggg==");}
+.matrixElement .verticalEllipsis,.textElement .verticalEllipsis,.rtcDataTipElement .matrixElement .verticalEllipsis,.rtcDataTipElement .textElement .verticalEllipsis {margin-left: 35px; width: 12px; height: 30px; background-repeat: no-repeat; background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAZCAYAAAAIcL+IAAAALklEQVR42mP4//8/AzGYgWyFMECMwv8QddRS+P//KyimlmcGUOFoOI6GI/UVAgDnd8Dd4+NCwgAAAABJRU5ErkJggg==");}
+.S11 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 1px solid rgb(233, 233, 233); border-bottom: 0px none rgb(0, 0, 0); border-radius: 0px; padding: 6px 45px 0px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S12 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 1px solid rgb(233, 233, 233); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 4px 4px 0px 0px; padding: 6px 45px 4px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S13 { margin: 20px 10px 5px 4px; padding: 0px; line-height: 20px; min-height: 0px; white-space: pre-wrap; color: rgb(60, 60, 60); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left;  }
+.embeddedOutputsErrorElement {min-height: 18px; max-height: 250px; overflow: auto;}
+.embeddedOutputsErrorElement.inlineElement {}
+.embeddedOutputsErrorElement.rightPaneElement {}
+.embeddedOutputsWarningElement{min-height: 18px; max-height: 250px; overflow: auto;}
+.embeddedOutputsWarningElement.inlineElement {}
+.embeddedOutputsWarningElement.rightPaneElement {}
+.diagnosticMessage-wrapper {font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px;}
+.diagnosticMessage-wrapper.diagnosticMessage-warningType {color: rgb(255,100,0);}
+.diagnosticMessage-wrapper.diagnosticMessage-warningType a {color: rgb(255,100,0); text-decoration: underline;}
+.diagnosticMessage-wrapper.diagnosticMessage-errorType {color: rgb(230,0,0);}
+.diagnosticMessage-wrapper.diagnosticMessage-errorType a {color: rgb(230,0,0); text-decoration: underline;}
+.diagnosticMessage-wrapper .diagnosticMessage-messagePart,.diagnosticMessage-wrapper .diagnosticMessage-causePart {white-space: pre-wrap;}
+.diagnosticMessage-wrapper .diagnosticMessage-stackPart {white-space: pre;}
+.embeddedOutputsTextElement,.embeddedOutputsVariableStringElement {white-space: pre; word-wrap: initial; min-height: 18px; max-height: 250px; overflow: auto;}
+.textElement,.rtcDataTipElement .textElement {padding-top: 3px;}
+.embeddedOutputsTextElement.inlineElement,.embeddedOutputsVariableStringElement.inlineElement {}
+.inlineElement .textElement {}
+.embeddedOutputsTextElement.rightPaneElement,.embeddedOutputsVariableStringElement.rightPaneElement {min-height: 16px;}
+.rightPaneElement .textElement {padding-top: 2px; padding-left: 9px;}
+.S14 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(0, 0, 0); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }
+.S15 { margin: 10px 0px 20px; padding-left: 0px; font-family: Helvetica, Arial, sans-serif; font-size: 14px;  }
+.S16 { margin-left: 56px; line-height: 21px; min-height: 0px; text-align: left; white-space: pre-wrap;  }
+.S17 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(60, 60, 60); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left;  }
+.S18 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 1px solid rgb(233, 233, 233); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 0px 0px 4px 4px; padding: 6px 45px 4px 13px; line-height: 17.234px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_D37184EE' ><span>Gait Variability Analysis CLBP</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#H_7D86BAF1"><span>Clear and close;
+</span></a><a href = "#H_82614DF8"><span>Load data;
+</span></a><a href = "#H_04FF0795"><span>Settings;
+</span></a><a href = "#H_A9CB60AB"><span>Plot the data;
+</span></a><a href = "#H_CA962223"><span>Calculate parameters;
+</span></a><span>    </span><a href = "#H_2EA43FAC"><span>Index of harmonicity (Lamoth et al. 2002)
+</span></a><span>    </span><a href = "#H_3E396779"><span>Lyapunov exponents (Wolfs vs. Rosenstein)
+</span></a><a href = "#H_9FD2A70A"><span>Visualize step detection;</span></a></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Gait Variability Analysis </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Script created for MAP 2020-2021</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% adapted from Claudine Lamoth and Iris Hagoort</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% version1 October 2020</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Input: needs mat file which contains all raw accelerometer data</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Input: needs excel file containing the participant information including</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% leg length.</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S6'></div></div></div><h2  class = 'S7' id = 'H_7D86BAF1' ><span>Clear and close;</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span>clear;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre;"><span>close </span><span style="color: rgb(170, 4, 249);">all</span><span>;</span></span></div></div></div><h2  class = 'S7' id = 'H_82614DF8' ><span>Load data;</span></h2><div  class = 'S8'><span>Select 1 trial. </span><span style=' font-weight: bold;'>For loop to import all data will be used at a later stage</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span>[FNaam,FilePad] = uigetfile(</span><span style="color: rgb(170, 4, 249);">'*.xls'</span><span>,</span><span style="color: rgb(170, 4, 249);">'Load phyphox data...'</span><span>);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>filename =[FilePad FNaam];</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre;"><span>PhyphoxData = xlsread(filename)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableMatrixElement" uid="30B7F20C" data-testid="output_0" data-width="859" style="width: 889px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 859px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">PhyphoxData = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: normal; color: rgb(179, 179, 179); font-size: 12px;">24092×4</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:66,&quot;totalColumns&quot;:4,&quot;totalRows&quot;:24092,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 266px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">         0    0.4018   -8.5041    4.8779
+    0.0100    0.3962   -8.4703    4.7944
+    0.0199    0.4335   -8.4378    4.7159
+    0.0299    0.5209   -8.3889    4.6266
+    0.0399    0.6495   -8.3796    4.5437
+    0.0498    0.7528   -8.3817    4.4288
+    0.0598    0.8820   -8.3622    4.3134
+    0.0697    0.9841   -8.4321    4.2221
+    0.0797    1.1041   -8.5237    4.1916
+    0.0897    1.1959   -8.5418    4.1310
+</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class="inlineWrapper"><div  class = 'S11'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%load('ExcelInfo.mat');</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%Participants = fields(AccData);</span></span></div></div></div><h2  class = 'S7' id = 'H_04FF0795' ><span>Settings;</span></h2><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S12'><span style="white-space: pre;"><span>LegLength = </span></span><span>98</span><span style="white-space: pre;"><span> </span><span style="color: rgb(2, 128, 9);">% LegLength info not available!</span></span></div><div  class = 'S10'><div class='variableElement' style='font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px; '>LegLength = 98</div></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%LegLengths = excel.data.GeneralInformation(:,5); % leglength info is in 5th column</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>LegLengthsM = LegLength/100;           </span><span style="color: rgb(2, 128, 9);">% convert to m</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>t1 = length(PhyphoxData(:,1));         </span><span style="color: rgb(2, 128, 9);">% Number of Samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>FS = 100;                                  </span><span style="color: rgb(2, 128, 9);">% sample frequency</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>Time_ms = PhyphoxData(:,1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>accX = PhyphoxData(:,2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>accY = PhyphoxData(:,3);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>accZ = PhyphoxData(:,4);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>AccData = (PhyphoxData(:,[1 2 3 4]));      </span><span style="color: rgb(2, 128, 9);">% matrix with accelerometer data</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>Start = 1;                  </span><span style="color: rgb(2, 128, 9);">% Start time (s) for plot</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>End = 60;                   </span><span style="color: rgb(2, 128, 9);">% End time (s) for plot</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>T1 = Start*FS;              </span><span style="color: rgb(2, 128, 9);">% Start time calculated from Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>T2 = End*FS;                </span><span style="color: rgb(2, 128, 9);">% End time calculated from Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre;"><span>c = (Start:(1/FS):End)';    </span><span style="color: rgb(2, 128, 9);">% Time STEPSIZE = 1/100</span></span></div></div></div><h2  class = 'S7' id = 'H_A9CB60AB' ><span>Plot the data;</span></h2><div  class = 'S8'><span style=' font-weight: bold;'>(1) first step in notebook</span></div><div  class = 'S8'><span>1st column is time data (ms)</span></div><div  class = 'S8'><span>2nd column is X, medio-lateral: + left, - right</span></div><div  class = 'S8'><span>3rd column is Y, vertical: + downwards, - upwards</span></div><div  class = 'S8'><span>4th column is Z, anterior- posterior : + forwards, - backwards</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span>AccX = accX(T1:T2);                         </span><span style="color: rgb(2, 128, 9);">% Signal over timeframe</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>AccY = accY(T1:T2);                         </span><span style="color: rgb(2, 128, 9);">% Signal over timeframe</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>AccZ = accZ(T1:T2);                         </span><span style="color: rgb(2, 128, 9);">% Signal over timeframe</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>figure(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>plot(c,AccX,c,AccY,c,AccZ);                </span><span style="color: rgb(2, 128, 9);">% Plot signal over timeframe</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>title(</span><span style="color: rgb(170, 4, 249);">'acc signal not filtered - First Minute'</span><span>)</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>xlabel(</span><span style="color: rgb(170, 4, 249);">'Time (s)'</span><span>);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>ylabel(</span><span style="color: rgb(170, 4, 249);">'acceleration (g)'</span><span>);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre;"><span>legend(</span><span style="color: rgb(170, 4, 249);">'X - ML'</span><span>,</span><span style="color: rgb(170, 4, 249);">'Y - Vertical'</span><span>,</span><span style="color: rgb(170, 4, 249);">'Z - AP'</span><span>)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="CBEE167A" data-testid="output_2" style="width: 889px;"><div class="figureElement" style="cursor: default;"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_164_3" widgetid="uniqName_164_3" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_164_5" widgetid="uniqName_164_5" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjAAAAGkCAYAAAAv7h+nAAAgAElEQVR4AezBDXTV9YHn/3cumCKNqYDIGNoZsZKPrQ/FQ3vUIihpxqILWYjhSSai0tSOoqVSdvrkCON2W9qGo5VqD2opqEVoIBqBAWSiYulE2yhl0eHDsiLkB9IxQQlwk0uS3/2f+9+Tc7IsoGAEgt/XK5EOgiAIgiDoYhIEQRAEQRB0MQmCIAiCIAi6mARBEARBEARdTIIgCIIgCIIuJkEQBEEQBEEXkyAIgiAIgqCLSRAEQRAEQdDFJAiCIAiCIOhiEgRBEARBEHQxCYIgCIIgCLqYBEEQBEEQBF1MgiAIgiAIgi4mQRAEH9qkSZMoLi6mubmZE2nSpEkUFxfT3NzMx62uro66ujqO5N1332XcuHGcffbZfP7zn2f9+vVMmjSJ4uJimpubyZg0aRLFxcU0NzfTrq6ujrq6Ok6GKVOmUFxczMaNGzlekydPpri4mOLiYoqLiykuLqa4uJji4mLWrl1LxqRJkyguLqa5uZmPqq6ujrq6Oo5k0qRJFBcXU1xczEsvvURHkydPpri4mOLiYl566SUyJk2aRHFxMc3NzXS2uro66urqCIITKUEQBB/ac889R2VlJa2trZxIzz33HJWVlbS2tvJxevjhhxk4cCC2OZKf/OQn/P73v+czn/kMgwcPpkePHjz33HNUVlbS2tpKxnPPPUdlZSWtra1kPPzwwwwcOBDbnAyrVq2isrKS//zP/+R4rVy5ksrKSiorK6msrKSyspLKykoqKyvZvn07Gc899xyVlZW0trbyUTz88MMMHDgQ2xzJc889R2VlJZWVlfzmN7+h3c6dO1m4cCGVlZVUVlayY8cOMp577jkqKytpbW2lMz388MMMHDgQ2wTBiZQgCIIPbfXq1axbt44ePXpwOqqsrCSVSnE077zzDhk//elPWbJkCYMHD2b16tWsW7eOHj16cDiVlZWkUilOB88++yypVIpUKkUqlSKVSnHrrbeSsXr1atatW0ePHj34KCorK0mlUnwYZ555JmvXrqXdmjVryPjUpz5FR6tXr2bdunX06NGDzlRZWUkqlSIITrQEQXAK27x5M6NHjyY3N5ecnBwGDRrE/Pnzadfa2sqsWbPIz88nNzeXQYMG8fjjj9OutbWVWbNmkZ+fT25uLoMGDeLxxx/nSNauXcs111xDbm4uubm5fO1rX+Oll16iXXl5ObNnz6a1tZWMxsZGpk6dytlnn83nP/95Hn74YX784x8zcuRINmzYQEZJSQmjR4+mtraW4cOHk5uby1VXXcX69etpt3nzZkaPHk1ubi45OTkMGjSI+fPn82GVlJQwevRoamtrGT58OLm5uVx11VWsX7+ejubNm8eXv/xlcnNzyc/PZ9asWRw8eJCMWbNmsWHDBjLuvfde5syZw6Huvfde1q1bR8bPf/5zSkpKyCgvL2f27Nm0trZyqFmzZrFhwwYy7r33XubMmUPG/v37ufvuu+nXrx9nn302EydOZPv27bQbOXIkJSUlPP744/Tu3Zvhw4eTsX//fu6++2769evH2WefzcSJE9m+fTvt3nvvPe644w7OPvtsPv/5zzN37lw6U/fu3cnOziY7O5vs7Gyys7NJJBJklJeXM3v2bFpbW8kYOXIkJSUlPP744/Tu3Zvhw4eTsXbtWq655hpyc3PJzc3la1/7Gi+99BIZs2bNYsOGDWTce++9zJkzh6O57rrr2LVrF5s3byZjzZo1ZAwePJiOysvLmT17Nq2trWSUlJQwevRoamtrGT58OLm5uVx11VWsX7+edqNHj2bkyJG0trbSbvTo0YwcOZLm5mZmzZrFhg0byLj33nuZM2cOGfv37+fuu++mX79+nH322UycOJHt27cTBJ0pQRCcolpbW/na177Gs88+S0FBATfccANvvvkmt912G7W1tWRMmTKFmTNnkkqluO6669ixYwff+MY3eOKJJ8iYMmUKM2fOJJVKcd1117Fjxw6+8Y1v8MQTT3CorVu3csMNN/DWW28xceJEbrzxRl5++WWuv/56tm/fTsaaNWtYsWIFra2tZIwbN45f/epX9OrViyuvvJJZs2bx85//nBUrVlBfX0/GmjVrWL58OSNGjKBv375ccMEF1NTUUFJSQkZraytf+9rXePbZZykoKOCGG27gzTff5LbbbqO2tpYPY82aNSxfvpwRI0bQt29fLrjgAmpqaigpKaHdfffdx+23386mTZu49tpr2bt3LzNnzqSoqIiMzZs3s2/fPjLeeOMN3nrrLQ5lm/fee4+Mt99+m02bNpGxZs0aVqxYQWtrK4favHkz+/btI+ONN97grbfeImPUqFE89NBDnH/++RQWFvL0009z5ZVX0tDQQMaKFSuoqqri9ttv5+DBg5x//vlkjBo1ioceeojzzz+fwsJCnn76aa688koaGhrIGDduHI888gi9evXiyiuv5P7772fXrl10lnXr1rF48WIWL17M4sWLefnll2m3Zs0aVqxYQWtrKxkrVqygqqqK22+/nYMHD3L++eezdetWbrjhBt566y0mTpzIjTfeyMsvv8z111/P9u3b2bx5M/v27SPjjTfe4K233uJoCgoKyHjxxRfJqK6u5pxzzuGLX/wiHa1Zs4YVK1bQ2tpKxpo1a1i+fDkjRoygb9++XHDBBdTU1FBSUkK7VatWsWLFCuI4pt2qVatYsWIFra2tbN68mX379pHxxhtv8NZbb5ExatQoHnroIc4//3wKCwt5+umnufLKK2loaCAIOkuCIDhF7du3j1/84hc88sgjPPPMMyxZsoSxY8eSsXXrVrZv387ChQs588wzee2116ioqGDhwoV8/etf569//Svbt29n4cKFnHnmmbz22mtUVFSwcOFCvv71r/PXv/6VQ23cuJGWlhYuuOACvvOd7zB//nyef/55KioqOO+88zjUpk2bWL16NWeeeSZ//vOfeeqpp1i/fj379+/nUG1tbfz0pz9lyZIl/PGPf6Rbt27s3r2b/fv3s2/fPn7xi1/wyCOP8Mwzz7BkyRLGjh1LxtatW/mw2tra+OlPf8qSJUv44x//SLdu3di9ezf79+/nnXfe4cc//jHdunXjz3/+M1VVVWzevJkLLriA1atXs3z5chYtWsTQoUPJWLZsGXPnzuVQS5Ys4b/+1/9KxiOPPMLmzZv5IIsWLWLo0KFkLFu2jLlz5/LCCy/w4osvcvnll/PKK69QUVHBzJkz2b17N/Pnz6ddS0sLc+bMYf/+/TzyyCO88MILvPjii1x++eW88sorVFRUMHPmTHbv3s38+fPZtGkTa9eu5VOf+hSvvvoqTz31FC+//DKdafbs2UyYMIEJEyYwYcIEZs+ezdG0tLQwZ84c9u/fzyOPPMLGjRtpaWnhggsu4Dvf+Q7z58/n+eefp6KigvPOO49FixYxdOhQMpYtW8bcuXM5miFDhpBRXV3Nxo0b+c///E8KCwv5MNra2vjpT3/KkiVL+OMf/0i3bt3YvXs3+/fv58NYtGgRQ4cOJWPZsmXMnTuXF154gRdffJHLL7+cV155hYqKCmbOnMnu3buZP38+QdBZEgTBKapXr17ceOON9OrViylTpnDFFVfwu9/9jna1tbVkFBYW0qdPHzJGjhzJqlWr+O53v0ttbS0ZhYWF9OnTh4yRI0eyatUqvvvd73KoIUOG0KtXL9atW8cXvvAF+vbty2OPPUavXr3Izs7mUP/xH/9BxnXXXUefPn3IuPDCC/niF7/I4QwfPpyMnj170rNnTzJSqRS9evXixhtvpFevXkyZMoUrrriC3/3udxyP4cOHk9GzZ0969uxJRiqVYt26dbS1tTF8+HAuueQSMnr16sWoUaPIePbZZzmRampqyNi/fz9lZWWUlZXxhz/8gYza2lo6Gj9+PBk9evSgpqaGjP3791NWVkZZWRl/+MMfyKitreU//uM/yBgxYgR9+/YlIz8/n169enEkDQ0N5ObmkpubS25uLgMGDOBopk+fzsKFC1m4cCELFy5k+vTpfJDx48eT0aNHD4YMGUKvXr1Yt24dX/jCF+jbty+PPfYYvXr1Ijs7m2N16aWXcs4557B8+XJefPFFMoYPH86HNXz4cDJ69uxJz549yUilUhyvmpoaMvbv309ZWRllZWX84Q9/IKO2tpYg6CwJguAU1dDQgCQmTJjAjh07KCoq4tprr6VdW1sbR9PW1sax6NevH6+++ip33nknf/u3f0t9fT1PPvkkX/3qV1m5ciVHEscxHcVxzOGce+65HE5DQwOSmDBhAjt27KCoqIhrr72W43HuuedyNJ/+9Kfp6NOf/jQZra2tnEh79+4lo6WlhYaGBhoaGjjrrLMYM2YMgwYNoqM+ffrQbu/evWS0tLTQ0NBAQ0MDZ511FmPGjGHQoEEcSbdu3Tiaffv2sW/fPvbt28f+/fs5moKCAkpLSyktLaW0tJThw4fzQfr06UO7fv368eqrr3LnnXfyt3/7t9TX1/Pkk0/y1a9+lZUrV3I8CgsLaWpqory8nIxhw4bxYZ177rl0pr1795LR0tJCQ0MDDQ0NnHXWWYwZM4ZBgwYRBJ0lQRCcolauXMnbb7/N2LFjef755/nhD39I3759aTdw4EAyqquraW5uJmPDhg187nOfo6ysjIEDB5JRXV1Nc3MzGRs2bOBzn/scZWVlHGrTpk3U1tYyfvx4tm/fzo4dO7jpppvIWLp0KYcaOHAgGWvWrOHdd98lY9OmTbzxxhsci5UrV/L2228zduxYnn/+eX74wx/St29fOtMXvvAFMtauXUtDQwPtqquryRg2bBgdxXHMxyGOYzIGDx5MxoUXXsiyZctYtmwZM2fO5Oabb+amm26io+7du9Nu8ODBZFx44YUsW7aMZcuWMXPmTG6++WZuuukmPvvZz5Kxfv16Dh48SMY777zDe++9x5H06dOHffv2sW/fPvbt28e2bdvobN27d6fdpk2bqK2tZfz48Wzfvp0dO3Zw0003kbF06VI6iuOYD2P48OFk7Nixg3PPPZeLLrqIzpBIJMjYsWMHGQ0NDaRSKQ4njmMyBg8eTMaFF17IsmXLWLZsGTNnzuTmm2/mpptuIgg6S4IgOEVlZ2eT8e///u8888wzzJ49m9///vdkHDx4kEGDBjFs2DAOHDjA1VdfzdSpU5kwYQJRFJGXl8egQYMYNmwYBw4c4Oqrr2bq1KlMmDCBKIrIy8vjUG+//TYTJkygpKSE+fPnU1NTw9atW8m4+uqrOdSgQYMoKCgglUoxaNAgxo0bx9VXX80ZZ5zBscjOzibj3//933nmmWeYPXs2v//978k4ePAgneGyyy7j+uuv58CBAwwdOpQ77riDa665hpqaGiQxadIkMs444wwy5syZw9y5c+ksZ5xxBhlz5sxh7ty5jBo1iry8PNauXcu0adNYsGAB119/PWPGjOEvf/kLRzJq1Cjy8vJYu3Yt06ZNY8GCBVx//fWMGTOGv/zlL1x11VVceuml1NfXc8011/DAAw/w93//97S1tXE0OTk55OTkkJOTQ05ODh+nt99+mwkTJlBSUsL8+fOpqalh69atZFx99dVknHHGGWTMmTOHuXPn8kGGDRtGu8LCQjrLl770JTLuvvtunnjiCb7+9a/z6U9/mo7OOOMMMubMmcPcuXMZNWoUeXl5rF27lmnTprFgwQKuv/56xowZw1/+8heCoLMkCIJT1NixYxkzZgxRFDFmzBgWLFjAjBkzyKiuriajoqKC66+/ntraWn71q1+xdetW7rzzTu677z4yKioquP7666mtreVXv/oVW7du5c477+S+++7jUCNHjuShhx4ilUpx2223MW7cOF5//XV+9KMfceutt3I4Tz/9NBMmTKChoYH169fz0EMP8ZnPfIaMbt268WGMHTuWMWPGEEURY8aMYcGCBcyYMYOM6upqOsuSJUu488472bp1K4888gjr1q3jv/yX/8ILL7xAdnY2GaNHj6Zbt26sXr2aVatW0VlGjx5Nt27dWL16NatWraJHjx6sXr2aSy+9lAcffJBbbrmFvXv3Ul5ezsiRIzmSHj16sHr1ai699FIefPBBbrnlFvbu3Ut5eTkjR44kY8WKFVx++eXU1NTwne98h8GDBzNmzBhOFSNHjuShhx4ilUpx2223MW7cOF5//XV+9KMfceutt5IxevRounXrxurVq1m1ahUf5KKLLuKcc84ho7CwkM4yZ84c/uZv/oZ//dd/ZcqUKYwZM4Zrr72WjkaPHk23bt1YvXo1q1atokePHqxevZpLL72UBx98kFtuuYW9e/dSXl7OyJEjCYLOkiAITlGJRIJly5bR1NREfX09b775Jj/72c9Ip9PMnz+fjL59+7Jy5UpSqRRRFHHw4EHmzp1LIpEgo2/fvqxcuZJUKkUURRw8eJC5c+eSSCQ4nKlTp/L++++zZ88eoiiiubmZ+++/n3aNjY2k02lycnJobW1l1apVTJo0iXfeeYedO3dSWlpKS0sLGZdddhkZjY2NpNNpcnJyaNfY2Eg6naZPnz4kEgmWLVtGU1MT9fX1vPnmm/zsZz8jnU4zf/58MhobG0mn0+Tk5HA4jY2NpNNpcnJyaNfY2Eg6naZPnz5k5OTkMHfuXJqbm9m1axepVIrly5dz3nnn0e6b3/wmyWSSKIqoqqricBYtWkQ6nWb8+PG0a2xsJJ1Ok5OTQ0ZjYyPpdJqcnBwyvvnNb5JMJomiiKqqKjIuueQSNm7cSFNTE7t376axsZF77rmHdul0mnQ6zaEuueQSNm7cSFNTE7t376axsZF77rmHdp/73Od47bXXqK+vJ5VKsWDBApYtW0Y6naawsJDj9e6775JOp7nhhhs4ksbGRtLpNDk5OWSk02nS6TSHmjp1Ku+//z579uwhiiKam5u5//77affNb36TZDJJFEVUVVVxOI2NjaTTabKzs8l49913SafT3HrrrWQ8+uijpNNpSktLyWhsbCSdTpOTk0NGY2Mj6XSanJwc2jU2NpJOp+nTpw8ZV111FTt37mT37t0kk0l++MMfsnz5ctLpNDk5OWR885vfJJlMEkURVVVVZFxyySVs3LiRpqYmdu/eTWNjI/fccw9B0JkSBMEprkePHvTp04ejyc7Opn///iQSCQ4nOzub/v37k0gk+DB69epF//79SSQSHEkikeDb3/42o0aNorS0lPnz51NaWsrevXsZOHAgffr04Vj06NGDPn368HFLJBKcd955ZGdnczjZ2dn079+fRCJBZ8rOzqZ///4kEgk66tGjB/369SORSHAsevToQb9+/UgkEhxOnz59yM7O5lTWq1cv+vfvTyKR4FDZ2dn079+fRCLByZRIJOjXrx/du3fnSLKzs+nfvz+JRIKOevToQb9+/UgkEgRBZ0sQBMFxSSQSPP300wwcOJAVK1Zw22238eSTT3L55ZdTUVFBEARB8PFJ8Anw8ssvc6i6ujrWrl2LbYLgeF133XVs2bKFVCrFvn37SKVSvPbaa1x22WUEQRAEH58Ep7mHH36YH/zgB3T03HPPMWHCBFavXs0//uM/8uCDDxIEH0V2djY5OTlkZ2cTBEEQfPwSnKbef/99vv/97/PYY4/RUVtbG/fddx8LFizg5z//ORUVFcyfP5+3336bIAiCIAi6hgSnqQceeIDevXvzP/7H/6CjdevWcfbZZ3PhhReS0bt3b4YNG8Yf/vAHgiAIgiDoGhKcpv75n/+ZGTNmcOaZZ9LR+++/z0UXXURHOTk5bNmyhSMpLS1FEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUiitLSU01GC01QikeBw2traSCQSdJRIJIjjmCN59dVXsY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxjYZtrGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2CbDNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29gmwza2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9tk2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sY5sM29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbPPqq69yOkrwCfOpT32KtrY2OorjmO7duxMEQRAEQdeQ4BPm3HPPZdOmTXT03nvvMXjwYIIgCIIg6BoSfMJ85StfIeOll14i43/9r//FH//4R6666iqC4zN16lSCI5s6dSrB0U2dOpXgyKZOnUpwZFOnTiX45EnwCZNIJPjFL37BD37wAyZPnszEiROZPXs255xzDsHxueuuuwiO7K677iI4urvuuovgyO666y6CI7vrrrsIPnkSnOauueYaXn75ZTq64oorWL9+PQsWLODPf/4zI0aMIAiCIAiCriNBEARBEARBF5MgCIIgCIKgi0kQBEEQBEHQxSQIgiAIgiDoYhIEQRAEn2ilpaVIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQRGlpKZ90CYIgCIJPtFdffRXb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2sY1tbGMb29jGNraxjW1sYxvb2MY2trGNbWxjG9vYxja2efXVV/mkSxAEQRAEQdDFJAiCIAiCIOhiEgRBEARBEHQxCYIgCIIgCLqYBEEQBEHQRf3pT3/i7rvvprW1lY5+8YtfsHLlSo5Fc3MzZWVl/PrXv+ZQzzzzDGVlZbS2ttLc3ExZWRnByZUgCIIgCLqor3zlK/zP//k/+cEPfkC7RYsW8dvf/paCggKORWtrK/Pnz+d73/secRzT0X//7/+dxx57jDiOaW1t5bHHHiM4uRIEQRAEQRf2u9/9jscee4wXXniBrVu3cs8997Bs2TJ69OjBserevTtf+cpXWLNmDe22bt3KWWedRXBqSRAEQRAEHcxavY2s6dVkTa8ma3o1WdOryZpeTdb0arKmV5M1vZqs6dVkTa8ma3o1WdOryZpeTdb0arKmV5M1vZqs6dVkTa8ma3o1WdOryZpeTdb0arKmV5M1vZqs6dVkTa8ma3o1WdOryZpeTdb0arKmV5M1vZqs6dVkTa8ma3o1WdOryZpeTdb0arKmVzNr9TY6Ou+883jssce47bbbGDduHHPmzCE/P5/jNWHCBBYvXky7xYsXM3HiRIJTS4IgCIIg6OC+rw8gXV5AuryAdHkB6fIC0uUFpMsLSJcXkC4vIF1eQLq8gHR5AenyAtLlBaTLC0iXF5AuLyBdXkC6vIB0eQHp8gLS5QWkywtIlxeQLi8gXV5AuryAdHkB6fIC0uUFpMsLSJcXkC4vIF1eQLq8gHR5AenyAtLlBaTLC0iXF3Df1wdwqOLiYvLy8jh48CATJ07koxg7diyVlZXEcUzG4sWLGTt2LMGpJUEQBEEQdHG///3vaXJjffgAACAASURBVGho4MCBAzz++OMczrx585g8eTKTJ09m3rx5HElubi5XX301a9asYdOmTZx//vn06tWL4NSSIAhOgpb6Gpr8AEEQBB/VW2+9xe23386CBQt48skn+fa3v82WLVs41MUXX0xhYSGFhYVcfPHFHM348eNZvHgxixcvZvz48QSnngRBcBLETRFxcidBEAQfRRzHjBs3jhkzZnDFFVcwZMgQvv3tbzNhwgTiOKajIUOGUFpaSmlpKUOGDOFobrzxRp599lmWLl3K2LFjCU49CYIgCIKgi7rnnns466yz+P73v0+7+++/n4zvfe97HK+ePXty7bXX8qUvfYns7GwOJysri6ysLLKyssjKymLlypUEJ06CIAiCIOiiHnjgAV544QU6SiQSvPbaa/zsZz/jWOTk5NDc3Ey7ZcuWsWjRItql02mys7PJyckhnU6TTqdJp9Ok02nS6TQ33HADwYmTIAhOgjgZEQRBEATHK0EQBEEQBEEXkyAIgiAIgqCLSRAEQRAEQdDFJAiCIAiCIOhiEgRBEARBEHQxCYIgCIKgi/rJT37CU089xaEWL17ML3/5S47Ffffdx5/+9CcOVVtby09+8hOOxTvvvEO7zZs3c88993A8Nm/ezJw5cwj+XwmCIAiCoIu69tprmTJlClu3bqXd1q1buf322xkxYgTHIo5j5syZw6F++ctfkkgkOBYDBw6kXRzHHDx4kOMRRRH/+q//SvD/ShAEQRAEXdRVV13FtGnTuPXWW2l36623MnPmTPLz8zkW3/jGN1i6dCnNzc20a25uZvHixdxyyy20W79+Pc888wzbt28n47333mPjxo1s2rSJtWvXsmHDBg4cOMDatWuJ45jzzjuP8ePH0279+vU888wz7Ny5k3ZxHLN27VqeeeYZXnnlFYIPliAIgo+syQ8QBMHJ8S//8i+8++67/PrXv+bXv/412dnZTJs2jWP1d3/3dwwdOpTFixfT7qmnnmLEiBH069ePjOLiYn70ox+xbt06/v7v/57FixdTW1vLP/zDP3DzzTdz//3389JLL5GxePFi4jimtraW4uJiMkaOHMk///M/s27dOr761a+yZs0ampub+fKXv8z8+fNZs2YNkydP5r777iM4ugRBEHxkTX6QIDhZWuprSNVV0FkalpSzpSSPLSV5bCnJY0tJHltK8thSkseWkjy2lOSxpSSPLSV5bCnJY0tJHltK8thSkseWkjy2lOSxpSSPLSV5bCnJY0tJHltK8thSkseWkjy2lOSxpSSPLSV5bCnJY0tJHltK8thSkseWkjy2lOSxpSSPLSV5bCnJY0tJHltK8thSkseWkjwalpTTUXZ2Nk888QT33nsvP/nJT1i4cCHH65ZbbuHJJ5+k3e9+9zvuuOMOMlauXMnOnTt54YUXmDNnDi+88AL/+I//SDqdZvPmzbz44ou89NJLfPvb3ybj0UcfpXv37rRbvnw5DQ0N/Nu//Rtz5szh8ccfZ+PGjWzevJkbb7yRp556iocffpg5c+bwyiuvEBxdgiAI/n9xMiJVV8HxipMRQXCyNPlBOkufcdPJr9hFfsUu8it2kV+xi/yKXeRX7CK/Yhf5FbvIr9hFfsUu8it2kV+xi/yKXeRX7CK/Yhf5FbvIr9hFfsUu8it2kV+xi/yKXeRX7CK/Yhf5FbvIr9hFfsUu8it2kV+xi/yKXeRX7CK/Yhf5FbvIr9hFfsUu8it2kV+xi/yKXeRX7CK/Yhd9xk3nUF/5ylcYOnQod9xxB/379+dw5s2bx+TJk5k8eTLz5s3jcCZOnEhtbS1//etfqaurY8uWLVx33XVkrF27lt27d1NcXExxcTF33XUXjY2N1NfXk5+fT25uLkfzpz/9icsuu4x2hYWFfPe732XQoEEUFhby3/7bf2PixIncddddtLW1ERxdgiA4RcTJiDgZcbK0JSNSO5YSBKer99cOJU5GnK4+9alPcc4553AkF198MYWFhRQWFnLxxRdzON27d+fmm29m0aJF/Pa3v+Ub3/gG7c4880yuvfZaHn30UR599FEeffRR/vrXv9KrVy/OO+88Pkh2djZxHNMumUyyYcMGVq1aRXFxMV/60pf47ne/y0MPPUQcxwRHlyAIThGpugpSdRWc6g68PoM4GRGcGlJ1FbTU13A0LfU1HHh9Bp90cTLik2zIkCGUlpZSWlrKkCFDOJLbbruNpUuXsmjRIm655RbajRgxghdffJFEIkGfPn14++23ueSSS8jKyuJwWltb6aiwsJCXXnqJ/fv3k7Fw4UL+5V/+hTVr1lBYWMikSZMYPHgwL774Im+++SbB0SUIgpOkLRlxqombIj5IWzKipaGGT5omP0CcjDjVtNa/QmtDDUcTN0UEwYd12WWXceDAAfr378/f/d3f0W7o0KHccccdfPGLX6SoqIiioiIeffRRunXrxqGuvvpqcnNz2bx5M+2uuOIKpkyZwqWXXsqIESOYO3cuDz30EN/61rdYs2YNo0ePZvjw4Xz6059m7969xHFMcGQJguAESNVVEHRtqbqldGVtyYiTKVVXQUt9DUfTUl9D4/qJBMdn0aJFTJkyhc7w2muv8fzzz3Oof/qnf2Lnzp08/fTT7Ny5k5EjR1JYWMjzzz9PRy+//DKNjY1cdNFFFBYW8u6775LxT//0T/zv//2/WbZsGZs2baJ///7k5+fzzjvv8OSTT/Jv//Zv3HfffSSTSRKJBIWFhTz//PME/68EQXACNPlB4mRE0LW1JSOOVaquggOvz+DjlOj5WU51cTKitaGGY9VSX0NLfQ3BqSORSNCzZ08+SPfu3TmcRCJBz549OVROTg6JRILgw0kQdFlxMuJ0Eid3ciI1+QFa6msIjt/7a4dyKoiTEaerg3VLiZsigiD4vyUIuqz9r88gVVdBVxAnIz6MOLmTEyVO7iRuigiOX5yM+KRJ1VXQUl/DxyVuijhUnIwIguD/liAIgo8kTkYEp5YDr88gTkYcq8b1E4mTEUcTJyMO1i0lCE4ljesnEicjPkkSBB9ZnIw48PoMTnct9TUceH0GJ1OqroIDr8+gM7QlIz5Iqq6ClvoaTqSW+ho+SEt9DU1+gK4gTka0JSOOVUt9De+vHcrxaEtGtCUjjlVrQw1B0BW1NtTwSZMg6BQtDTWcbg68PoM4GdFRWzLiZGtLRpwocTLiYN1STqR9f5xInIw4mrgpIk7u5FQQJyM+SeLkToIgOPkSBMERtDTU0FHcFHE6i5siPqw4GXE0LfU1pOoq6AxxMqKlvoZDtSUjTqY4GXEqi5siTiVxcieHavIDxMmIIOgMbcmIT5IEwUfWlozoTHEy4lQQJyOC45fasZQPEicjPkhLQw1NfpBPojgZcTqJkzvpKFW3lOCj2bRpE2VlZZSVlVFWVkZZWRllZWXcc889HK/777+fBQsW0FFraytlZWWUlZVRVlZGWVkZP/7xj9m6dSvByZEgOOWk6io48PoMTgVtyYiO4qaI49WWjAj+b/tfn0FLfQ3BxytORnzSxcmIQ8XJiFRdBV1Zr169GDZsGMOGDWPYsGEMGzaM119/ndraWo7H9u3bWbRoEd/73veI45h2cRzz2GOP8dWvfpVhw4YxbNgw3nnnHb785S9TV1dHcOIlCE45cXInJ1qqroKW+hpOVy31NbTU19AZ4qaIT6I4GXGqipM76ShuiugK4mRER3FTxInU0lBDa/0rdGX9+/entLSU0tJSSktLieOYd955hyVLlnA8fvOb31BUVMSXvvQlFi9ezKEmTZpEaWkppaWlzJ07l8svv5zq6mqCEy9BcMLEyYhUXQWnotb6V2htqOFEiZM7OZFaG2o4WLeUQ7XWv8LHIW6KiJsiPoy4KeJ4xE0RHcXJiODkaKmvoaOW+hqa/ACdoS0ZcaI1+QH2VA1gT9UA9lQNYE/VAPZUDWBP1QD2VA1gT9UA9lQNYE/VAPZUDWBP1QD2VA1gT9UA9lQNYE/VAPZUDWBP1QD2VA1gT9UA9lQNYE/VAPZUDWBP1QD2VA1gT9UA9lQNYE/VAPZUDWBP1QD2VA1gT9UA9lQNYE/VAPZUDWBP1QD2VA1gT9UA9lQNoMkPcDh/+tOfuPPOO1m2bBn9+vXjePz2t79lwoQJTJo0iV//+td8kIMHD9K9e3eCEy9BcMK0NNSQ2rGU4xEnI1rqa+gMcTKipb6GT5I4uZPTXZMfpMkPcLqImyI6U9wUcTzeXzuUOBlxNE1+kJb6GjpqqX+Fj0vj+onEyYjO0JaMONSZmkbvom30LtpG76Jt9C7aRu+ibfQu2kbvom30LtpG76Jt9C7aRu+ibfQu2kbvom30LtpG76Jt9C7aRu+ibfQu2kbvom30LtpG76Jt9C7aRu+ibfQu2kbvom30LtpG76Jt9C7aRu+ibfQu2kbvom30LtpG76Jt9C7aRu+ibfQu2kbvom30LtpG76JtnKlpHOrdd9+lqKiIRx55hCuuuILjsXbtWj7zmc8waNAgJk6cyOuvv87mzZvpaNq0aUydOpWpU6cyYsQIoihi1KhRBCdeguCEipsijkeqroIDG2aQqqvgo2pLRjT5QT4uqboKWupr+LjETRHHoy0Zcarp1vOzHK84GREcXpyM6CxxMuJUEzdFHCpORhyrOBlxOmhtbWX06NGMHTuW0tJSjmTevHlMnjyZyZMnM2/ePA41f/58MqZOncq0adPo27cv8+bNo6OLLrqIL37xi1xyySVMnjyZN954g9zcXIITL0HwsWnyA8TJiHZxMuKjiJMRXUGcjDhYt5RTTdwUcappaaihs7QlIw61p2oAcTLiaOJkRFcRJyNSdRV01JaMOBXETRHHoy0Z8UHi5E4O1ZaMCP6PO++8kx49evDLX/6So7n44ospLCyksLCQiy++mI7ee+89li5dyve//32GDh3K0KFDueuuu/jNb37DwYMHafetb32LO+64g29961tMnDiRnJwcTrY4GfFJlCD42KTqltKWjOhMcTKiXdwUEScjjlXcFBE3RRyPOBlxMsXJiM7UlozoLG3JiKNJ1VUQJyOOR9wU8WHEyZ0cTZyMSNVV0FFLQw0HXp9BR3Ey4nBaG2roDKm6Cpr8AB3FyYg4GXE0bcmI1I6lfJzakhFH05aM+CBxU0TcFPFhHKxbyonUlow4nTz++OOsWbOGJUuW8EGGDBlCaWkppaWlDBkyhI6eeOIJrr32WiZOnMj48eMZP34806ZN47Of/SxPPPEEwaknQXDKaUtGdJY4GdFSX0NniJMRnSluivg4xMmIVF0FH0bcFHE0cXInnSW1YyltyYiTrckPcqi2ZERHTX6QJj/AoZr8IJ0hTkbEyZ10lKqroMkPcqhUXQXt4qaI002qroLg+FVWVvL2229zzjnnkJWVRVZWFllZWWRlZXEsfvOb3zBp0iQOddtttzFv3jy6mpb6Gpr8AKezBEGniJMRxyNORrTU1/BxaUtGHNgwg1NRnIyIkxGdrS0Z0eQH+TDiZERLfQ3t4mTEiRYnI9rFyYiMtmTEx6EtGdHVtNa/wokQJyM6U5yM+LjEyYiO4qaI4xU3RXRly5cvJ51Ok06nSafTpNNp0uk06XSaY7FhwwZKS0s51D333MMrr7xCdnY26XSa7OxsuoK4KaKl/hVOZwmCk6otGdHkB+kobopoS0Z0ljgZ0VW0JSPaxcmIA6/PoDO0JSOOV0tDDXEy4ljFyYiO4qaIuCmiM8TJiI9TWzKis8TJnRwqTu7kUHFyJ6eaOBlxosXJiEO1NNTQUl9DEBxNa0MNnyQJgo9V3BTxQeKmiM6yp2oAcTLiRImTER8kTkYcSVsy4mhSdRXEyYiTKU5GtDTUcLLEyYjjEScjDidORpxocVPEoVoaakjVVXCs4qaIQ7UlIz5InIyIkxEfh7gpojPEyYjDiZMRH0bT/8ceHIVKlt+Hnf/md6pcnF9Lw8RVOMT1K0Hww3mMWg++B8l+WJgsBBItm74ElBdjNggMEd0v/eRANni1L/dFHa1ZCItYkYfRwu0Y/JZlRBYGeaqRMj0IJ6Ygi+ypX2EMt2Qjq39H1VXn7+U8HHI4e29V3du3e6Se/nwW3+CQ3XrOG6+n7cUTPk2EN34hpFjxIlI4h6RYcV3b9ZyffO8rdKVYUYfT93z5mNuQwvmkpVjRVy0eccjm48ekcH7epMp5mVI4fSmcY9Th3Jbtes7rLMWKVrX4BtuLOX3biye0Uqx4441fVMIbL9Wzpw9J4bRSOF2pcl6F7XrOPnU4l9mt59yW7XrOIbv1nBROow7nKpvlObclVc5tSJVzyG4951XbruccksJ59vQh11GH06jDuQ11OC9bCuc21OFcVx3Oq5RixW495xi//uu/TlEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFQVEUFEVBURQURUFRFBRFwa//+q9zSKqcFM7rSnjjpUnh3FSqnGOkWLFPqpwXUS0ecVtSOPvU4TTqcA5J4eyTwqkW3+BlSOG8iDqcrhQr+qrFI7pSODe1+fgxb/z/bZbnbC/mHKMOp2u3npPCOaRafIOfN6ly+v7dv/t3LBYLFosFH5w954Oz5ywWCxaLBX/8vW/z5Pd/hcViwWKxYLFY8OF3vswHZ8/54XtfZ7FY8CdPv8sHZ8958vu/wmKxYLFY8Mff+zYfnD3nh+99ncViwWKx4KM//B0+OHvOYrFgsViwWCz48Dtf5oOz5/zwva+zWCz4k6ff5YOz53z4nS+zWCxYLBb88L2v88HZcz76w99hsViwWCz4/re+wPe/9QUWiwWLxYLFYsEHZ8/54Ow5i8WCxWLBh9/5Mh+cPWexWLBYLFgsFnz/W1/gg7Pn/MnT77JYLFgsFjz5/V/hw+98mcViwWKx4E+efpcPzp7z5Pd/hcViwWKx4KM//B0+OHvOYrFgsViwWCz4/re+wAdnz/nhe19nsViwWCz46A9/hw/OnrNYLFgsFiwWC/74e9/m+9/6Ah+cPWexWLBYLPjhe1/ng7PnfPidL7NYLFgsFvzwva/zwdlzvv+tL7BYLFgsFjz5/V/hg7Pn/PH3vs1isWCxWPD9b32B/+N//R/YJ4WTwtksz3ldCZ9CP/7xj/nBD37AD37wA37wgx/wgx/8gJ/85Cd8UlI4z54+pCuFc5kUKw6pw+lL4fyiSJVzHdXiG2yW5/RtL57QlcLZJ4XTSOH0pXC6tus5L1OqnL7txZybSJVzm1I4Xc8+ekjXZnnOZnnOy5DCuS2bjx+TKudV2q7nHGN7Med1sfn4Ma0UKxopnEPqcPpSrLhKCue21OHctt16TlcK51UQNV5XwqfQH/zBH/Bbv/VbfPWrX+WrX/0qX/3qV/nhD3/IJ6kO51VK4Tx7+pCb2F7MeZXqcPbZXjwhhfNJS+G8TKly+lI4N5HCaaVwXkQKpyuFs7t4wotK4Rxrt56TwmmkcI61u3hC37OPHvKypHBSOH0pnK5UOX2pcj4JqXJSOCmcQ1I4x6jD+aTV4dyWFM6rtvn4McfYXTzhdSV8Cv3n//yf+d3f/V0+/PBDPvzwQz788EN+4zd+gzf+mxROK4XTlSqnlcJJlZPCadXhHFKH87KkyvlFl8LpS+H0VYtHtFLlHJIq51ib5WNuqg7nGCmc60rhbC/m9NXhvEp1OIekWHHbUjjHqMP5RZYq5+dJihVXSbGib3sx52XareekcD7NhE+h//Jf/gu/9mu/xo9//GO22y3HKIqCoigoioJvfvObXKZafIPL1OF8UlI4L5OokcKpFo/4pOzWc46VwmmlyrmpFM7Pozqc25LCOVYKp7W9eMJlNstzDqnDOUaqnL5UOdeRKudlSeG8bKlyblsKp5XCeVlS5fSlcG5LCueT9nz5mMukcA5JlXOMOpxGHU4jVU7jm9/8Jt/85v9G49//wb/ndSV8ytR1zccff8zv/d7v8Y/+0T/i7//9v8+//Jf/kkMWiwWLxYLFYsHXvvY1LpNixS+SOpyuOpzrSuG8iFQ5r1Idzk2kWHGVOpy+FM4xUji3abee05fC6avD6avDeVEpnNuWKueNm9ksH/MypXAOSZXz8y6F8yLqcPrqcD4pX/va1/ja1/4FjX/yP/4TXlfCp8xf/MVf8M477/Bv/+2/5Y/+6I/4j//xP/L+++/z7rvv8qLqcG4qVU5fCudVSZXzKqRwWqLGy5Qq55DBuOQ2pXAOSZXzSdut5xwjhdNI4Rxju56zW8+5TArntqRwbiLFiq46nL4UTl+qnNtSh3NICuc2pHBuSx3Oz5vdek4K55OWKieF05Uqp5Uq543bJ3zK/Oqv/ir/5t/8G371V3+Vxt/5O3+Hf/AP/gH/6T/9J15UpsYxUjifpBTObavDeVl26zl9KVb8Itgsz+kSNfo2y3NuUwrnRaXKuUodTl8dzj6DcUmKFZ+UFCsus1vPualUOV27iyf01eG8CimcRh3OVepwGimcVh3OTdTh/CLZLM85pA6nL4XTl8JppcpppHA2y3O6UjjH2K7nVItv0JXC+cn3vkLXs48eksJppMo5Vh3O60r4lPmzP/szzs/P6Xr+/DlZlvGitus5+6RY0diu57RSONdRh9NK4XSlcBrPPnpIl6iRYsVN1eF0pXBephRO12b5mJclUyOF07Vdz7kpUWMfyY1WCuc6Uqzo267nbC/mXFeKFS8qVU4rVU5Xqpyu4eSEzfKcVh2OqHGMFM5PvvcVrpIqp5HCuYkUTkvUuMzu4gmtVDmX2SzPeZU2Hz/mVUjhvEx1OJdJ4WyW53TV4dyWVDm3QdRopFjRl8Lp267ndKVwthdPOCSFs13P6UqV82kmfMr87Gc/41/9q3/Ff/2v/5XGX/zFX/Dd736Xf/yP/zG3IYXTt1vP6dp8/JjN8pyXJYWTwjkkVc51pFhxlVQ5XalyXqUUK16GOpxGHc4xqsU3eJVSOLv1nNuQKmd7MecmUjj7iBqixmZ5Tms4Lkmx4mVJ4dyWOpyXqQ7nkBROV6qcY6Rw9knhpHBuYrN8zGZ5TlcK5ya2F09I4XQ9e/qQm/jp04dcV6qc2yJqXGV38YS+VDl9qXLe2E/4lCmKgt/93d/ln/7Tf8pv/dZv8Q//4T/kn//zf85v/MZvcFOpchopnL967zdJ4XRVi0ekcFq79ZzdxRO6Ujj71OFcpg7nGHU4m+U5hzx7+pAUTleqnK7N8jHbizldKZzN8pxWCmd7MaevDmcfUeMm6nD6UjhdosZuPedl2iwfcxMpnL4UzlVSrBA1GilWNFI4XXU4rZ987yt0bZbnbC/mdKVwni8f0xI16nD6ni8fk8K5rtHsHimcru16zmZ5ziGpchopVlxls3zMZnlOa3vxhJsQNVI4L1sK50WkcI5Rh3PIX733m/Rt13O6UuX0pXBSOPukcP7qvd+kUYfT2F08IYXTGoxLdus52/WcOhxRo5XCuUyqnKvs1nOukirnRfz06UP68uI+XaPZPUSNVgrnGCmcF5HCaaVY8boTPoX+2T/7Z3z44Yd8+9vf5sMPP+S3f/u3eVGDcUlXCqerDudYKRxRY7M8J4XTGoxLdus51eIbtHbrOZvlOYfs1nN2F0/oSuF07dZzNstztus5XdXiEV0pnOfLx/TtLp7QSJVzU5IbfSmczfKcfXbrOceoFo9I4eyTwulLlbO9mNMSNVI4h+wuntBK4WRqbD5+zPZiTtdPnz4khdO1Xc/ZZzguuUwdTt9uPSeF0xiMSxrPl4/pq8PpevbRQ7YXc7o2y3PqcG4ixYquFE4Kp7Vbz9ksz9lnMC7ZXjwhhdOVwmnU4VxHtXhECqcOR3LjNu3Wc1I4jVQ5g3FJqpzrSJVzlR//4d/jtmwv5nQ9e/qQrhTOMX769CGNVDmiRiOF0xqMSzbLczbLc/pSOA3Jjeuqw7mOavGIzfKcVgqnlSrnKrv1nL7BuKQOp68Op0vUqMO5qRTOG/+N8MatyNQQNY5Vh7OP5Ea1eMR2Pac1nJzQSLHikDqcvjqcm0jhtAbjkkYdziGpcl5EqpxWtXhEI8WKF7Vdz7muFM5uPadru57TEjUaKZyuzfKczfKcrt16Tqqchqgxmp2yW8/pe/b0ISmcq4hOuUyqnKukcK4jhXOZTI2uFCsOETVaqXKu8uzpQ1I4XSmcrt16znY9Z59UOX2b5TnV4ht0bZbn3EQK5xg/ffqQ69pePCGF0xrNTmnV4bwMqXL6UjitFCuOsVvPaQ3HJaLGPqlyhpMTbuqnTx9ymRROQ9SoFo/YXsxpiRopnEYKZ5+ffO8r9KVwDpHc6BuOS46Vwjlk8/FjUjgNUaOxvZjTtVvP+bt/+294HQlv3Lo6nIaoMZqdcqyffO8r7CNqXGZ38YS+v/6jr5DCOeTZ04ccI4XTGE5OuEodzmXqcFp//Udf4SqpcoaTEzbLx2wv5nSlcA5J4fSlcA7ZLB9zrBSOqNF49vQhKZyG5MZgXNJKsULUaKRwWqJTGimcvjqcOpyrbNdznj19SF8dTms0O+XZ04ekcBqDcYmo0ZWpcZVUOS3JjUaqnL46nIaocZkUTqsO5zKDyQmNFCv66nCukqnRSOG0BuOSY6VY0UjhDMYljTqcluRGHU6jDucyosZlNstz+lLlHJIqpyVq7NZzqsUjujbLx2yW51zXbj3nWHU4g3GJqHGIqNFIseIypuem+gAAIABJREFUKRzRKVdJsaIlajRSrLhMCucqu/Wc7cWcfVI4z5ePaQ3HJV2ixlV26zmNOpxWHU5XpkaqnEaKFaLGMUSNRgrnJnbrOdv1nIbkRuOv/+gr9H3h1/6G15Hwxo1tL+b85HtfYZ/B5IRGqpxDUuW0MjUaKZxDNstzUjgNUWM0O6VRh3OZFCtadTgtUeMQUaORKqeRYoWocR0pnIbkRgqnL4WzW8+5rr967zdJ4aRwWj99+pBGqpzhuKSRwmmkcESnpHBSOClWiBpXSbGirw7nKsNxSSPFin1EpxwjhVOH0xCdsk8dTktyo1GH0xCd0qjDualUOY3huOTO3TNaKZxWCqcluXFIqhxR45DB5IRGihWt4eSERgrnNjz76CFdqXK6huOSyzx7+pDrEDUaz54+pJFiheRGn+iUFE4Kpy+F00qV0yVqVItHpHC6/uq936QvhdMajkv6tus5m+U5reG45CopnNZwXNKVqdEnuXGM3XrOs6cPaYkajVQ5l6nDkdzoE51ymRTOMVLlHJKpkSqnb7ee05LcuIyo0UjhdKVY8cZ/I7zxQlLl9KXKuYkUzj6SG406nH0GkxNu4s7nz8iL+6RwriMv7iNqpMrpevb0IZvlOfukWJHCua7N8pxnTx+SwtknVU5LdEojxYqWqNG3vXhCCue2iE5piRp9okYjVU5fHc5lUqwQNa6SKudYKVa8DD99+pAuyY06nH0kNxqpclqDcckhokaXqJHC+fEf/j2OlSonUyNTI4WTwmmlcH7yva9wmRROCqcrxYpjjWanNFI4jeHkhEYdznWlcJ49fUgjVY7kRqMOJ1VOK4XTJWrsI2qkcHYXT2iJTmnV4byITI1j1eG0RrN7vIgUTtdfvfebXCaFcxXJjUYK5xDRKV2ZGpe58/kzRI3WYFxy5+4Zm+U5+4gafaLG60p448aGk5IUTktyo284LhE1DhmMS7pEpzRSrDhGHc5VJDcaqXJad+6eIWqkymmkcIaTkmO8/c77pHCOkcLZZ7M8p1o8oi/FikPqcPrqcBqj2SmDcUmXqHGVOpyG5MZuPWezPKeVF/fpG45LGqly+lI4DdEpjRQrXtRgXNJIlbPPYHJCX6bGTYw+d4++wbhkMC45RqqcfSQ3jpEX9+kbjEv2kdw4RqZGI1XOVUSNxm49pyU6ZXvxhBROXwqnK4XTytRI4bwoUWMwLunKi/s0tus5rUyNRqqcffLiPilW9NXhHGswLsmL+/SJTmmkcG6iDqcrVU5fCudYKRxRo28wLmmkcK5L1DiGqDEYl2wv5jREp7RSOClWXGU4LjlWCqfrf/rva15HwhsvRNRoZWr0iRrDcUnrzt0zGqlyuvLiPl2ixotKlXNb6nBaokbf2++8T9dodsqx6nBaosZVUjjHEp2SqZHCaUluDMYll0mV0xhOTmikWNESNfpEp7RS5WRqZGpcpg6nL8WK1mh2ymh2SmswLhnNTmmkyjkkVc4xUuW0RI1UOa07d88YzU5J4bSG45LR7JQUTms4OSFTo68O55BMjVQ5NyFqpHBao8/do5XCkdxopFjREjUaKZyuOpzWYHLCIZIbl9mt52yW57REja7BuOQyKVY06nAkN0SndIkah0huvPWldxE1ukSNrjqcrtHsFFHjpupw9hnNThE16nBag3GJqNESnXKZz37xXepwWqJG12h2St9odspodspl6nC66nD2GU5OOGQwLhE1bpuosV3P2UfUEDVSOI3BuKSRwmlJbvRJbryuhDdeiOTG5uPHNO7cPWM0O+UyKZzWnbtntFKsOOTZ04ekymm8/c77pMrpS5XTktzoEzVSOH0pnC5RI8WKOpx9RI3N8pztek4rhbOPqNGow+naree0JDf6RI2uvLjPMUSndGVq5MV96nBaw3HJaHZKXx1OS3KjUYfTGs1OGc1O2UfUGM1OaaRwRA3JjUYdzj6DyQk3IbnRSOGkysnUONadu2f0iU65TAqnJblxmRROl6ghudHK1GjU4RyrDkdyQ3KjDqc1nJSIGl3Dcckhw3HJMQbjkn1Ejby4T9foc/cYzU7p2yzP2V7MaYkajTqcQ1KsuEyKFZf5zN0zRrNTuiQ3GimcRl7c5yZEjUYdTkvUkNzoGk5KugbjElGjDuc6RKdcRnRKazAuyYv7dGVq9EluNFKs2EfU6MqL+4xm90jh9IkaKZyW6JQUTiPFilamRqqc1i9/+UeMZvdI4XRJbtThXCaFkxf36cvU6BtOTvi7f/tveB0Jb7yQTI0u0SkpnO16Tkt0StdwXNI1mp0ynJSIGimcrjqc7XpOCqchaqRwthdzGoNxyWh2SlemhqjRGk5OuPP5M0SNzfKc6xA1rpLCSeFIblwlxYrWcFwyGJe0MjX6MjXu3D2jNRiXvP3O+4gaLVGjkSqnJWo0UuW08uIBfZkaqXJaokaXqNFIldM1mp3SJWoMJifsLp6wzy/N7tE1nJSIGrv1nDqcluiUFE5LcmM0O2V38YTGcHJCI4XTJWqkcFrDSUle3KdLdEojhdMaze6RwrlKCkfUuIzolH1Gs1NEjUYKpytVzvZiTgpH1BA1UuW0PnP3jLy4TwonxYrryIv7XKYO5yqixmh2SpfolEYdTqqcTI1MjcukWNEajktEja68uE+X6JRGqpy+VDmtO3fPSJXTEjUuI7lRh9MQNRopnOsYjEv6RKc0UuU0huMSUaNLcmMwLrmJO58/4xiiRqqcFCtEDVGjT9RIsaKVFw8YjEtuqg4nhdMYjktEjTqc6xI1Uqzoktw4VqbGbj3nKsNJyWh2SooVLdEpjTqc1mBc8roS3nhhqXK6UqyoFo+Q3LiMqJHCaQ0mJ/RJbogaqXJamRoNUSNVjqjx1pfepVWHI7khagzHJY0UK0SN4aRkOC7ZXTxhu57TGM3ucchwXDIYl6TK6RrN7rFZPqZL1EjhXIeo0UjhtCQ3tus5jUyNrjqcy0hujGanHCJqpHC6RKekcC6TKqeRF/dJldNXh7NPpkaqnK6333kfUeP58jFXGU5Kfml2jzqcFCtEDVGj760vvksjhXMVUUPUSLGilRcPaNXhSG4cImrkxQMaKVakcESnNFLltESntKrFI7qG45JUOa2333mfFE4K5zKiRkNyow6nlamRKmefweSEwbgkVc6LEp3SSOFcRdSQ3KjDaYkahwzHJYNxSddwXHITkhs3IblRh3MV0SlvffFdUuV0vfWld0mV05WpkSqnS3KjDqc1nJTs1nNSOK1MjVQ5LcmNRgqnK4XTSOGIGoeITmmkyrmuweQEyY1jDMcl2/Wc1mh2ymZ5TgqnS3RKtXhEl6ixvXjCdj2nNZickGJFXx1OazA5oSsvHjAYl+zWc7r+/C//Fimc143wxgsZTE5I4bREjUYKpyVqpFhxHcNJydvvvE8jhdM1HJekcPYRnZLC6dssz0nhSG7kxQMuU4ezW89pZWr0pVjRytRo1eE0RKccIjolhSNqdA0nJSmcLsmNOpyG5MaxRI0UTl8KpyvFitZnv/guKZxWpoao0ajDaQ3HJalyWqJTGnU4h0hubJbn7JOpkSqnS9RI4dThNESNwbikDqclaqRY0RrNTsmL+xxD1EjhtESNFCu6BuOS7XpOK1MjhdM1mt1jNDulDueQ0eyU7XpOS9RIsaKRF/cZTkqGkxNS5RyyWZ7TGs1OydRo1OE0PvvFd0mVs09ePGAwLkmV08qLB4gaNyFqpHCuImpkalwlhTMclwzGJXU4+2Rq9IlOSeG0MjUOGYxLBuOSLlEjhdNIlZOp0UqV0xKdksI5ZDAuqcPpSuG03vrSu+TFfbYXT2i99cV3OUamRqqcxmBc8tkvvksKp5Wp0TcYlwzGJaly9hE1UqzYLM85ZDQ7Zbue05fCSbGiK1VOCmcfyY1DhpMTUqz4NBDeeGXqcLpSOF2SG3U4XSmcQ0SnpHD6UqzoEp3SNxiXbC/mtCQ3GpvlY65rOC5JldMYzU65c/eMLtEpmRqpclqixk2IGimcmxiMS7pEja7hpOQykhupclqiRgrn2dOHNPLiAaPZKbv1nDqc6xA1UqxIlZOpcZm333mf4bhku57TkNxoZGrs1nNEjatIbtThdA3GJduLOV3Dccl2PWef4aQkhdMSNfry4gG/NLtHn+iUFM51iRq7iye0RI0UTtdodoqokSrnOkSnpHC6MjWuksIZjEsa2/Wc6xA1GimcLtEpKZyWqJHCaYkan7l7xm49pytTI1VO687dMw7Ji/uIGn2pclrDSclwckIKp0vUOGQwLtksH5NixT6ZGrv1nJaoMRiXpMppSW50iRp9kht1OM8+ekjX7uIJreGkpEvU+OwX32W7ntMaTkoyNVqj2Smj2SlX2Xz8mBROazA5YXfxhL7Nx4/ZLM9piRqNOpx9JDfqcBqZGo1Mjd16zj6DcUkdTh3O605441aITmlIbtThdA3HJZvlOQ3JjcZodsp2PacrU2O3niNqtESNQ0SNFCu6RI1jZGqkymllaqTKaYgaolMaKZwu0SkpnMukcESNQwbjkrffeR/JjTqcLlFj8/FjrnLn82eIGtv1HFHjM3fPuEodTt9meU5rOC6pw+kSNVI4XZkaKZx9BpMTUqxoSG6IGimcvkyNy6RwWqJGCuc2ZGqkyunK1Hi+fMxuPaclOiWF01eH05diRVcKpytTY7ees1vPuY7huGS7nnOIqLG9mNOXwsnUaKVwulI4hwwmJ+wuntBXh9PI1Lhz94wUTlemRqqc6xqMS1Ks6NtezGmJGscQNVI4VxE1JDeqxSNaw0lJCqdL1EixIsUKUaORwumT3EjhtDI1Ujib5TmtTI1UOX2b5WNSOF27iydkajQyNVLl9KVw9hGdUofTl8JpDSclKZzteo7kRp/olEamRqqcvlQ5fdv1nDqcluiU3XpOYzQ7JVNjOC4ZzU7ZreeIGq0UTlemRqqcFE6fqNGQ3KjD+bQS3nghkhtdw0nJbj2nS9ToE52SwrkJ0Snbiyd0bddzuiQ36nCOkcJpiRqSGw3Jjbx4wGByQooVdTiSGw1Ro090SgqnJblRh/N8+Zh9MjVS5fTt1nNEpzQyNVLltIaTkuG4JIXTEDUau4snpFjRyov7PF8+pitTo1o8oitVTtdwXFKHcxN1OF2ixnY95xDRKZvlY/YRnbL5+DGpcq4iuVGH0yVqpHC6RKdslud0iRopVrQkN+pwni8fI7nRtVme0xI1jiFqpFjRJTolhdMSNVI4h4xm93j20UP6NsvHtESndGVqiBopnJaokWLFIZIbLVFjNDtF1KjD2Udyow6nMRyXjGanpFjRlSrnMs8+ekiXqJEq5xgpHFFD1Eixoms4OeEQyY06nO16TkvUSOEcS9RoDCYn7C6e0CU6JYVzSApnszynNRiXbNdzulLlSG5kajQG45K+0eyUPlFDcqMlOiWF0yU6ZXfxhJbkRh1On+RGCme3niO50RA1WoPJCaKGqNEnuXGMweSEFCtamRqpclLlZGo0MjVS5XT9+Y+hDud1I7zxidksH7Ndz2mJTkmxokty4zK79ZzWaHZKCidVTqZGYzgp2a3ndIkafaJTNsvH9KVwbksdziGbjx+TwhE1bkp0SmOzPOc6RI0UTl+qnENEjS7Jjb7huKRaPOKQFCsOETV26zkpnJbolBQrWsNJyW49J4Wzj6hxmTocUaMxnJTs1nO26zldokZD1OhKsULUaIgaxxA1UqxI4YgaDVFju57TVYfTJWqkcPYRNVKs6JLcqMPZR3KjDuc2ZGqkymkNJif0Dccl2/WcrtHslBTOPqJTUjhdkht1OPuIGnU4qXIyNRqiRqqcrlQ5KZx9hpMTUqxoiRp9kht1OKlyMjWukqlRh9MSNfoyNVI4reGkJIWTKqcrVU6qnEyNVrV4RF+qnK7N8jHbiye0BuOSOpxUOZkamRqpcvqGk5LbJGqkcPo2y3NaokYKpy+FkyrndSe8cetEjUamRkvU2K3ntESNFE4KR3KjIWrU4XRlavSJGpd59vQhXaJGHc4hKRxRo5Wp0SW5UYdziKiRYsVlBuOSvHjAYHLC7uIJfbv1nC7JjS7RKbuLJ/RtL54gubFPHU4K5zpEp/SJTkmx4pBUOV2iU1I4XaJTGpIbjeG4ZLM8J4XTNRiX1OFc12h2SrV4RJeokWLFdYkaKZyu4bikMZqd0pDcqMO5CcmNOpy+FE5LcmO3nrNbz9lHciOFcxVRo5EqpyW5UYdTLR6xT6ZGqpx9RKekcOpwJDcuMxyX1OF0iRopnDocyY3rSLGiK1Pj+fIx+0huXCaF08rUSOF0SW7U4XSlWHETosZNiE7pEzW6MjUus1me0yW5kcJpDcYlKZzdek4rUyNVTkvUSOGkcG5LpkYrL+4znJRcRnKjT9RI4bREjUYKp/Xnf/m3SJXzuhHeeCGZGn3DcclVMjWu45dm92iJGlcRNRqiU25LpkYjU2O3npMqpyW5cYxUOY1MjYbkRh1Ol+iUq4xmpzREjTqc3XpOV6qc4eSEhqhRh9MQNRqSGy1RoyE6pU/UqMNpiRqbjx9zXZkaKZzdek5L1DhE1BA1+jI1dus5+4gadTiXEZ3SVYcjajQkN44xHJf0iU4RNVqZGs8+eshmeU6XqNElubFdz+nK1EiV0yW5cRskN+pwujI1UjiiRmM4Kdmt56RwulLldIlO2V084ZAUK7pEp6RwWqLGbj3nMqlyWqJT+iQ3dhdPaIka2/WczfKcluiUOpzWcFxSh3OI5MZm+ZhDnn30kBTOPqLGTaXKEZ3SEjWOkcI5hqhxleGkRNS4juHkhFZe3KdrOC4ZjEtEjS7RKV2iRmswLrnKcFLSkNw4RNRoZGr8L//XgOG45HUjvPFCRI1f/vKPyIsHtO7cPePO3TMGkxO6RA1RoyG5cZlUOZIbreGk5LNffJcuyY1jpcqR3GhIbvSJGsdK4bQyNVqiRkNyY7M8p5Wp0RpMTmhkaqTKSZWTqXGVTI2GqNFKldMlOiWFI2o0huOSVDl9u/Wcm9it56RwurbrOZIbreG45FiSGy1Ro09y46ZS5XSJTumT3EiV08rU6JPcSJVziKhxFcmNfVI4fSmcy0huNDI1+iQ3+jI1GqJTGpkau/Wcxp27Z1xF1OjK1OgTNepwuiQ3ni8f0xrNTtksz9mt53SlWNH37KOHZGp0pXBaosZl6nAyNRqj2SkpnL5UOS1RY7eeUy0esU+mRgqnJWq0JDcao8/do090ynY9J1PjKpkaqXIOEZ2Swjlks3zMPqJGCqdLdEpfpsb24gmHpHAaokZfXjyglRcP6BI13vrSuxxD1LjKnbtntO7cPaNLciPFii5Ro0/UeN0Ib7wUo9kpo9kpLcmNQyQ3UjiHDCclo9kpo9k9WpIbl0nhtIaTkqsMxyUt0Sldooao0SVqND77xXfpEzVaKZzdes5wXHIVUeMQyY0Uzj6iRgqnK1OjT9S4TKqcfUSNFE5XHU6XqPEySW5cJoXzsohOuYzkRmswOSGF05cX9+nK1DhGpkaXqHEdo9kpDVGjsVvPkdy4iuTGZVI4olMaw3FJqpy+zfIcyY2GqNEQNTI1GqLGdj0nhSNqNESNFE6XqHFIpsZuPWcfUSOFI2q0RI0UTitTY7eek8K5rhROXwpHdMpVRI0UTgqnJblxE6JGCqdrOC45RNS4zG49Zx9Ro28wLrnKYFxyiKjR9/Y77yNqdIkafZIbo8/dYx/JDcmN153wxiuRqdE1nJQ08uI+w0lJV6bGIXfunpEXD2hlaogao9kpreG4pC8v7nOZweSEfSQ3+u7cPWM4KWllanSJGpdJ4VxXpkYrU6MhavSJGl2iRt9wXHLn7hl9KZyW5MYxMjVuS6bGVSQ3riK50RjN7tESNQ4RNfqGk5LGcFzSEjX6huOSz9w945DR7BRRoyVqDMYlXaJGQ9ToEjUyNVqiRiNT4zpEjUamRkN0SrV4RFemRpeokcLpEjVSOF2ZGqJGl6jREDWuS9QYTkoaw3HJMUSNywzHJS3JjcsMxiWixj6iRqZGQ3KjT9Tok9zoEzWuImpcRXLjRYgaty1T4ypvfeldhpOSrtHsHpIbt2E4KRnNTmllatThfBoJb7wykhtdv/zlH5EXDzgkU+MYkhuixj558YAuyY2+wbhE1OjK1BA1MjVao9kplxnN7tHKi/s0RI2GqHEM0SldosZtETX6JDcakhsvStToGo5L+iQ3jiE6pStToy9ToyFq7JOpkcI5huiU1nBcMhiXdIkaosYxRrN7ZGq08uI+faJGn+SGqHGVTI0+0SmHiBrHqhaP6BM1ulI4+0hupHC6JDf6JDe6RI28uM9lRKe0JDcOydQ4RHRKn6jREDUamRo3JbkxGJeIGo1MjWNkatwGyY0+0Sl9kht9oobkRuuXZvcYjEuOlRcPGE5KDpHc6Pv4ZxPeuJzwxishOuWQTI3Gxz8bc12iU4aTE7pEp4gamRpXGU5KRI2u4aTkzufPGExO6JLcEDWuImpcRtS4zP/8f/+Ixmh2ymh2ym2S3GiJGoNxSZfkxj7DSUmf5Eaf6BRRo28wLsnUaIgav/zlH/HWl97lRYkafXU4faPZKW+/8z558YDrktwQNVqiRqbGPpIbDVGjLy8eIGq0MjUukxf3aYlO6ZPcEDVeBtEpNyE65ZBMjb5MjWMMxiVdolMao9kph4hOaYlOOUTUaGRqdEluHCJqiBr7ZGocImo0RI1DRI2W6JQ+UeNYmRrXMZyUjD53j5uS3LjMW196l+GkpO+3v/MnXEemxqeB8MYrMZqd8pm7Zxzj//z+n/Ov/8OPaIkag3HJPnnxgLx4wGVEjS5Ro2s4LpHc6BpOSkazU16UqNEnaqRwuvLiPl2iRp+o0SW50ZDcaEluDMYlw0lJ13BywnUNxiWXGU5OaOXFA0aze4hO6Rp97h6ixnWITrmJTI2G5EaXqHGIqNHI1GgNJydIblyXqDGa3SNT47rufP6M4bikJWoMJyd0ZWqMZvcQNRqixmBc0iVqNESNluTGIaJGQ3RKS9Q4RNTok9zoEjUao9kp+2Rq3AbJjUNEjdsiuZHC6crUuEymxk1JblxF1LgtmRp9khs/r0SnXCZT43UnvPFKiBqixj6ixlXe+tK7XJeocYw7d88YTkoO+bO/rPh//t+/5LokN25C1OiS3Lhz9wxRY5/h5ITrytToe+tL79IQNboG45J9huOSQzI1bouo0cjU2Ed0iqgxmp3Sevud9xE1uvLiAcNJSZfolH2Gk5K333mfvHiAqLGPqCFqdA0nJaJGazQ7JS8e0JUX9xnNTunKi/sckqkxmp0ianQNxyWt0eyU0eyUQ0SN2yA65SZEjb5MjUNEjUOG45LBuKRLcqNL1BiMSw4ZTE4QNboGkxNEp7REjV/+8o84JC/uMxiXtCQ3bmI4KRE1ukSNRl7cp3Xn7hmj2SmiRitTI1PjtmRqNCQ3XpSosVvP6RKd8mkgvPHK/OmPf8af/vhnvEqSG5fJ1LgO0Sl/+uOf8a//w4/YZzAuETVao9kpn7l7RpfkxmVEjX3e+tK7jGan7DP63D0G45Ku0eweokafqHEbRrNT8uIBrTt3zxjNTtlH1BA1ugbjksG4RHRKS9RoDCclrby4j6jR9fY77yNq7CNqSG70jWb3eNXefud9rkvUEDW6MjUOyYv75MV9+gaTE7pEp9wWyY0uUaNL1OgTnXIZUeM6MjX6JDcG45K+TI2WqDGcnHBIXtynK1OjIbnRGs1Oefud9xnNTmmNZqfkxQP2kdxoSG60RI3LjGb3aI1mp+TFffokN7rufP6MY4hO6RKd0ie5cVOixjGG45JjiU7py4v7iBqvK+GNV+ZP/7Liv/vfP+RVGY5LRp+7R9+dz58harwMeXGf4bikS9Q4RNR464vv0hqOSw7J1OgbzU4ZTkq68uIBo9kp+4hOOSRT4zKixk28/c77jD53j9ZwUjKcnHBIXjygT9Q4ZDguyYv79OXFA0SNX1SiRktyQ9ToEjVEjZbkxjEkN/okN/LiPvtkalxGdEprOC7Ji/v0/X/swW+MXGl+0Pvvltfr59QMS51nvGg6W779tGrAzApBPJlmzaDE1aOVJ1ZGCrCO5EWAu5FuxIyIdsRGArEr7NZlhGBIJBTkfQWullbaVoTCi/CmfVfj02zGgMrxZsPGjfEc16+Sy9YA0e+poGjPb7VscnUUlVK3bv3r7vJ/fz6VpM6wI9U6s1Sqn2aWo8dPc6RaZ7+OVOuMOlKts0jPnXqPgaPHTzPOJ//y1zl6/DSlI9U6pUq1zrBKtc7Hj3+WgUpSZ5ZKUmec5OQ7HDtxnoFjJ86TnPwii5Sc/CJbN3vM8kH/JNNUkjqjkpPv8Nyp90hOvsOTrMIzj5RKtc6iVKp1jp04z6ijx09zUBKNaY4eP02lWucgKtU6A5Vqndrnvsk0lWqdRfn4C6d57tR7HKnWGVap1hmoVOss2rET55mlUq0zzpFqnf2oVOscPX6aJ9nR46d57kff4yCOnTjPx184zcCRap15HanW2Y9KtU6lWqdS/TQDlWqdWSpJnVKlWmeSSrXOPCrVOotQqdY5jGMnzrMflWqdcY6dOM+xE+cZOFKtM+ro8dMkJ7/IwJFqnXlUqnUq1TqLlJx8h//z//6TTJOc/CK/u/x/Mc3R46c5duI8lWqdp02FZx45v/P94zzJjlTr/LYd53fsOIf13Kn3OHr8NId19Phpjp04T6VaZ1jtc9/kQTp24jwff+E0A5WkzjjHTnyeZ8Y7evw00xyp1ilVkjrDKtU6R4+fZuATJz7Px184zbBP/uWvc+zEeQYqSZ3Sc6feY7+OnThPcvIdFqWS1BmoVOtUqnWGVaqfZpZKtc6wSvXTzCM5+UWOVOssQnLyixw9fpr75diJ8wxUqnVKlWqd/TpSrXMYosYslWqdeTx36j2OnTjP06bCM4+cSlLnUVOp1vltO86i/I69wCIupdO8AAAgAElEQVQcO3GegzhSrTMqyyMb23vMcqRa536pVOscPX6agaPHT5Oc/CKjkpPvUKnWedpVqnVqn/sm+1Gp1pnH0eOn+eRf/jrJyXeY5Ei1TqVaZ9jHj3+WY//H5xlWSeocRKVa5+gLpxk4evw0f+K1rzMsOfkOx06cZxGOnTjPc6feY9iRap1ZkpPvUKnWWYTk5Dts7nQQNSapVOtUkjqLUPvcN3nm8VThmUfKJ1/7OpVqnUfN0RdO89yp91iU37bjPEyVap3nfvQ9RokWTPMnXvs6lWqdB+nYifM8s1iVap1FqFTr1D73TYYdO3GeYyfOM+xItc4sR184zfOn3mPUc6feY9jR46cZ9dyp9zh6/DQDR184TSWpM6pS/TQDlaROpfpppvn4C6epVD/NsEq1zv2W5RGJBZN88rWvc6RaZ5pK9dPMo1KtcxCVap3nTr3HMw9PhWceKZVqnSPVOhKNRRA1RI3DqlTrLEpy8ov0nv8pRAsepqPHT7NfR4+f5mlUqdZ5ktQ+902OHj/Ng/LcqfdITr7DNJVqnd+24yzCc6fe4+jx0wyrVOtUqnUGjlTrzHL0+GmSk+/wqKlU61SqdWb5Zu85Wu0e98uxE+d5VGxs7/G0qfDME2/l3Rs8SirVOkdfOM2jRtSQaDyuNrb3yPLI/XKkWueZ+0tiwdqVW9wPx06c59iJ8wxUqp/mUSXROKxjJ87Te/6n2Gr3eJxJNObRavcQNZ4mFZ55okkseObpIFogajzzaBA1HmXJyXdITr7Dk6pSrfPxF07zzJOrwjMPnKiR5ZFnoNXu0Wr3eObRVknqVKqfZhJR45n/r43t22R5ZD9EjcdBpVrnabWxvcfmTocHSbTgmf+/Cs88cFvtHps7HabJ8sjG9h5Puq4aXTWemUzU2Nzp8DAdPX6a5OQ7jJPlkbWv3uKZ6VrtHq12jydB7XPfpFKtM2xjew9RY+XdG4gahyVqPA1EjWcOpsIzD4yoMQ/RAlGj1e6R5ZFHlagxjqgxL4nG42btyi2yPPIgtW72eFSJGosiaogaTwKJxrDdvE9XjXlkeaTV7vE4yfKIxAJRY7+yPPI4kWg88/BVeGZhRI2Vd2/wNBA1Vt69wThb7R6bOx3mIVog0XjSZHlkY3uPRZBYIGqIGps7HR41XTVEjUXI8sjKuzfY3OnwJBA19kuiIWpstXvcb1ke2dzpsCiiRkliwThZHtnY3mPUxvYeosbjQLTgYVi7cossjzzzxyo8sxCiRknUEDUmETVKEo15iBqPMlHjcZPlkc2dDveTqCFasEgSCy5f6zBNV41pRI21K7dYpMvXOjwONnc6iBqPC4nG/SZqSDSyPJLlkftN1JhEYsGAqFFaefcGz/yx3Q/7TCOx4GlS4ZmFWHn3BlkeWbSuGpOIGo8iicaibO50KGV5ZJFaN3schqjxoO1+2OewJBZINJ50rXYPUWNYlkckFjzzx7pqlHY/7LPV/ohp1q7cQtQ4jK4a44gao7pqiBqixkF11XjmyVXhmYWTWDCNRGMRtto9Nnc6zGNzp4OocViixqKJFkxy+VoHUWPtyrcQNaZptXuIGpOIGq12j5KoMUmWR9au3GISicbD0LrZYxqJxpNConEYm9c6jCNqPA66akwjaiySRGOWLO8jsWAeosYkogWtdo/NnQ7DRI1RwTskFoyT5ZEsj8xD1Gi1e8yS5ZF5iBZMI2ps7nSYh8SCZw6mwjMPhahxWBKNebVu9pBYsAhZHtm81qEksWASUWNeH/vS+4wSNUoSC0oSC6bZvNYhyyOTZHlkq91jFlFDojGLqDGLqDGPzZ0OmzsdphE19kPUGEfUyPLIyrs3WJTgHaLGo0LUGCXRGNVq91i7cosHRaIhasxD1Jhk5d0biBqPq64aEo1ZRA1RY5yt9keIGvPI8sjmtQ6zbGzv0Wr3OCyJBa2bPeYhanzsS+8zTZb3yfI+Eo1ZVt69QZZHngYVnlmYrhqL1rrZY2N7j0kkGvMQNRZF1Gi1e0wj0dhq99jY3uNByPJIqavGLKLGImxs36bV7jFNlkdW3r3BLBINicYswTtEjWla7R6bOx02tm9zWFkeETUmETWCd8wjyyNZHnlQJBa02j1EjUXY2N5D1DiszWsdNnc6PGpECx4k0YLD6qohakwj0ZglyyPzEDUkGvMQNR4UUaMkaogaa1e+xeZOhyddhWcWbvfDPlkemUS0YB7BO0SNVrvHOKIF+7Gxvcc8Wu0eG9t7LIJowTQSDYlGSdSYRtSYZPfDPqLGNF01JBpdNWYRNT72pfcRNSaRaEzSVUOiURI1Nnc6TCNaIFqwCFvtHpevdZBoTCNqzLK502Gr3WMRdj/ss9X+iAdpY3sPiQXTSDTm0Wr3kFgwTZZHphEtEC2QaCxalkdEjf0SLRAtmIeoMYmo0VVjHhKNwxItKG1s32Zzp8M0XTWmWbvyLUSNUleNSbI8ImrMS9SYRGJBqatGae3KLUSNUaIFA6IFosYzf6TCMwsj0Si1bvbYan/ELB/70vtkeWSSkDoGRI2DEDUGRI15dNUQLZikq8aAqDGOaEHrZo8s75PlkWlEjWlEjdJu3ifLI+NINOYharRu9iiJGqNEja4aAxILpumqMU1XjdLlax3G2djeo9XuUZJoPCiixryyPLK50+GwJBqiBYsgaswiaszSVWM/RI2SqDHO5k6HVrvHJFneJ8v7DGR5ZHOnwzBRI8sj+7XV/oitdo+HSaIxL4nGPDavdcjyyDQSjYMSNQZEjUXa2L7NvLK8T5ZHppForLx7g3lINJ50FZ5ZOFFDtGASicaAqHFQEo2DyvKIqDGwduUWG9t7fOxL73P5WgeJxmGJGqWN7T0mETVm2Wr3KLXaPUSNaSQa0wTvEDUelGXvmCbLIwOihqhxWBKNRcryPlkeeRC6apREC6bJ8sjalVtMs7G9R2mr/RGixiSixiytdo9hK+/eQNQYJdHYD1EjyyOjsrzPQUg0Roka02R5H4nGInXVKLXaPbI8sh+iRkmiURI1ttofcViixjRb7R7zaDZqSDSmETXuB1EjeMckXTWeJhWeWRjRgnmIGsE7Sl015iGxYJrNnQ4b23vMa6v9EVvtHgdx+VqHeQTvKIka85BYMI5E41EkaszSVWMaUWPY5rUOo0SNgxA1ZhE15iHRWASJxiJ01ZBoDKxduYWoMSp4R6vdI8sj07TaPabZzfvMq6vGvLpqZHmfSUSNw9pq91h59watdo9JRA2Jxjitdo+1K7cobV7rMLCxvYeoMSzLI8N28z67H/YZJVqwX612j1a7xzCJRkm0YBqJxixZHgneIdFYFInGLBKNga4as4gaz/yRCo+A733ve9y7d4///J//M9/+9rf58MMP6ff7PC5EjZJEo9moUZJoTBK8Yx7BJ5SCd4gak4gaWR45LNGCUrNRQ9Q4DIlGSB3BO0qiRqvdY0CiIdEYtvthn0majRqlrhqixrAsj5SajRpZHmm1e4wSNUohdUzSVaOU5ZEBUWOcje09Spevddjc6TBNs1GjJGpMItFoNmqIFsyy9tVbiBoPgkSjJGrMIrFgEYJ37FeW9xkVvGNeXTWmES0oddV4EIJ3HNbmToeN7T1aN3uIGlvtHgfRVUOiEbxD1NjY3kPUEC3I8sgwUWOURGMeXTWGiRYMiBbs5n128z7DRAuyvM9+be50EDWGBZ/wMIgW7FeWRz72pfeZRdQQNZ5UFR6iGzdusLa2xqlTpzh37hx/42/8DTY2Nvipn/opPvvZz/Laa6/xta99jcdJs5Eyj5A6FmVj+zZZ3ke0YJrgHaLGJBKN0sXVJcbpqjGqq8Y01996hVKWR3bzPsNEjWGXr3UQNQYkFoyz8u4Nhm3udGi1e5REja4ao7I8cvlah2ESC8bJ8j77IdFotXtsbO8xSqJxcXWJ4B2T7OZ9RI2Lq0vMElKHqDGJREPUGNjc6bC502FA1Ch11SitffUWrXaPcbI8ImrMI6SOaUQN0QJRY5aQOiQaWR4ZJWps7nSQaIga04gaIXWUumo8KBKNga4a47TaPTZ3OkwTvGMgyyMDokZp81qHj33pfVrtHiXRgmFZHhEtEDVKEo15iBrTtNo9pgneMQ9RYxqJRvCOaSQa8xAtGHb5WodRWR4JqeNBy/I+AxKNSYJ37NfG9m02tm/zpKrwEKgqP/7jP87f//t/n5/92Z/lgw8+YG9vj29/+9vcunWLO3fu8B/+w3/gH//jf0yr1eIv/IW/wK/92q/xOFj2js6XX0PUmObi6hIlicYkZxo11leXCKljHFFjlKjRaveYRGLB/SZaMBC8I3jHbt5HtGBU8I55BJ9QkmiURI1RwSdM0lWjFHzCfmV5ZB6iBcNEDdGC4B3TZHmk1GykSDTmIbFgY3uPVrvHgKghWjBMojGNqDHO2pVbbO50CN4xIGoclMQCicY8gk8QNbbaH9Fq99jc6TCQ5ZEsj7TaPUor795A1BgmagwEnzBL8I7WzR6bOx1myfKIqHEYwTtKWR4Z1VWjFFJHaavdQ9TY3OmQ5ZFhogWlje09Wu0e40g0hmV5ZGN7jwGJxoCoUZJYsPLuDcYJ3lGSWCDRmIdowYBEo5TlfWYRNULqmKXZqCHRGEeiIWpkeZ+BLI8Mk1gwTqvdY2N7j2FdNYJPWLRmo8YsIXXsh2jBk67CQ3D+/Hn+9b/+13zzm9/kC1/4AsePH6dSqTDMe8/rr7/ON77xDb7xjW/wi7/4i/yv//W/eJSJGsE7gneUWu0ea1duMc766hJXL7zMLFcvvEzwCV01Nrb3yPLIJBKNLI9stXuMCt5RWrvyLUSNwwjeUQre0brZo9Xu8TBJNEohdZQkGqMkGqUzjRqlZqPGvDa298jyyKhmo8YsWd6nFFKHxIJxRI1hWR5ptXuMCt4xTLRglEQjeMeAaIFEY5quGpM0GymLElLHfogWdNWQaAx01RgmamR5pCSxYJauGgOtdg+JRknUyPLINM1GjSzvk+WRUaKGqCFqzBJSR0miMcvlax0kFkg0ptnN+8wiaogaogX3U0gdl691aLV7iBaMkmgE73gYRI0BiQXT7OZ9hm3udLh8rUNIHaLGPESN+0HUGEei8TSp8BC8//77/Ok//aeZ16c+9Sl+5Vd+hU9+8pM8TrpqDOuqMU6WR7I8MklIHZevdWi1e4gaBxVSx4BowSTBO4J3iBrjhNRx/e1TXDq7gqixm/eZJqSOSZqNlHmE1DHLmZdqzKPz5deYJnhHs1EjeMeAqDGq2UgpiRYsiqghamxe6zBO8AnDumoMEzVC6lgEiUbp8tkVmo0aWR550CQaoyQawTuCd8wSUsckW+0eWR6ZV/AJpa4aJYkFA2tfvcXG9m3mEXzCfoga42R5n0VZX12i1Gr3KIkah9VVY5rgHR/70vtME3xCSaIxjajRavfI8sjG9h6jgncE71j76i2mCT5BtGBAtOBBCD7hIESNYaIFwyQaT7IKD9ndu3e5e/cud+/e5e7du9y9e5e7d+9y9+5d7t27x3e/+10eR8E75rXV/oit9keMCt4xi2iBROP626eYl8SCkkRjmKgRvGMezUbKQJZHRI1pRAumaTZqNBs1JBYcxvW3TyFaME3wjuATRI1xQuq4/vYrrL+6RFeNUaJGadk7BrpqSDTGCWnCOKLGQPCO4B3zEjUmCT5hWJZHRrVu9pjXpTdWCD5hEYJ3iBrThNQxTLRgQKIhaoTUcensCvOSaIwjagxkeZ9pzjRqNBs1xhE1BkQL5iFqTBNSx4CoMU6zUWOYaMGo9dUlZgmpo7TV7jEPUWOS4BP2Q9SYJKSO4B3jSDREjWYjpbR5rUMpyyOTiBqixjQSjSyPjJJojBI1FuFMo0ZJtGCS4BNmyfI+66tLPC0qPGQ///M/z5tvvsmbb77Jz/zMz/AzP/MzvPnmm7z55pu8+eabrK2t8ZnPfIb//t//O48biYZEY5LgHaIFoyQa85JolEKaIGp01ZBojBN8QknUGCXRKK2/ukSzkTKv4B2ihsSCSYJPkGiME1JHKfiESUSNi6tLrK8uMQ+JxijRgnkFnzCP4B2Xz64wzfrqEsE7gk8QNSa5dHaFeVy98DLrq0vs5n0kGgclagTvCN4h0Rgl0Ri1m/eZJPgEUWMeWR4ZZ3Ong0RjQKIxzfrqEs1Gja4ak1xcXeLqhZeZJqSOWSQazUbKxdUlsjwyyfrqErOE1LEowSfsl0RjnOAdEo1xRI1SSB3zkmiMI2qE1BFSR0liwSTL3nH9rVeYJXiHqFESNabpqjGOqBFSh6ix+2GfkkRjmrWv3kLUGKfZqDGv9dUlrr99ConGJCF1DOzmfUoSC0ZdXH2R9dUlngYVHrK//tf/Oi+++CL/6T/9J37jN36D3/iN3+DXf/3X+cxnPsM/+Af/gO985ztcuHCB8+fP8zhpNlJEC8YJqWOYaIFowUGIGqXgHbNcOrtCs1FjHs1Gysb2bVrtHqOCTygF7xA1xhE1gk+YZdk75nVx9UWyPFKSWDBOSBNEjUVY9g6JhqgxycXVJSQaoyQa+xG8Y6CrxrCuGqNEC0SNcULqGJBojBO8I6QOUaO0duUWosYwUSOkjlJIHVkeOQhRY2N7j4GN7T3Gad3sIVpQ6nz5NUSNg5BYMI6oMU7wCQOtdo9Wu8eAqLGxvcd+SDREjf0QNSbZzfsMk1gwSrRAojFK1AipY6CrxjDRglGixjCJxkDwCeOIGvsRfELwCeNILBhHtEC0YBxRoyRqjBNSx35INA4r+IT9CGlCSdSYJnjHLJfOriDRKEk0nlQVHrL33nuPX/7lX6ZWqzHw/PPPc/XqVX7xF3+Ro0eP8pWvfIX/8T/+B9///vd5Gi17xyhRYyCkjkm6agwE7wg+YavdI8v7TBNSR5b36aoxD1HjMELqKG21P2JA1JhE1BgneEdpc6fDoyCkjmFZHpkmeEdJ1Ljfrl74DJfPrlCSaEgsuF9EjeATrr/1CsE7RI1Wu4eoMSBqSDRKwTvmEXyCRGNRtto9Nq91GNZq95hHlvc506ghamxe6yBqHEazkTIvicY0wTtKEo1F6KoxSUgdwyQaA6IFJdGCi6svErxD1JgkeEdJ1JjHVrvHLBKNYaLGwLJ3TCJaMErUkFgwqqtGSB37JWpkeWTtyi3GuXx2hWf+WIWH7H//7//NkSNHGFWpVPjBD35AqVKpUPrBD37Aoy6kCaWQOiQa04Q0QaJxWFcvfIZ5hdSR5X0WIaQJ8wipQ9SQaIwK3jGs1e6R5ZFJRI1RosawZqPG5WsdsjyyKF01FmFjew9RYxqJxjQhdUg0Jln2jtLlsyuIGpME7zjzUg3RgpKocb8F7wipI8sjG9t7ZHlkkuAdogWHFbwjyyOjJBqlkDomkVgwSqIxTNQYCN4xTKIxIFpQWvaOeZx5qUazUUO0oCRq7IdEYyCkjlKWRyZpNlJEjWkurr5Is1FjHqIF0zQbKaWtdo9pgnfMErxjlpA69kO0YJLgHaLGLME7RI15BO8oddWY5NIbKzQbKdOENOFpUeEh+3N/7s/x1ltvURQFA9///vd5++23+TN/5s9Qun79Op/4xCd4/vnneZyIGg9C8I6HIXjHPJa9YxFCmrAfosao4B2lkDo2r3UQNSYJ3pHlkXlINKYJqWM37yNqSCwY1mzUaDZSSiF1iBbMImoMEzWG/eEvvM6yd5REjYGuGsNCmiDRKO3mfSY581KNcSQWhNSxH8EnjBI1DkvUGBW8Y5Zl75hXs5EiakyzvrrENME7BrI8Mk7wCc1GSrORMk1IHQOixiTX336F4B2ixjjL3nH1wsvM0mykXFxdQqJR6qoxatk75hVSx4BE434KPmGcrhoHsfbVW+yHqJHlkQFRY5wsj0wTUodowTCJBeOIGk+yCg/Z1772NX7v936PH/3RH2V1dZXV1VX+/J//83Q6Hf7tv/23fPe73+Xv/J2/w8///M/zOFn2jv2QaBxW8A6JxjRnXqoxjqhRWvaOYRKNeXTVGBVSx7yWvWNRgk8o7eZ9phE1xgmpY9GWvWMewSdINKZZ9o7DCqmjFLxD1BA1pglpwjQhdXTVKIkaByVqjJJojAo+YZEun10heMcoUWNY8I5ZLq6+iGjBNM1GjVKW95lFovEoCKljHsE7Lp9dQaKR5X0WRaIxTNQYuHrhZYJ3zEO0oCRqjAresb66hGjBLKLGfmy1e2zudJgmeMc8JBrTBO8QNZ50FR4B165d4/r16/zLf/kv+cpXvsLOzg4ffPABpR/5kR/h13/917l48SKL9Du/8zt84xvf4M6dOzwswTtEjVmCdwx01ZhGtEDUmCSkCfNY9o5Sq91jVEgdA8E7ZgneMU3wjuAdswTvGOiqMYtowSTL3nFYIU0I3iFqZHlkFtGCeYgaixC840ETNTavddjc6TAgsWAWiQUDy94xiWjBKNGCWUSNaS69scI4XTX2K6QJs1x/+xWCd8yy7B2ixiQXV5doNmqIGosQvGM37zNN8A7RgmnOvFRD1Jgl+ASJxizNRg1RY5JmI6Uk0RgmWjBw6ewKzUaNWS6uvsiAqLEoy96R5X1EjVkkGgMSjWHL3iFqlESNSZqNGk+6Cg/B//yf/5NRP/IjP8JnP/tZfvqnf5oQAsOef/55vv/977Mov/qrv8qFCxfY2dnhrbfe4l/8i3/B40ZiQfCO4B0D668ukeV9xgmp436SaAxINMYRNca5euFlmo2U4BNKosYkzUaNRdrYvo2oMU5IE0SNeYgaosYkwTuyvM84wScMhNQxS/COAYnGgxC8Q9SYZNk7Wjd7bGzfptXuIdGYpqvGMFFj1PqrS0g0pgmpY5aQJswjpA6JhqgxStQYCN4xS5b3mSWkjlmCd4wSNYZdf/sV5tFspByEaMEoiYaoMUlIE6YJPmESUWNU8AmzhNQhakwSvCP4hFESjVESjWlC6tivrhqlLI8MC94xEFJHlveZptlIeeaPVHgI/vbf/tv8zb/5N/nd3/1dZvn93/99/tE/+kf8xb/4F/ne977HYf3whz/k0qVLbG1t8d577/Fv/s2/4erVq4gIByWxYCB4R6nZSJkk+IR5hDSh1GykXL3wMqNC6uh8+TUGlr1jluAdA6IFo4J37EdIHbM0GymThDSh2UgphdQxSfCOUvAJs4TUMcv66hLBO7K8z/0gWjCs2UiZx7J3TBJSxzyCd5SajZR5BO9YBFEjy/tMElJHKaSOga4ak0g0RI3DCt4xINEYEDVmkWgMBO8YJmqUJBYMhDQheMeihDRhHsE7ZjnTqFESNeYhaowT0gRRYxFC6phHSB37IWocVEgTxpFoDASfMI+QOrI8Mq/gE2YJ3vHMH6nwEPzqr/4qf+Wv/BV+4id+gh//8R/n7bffZm9vj263y3e/+13u3r3LV77yFdbW1vixH/sxPvGJT/Dtb3+barXKYf37f//vqdVqvPTSS5S89/zET/wEv/Zrv8YiBe9YX11C1FiE4B0PUvCOeTQbKQe1vrpE8I5JumrMImoMhDShtOwd+yGxYF6ixn4se8coUaMkseAgQpowr+Ad8xItGBA1ZhE1dj/sUwrecRhdNYJ37Neyd8yj2agxS/AJosaDEnzCLME7hrXaPbpqjAqpY5bgHfMQLRgWfML9JGoM66pxEMEnTBJ8wiSiBcE7BoJ3lCQaw0SNgUtnV7h64WWCd0wTfMLmtQ4b23tINIJ3dNW4n4J3lIJPeNJVeEg+//nPc/v2bb785S/zX/7Lf+H8+fOcPXuWtbU1fvqnf5oPPviAjY0NfvM3f5OvfOUrLEq/3+fP/tk/y7Dnn3+e//pf/yvTnDx5kpMnT3Ly5El+6Zd+iYOQaATvCKljIHiHRONRE9KE4B3jLHvHwMXVJSZZ9o5pzjRqDDvzUo1ZQuo4jJAmzCt4xzyCdyxK8I5REo37qdlImWTZO4aJGgNZHrl8rUOp2UjpfPk1gneURAsGRI1FC6mjFLwjy/sMEzX2I3jHJKIFD5JEY1TwjoHNax2yPDIq+ISSROMwQuqQaAwTLdiv4B0DosYky95xWMveUTrTqDFLSB1Z3mceosYkwTvWV5doNlJEjWlEjSyPzCOkjnkE75jml37pl/iVX/kVSv/t//lvPKkqPGQ/+ZM/yfvvv89v/dZv8Z3vfIff/M3f5Pbt21y/fp2/9bf+FseOHWORfvjDH1KpVBhWqVT4gz/4A6a5c+cOd+7c4c6dO/zcz/0cB7X+6hKX3lhhmKghahzW+uoSVy+8zCx/+AuvM0zUGBW840ELacIiSTSmCaljmERjVLNRY1GCdwTvGNZs1Lh64WXGETXmJbHgQVj76i1EjXGCd4TUUZJoDHTVGCXReJCCT5glpI79arV7iBqj1leXmCSkCaWQOg5CojEqpI77Jcv77EdIE0rNRo39arV7tG72mESiURI17oeQJgwE7zgoicayd5xp1CiJGg/Sz/3cz/HX/tpfo/Tp+qd5UlV4hBw9epRjx45xPx07dowf/vCHDPuDP/gDPv7xj7NoVy+8zKhl7xgWUscoUeOg1leXGCbRWBSJxqjgHcE7HjSJxiTBOyYJ3rEfwScMbGzvsdXuMSqkjgFRY2N7j3HWX10ipI5Sq93joIJ3HJREoxR8wkGIGgNdNUZdemOFg8jySEgdw0LqeJCWvWOURGOaje09umoMBO+Y15mXagTvKEksGBZSx0BIHZME7zgMicYoUWOa4B3zaDZS9ms37yNqBO/Yr+AdoyQas0g0QuoY1mykHIZoQanZSBkQLQipQ6LxIIkaT6oKT5k/9af+FN/5zncYFmPkx37sxzis4B37FXzCPEKaUJJolESN4BMmETXm0Wr32Ni+zTjX33qF4B2ixsDlsys0GynDLp1dYZbgHSVRY7+Cd+xHSBMun11hmERjVLORMktIHcE7JBqiBbNILGi1e0g0Rl16Y4XgE0ob23vsftjnQRE1Jrm4+iLrq0Ad1fIAACAASURBVEvsl0RjP868VKO07B1ZHhnVbKQ8CKKGqDEqeMcoUSN4xzRZHplEtGCSZiPl+luv0GzUmCb4hAFRQ9SYh6hRWvaOeQWfsF+ihsSCUcveMUqiMSx4xzQhTSgte8cwiQXTSCxYu3KLWTa29xA1RoXUMcmyd8wi0SgF7xjI8j7jhNSxXyF1zBJSx5OuwlNmdXWV0u7uLqW7d+9y48YN/tJf+ks8DCF1BO9YpOAdswTvKHXVyPI+AyFNGAjeMWrZO4J3jBItmObS2RWCd8yjq0aWR8ZZ9o5ZgndcXF1iXqLGsGXvGLj0xgqXzq4wkOWRgeAdA8E7hokahxG8Y5rgHYvSbKRcXH2ReUksmEXUmEbUmJeoMUlIE/Zrq91jY/s2s2R5JHjHqJA6xgneMXBx9UUGWu0erXaPcYJ3PMpEjeAd02xs32Zzp8Oo4B33g6ix1f6IUSFNGJblfUSNYcveMSzLI8E7gk8Ytuwdw0SNcULq6KoxzeWzKzwswTueZBWeMpVKhX/+z/85//Af/kMuXrzIF77wBf7pP/2nHD9+nIfhzEs15hG8I3jHokk0Ss1GjeAdwTv2K3jHMFGjFLxjYH11if1Yu/ItWjd7PA6CT2g2UkqixrwkGvPI8kjwjmGdL79G8I6B3Q/7BO94kEQLJgne0Wr3mJdE40HJ8sg8dj/s02ykzCLRGCVqDOzmfbbaPUrBO4J3HERXjXlleWQWUWMgeMd+NBs1hkk0RjUbKZOE1DGOaMEsW+0erXaPaUSNwwjeEVJH8A5RY5rWzR7jBO8oXXpjheAd81j2jnkEn/AMVHgEfO9736Pb7XLv3j3u3bvHvXv3uHfvHvfu3eN++OxnP8sHH3zA1tYWN2/e5Cd/8ie5X4J3iBqlkDqCd0wiakxz6ewKixJSx7DgEy6dXWEciQUPg6gxzcb2HlvtHtNkeaTV7rEIEo39aDZqjNNVY5KQJowSNULqGHXp7Ar7JVowTkgTJBolUeNhWPaOWSQapWXvKAXvGOiqUeqqUQreMRBSxzCJxixZHjnTqDFJV439ajZSFiF4x7Bl7xjY3OmwduVbTJPlkY3t24gaw840agxILFh59wbjBJ9QkmjMI8sjK+/eYFRIEwZEC+634B3DRI2QOkY1GylXL3yGeYga44Q0YeDS2RXm0WyklESNAVFjmo3tPXY/7PM0qvCQ/bt/9+84deoUZ8+e5dy5c5w7d45z585x7tw53nzzTZ4EEgs2tvdo3ewR0oRxgndkeaTV7jEQvGOcje09tto99iP4hGGX3lhh2JlGjfXVJcYRNVbevcEsWR5Zu3KLB0W0IMsjk4gaW+2P2Gr3mGbzWgdRYx4SjXkE75hEolESLQg+YVTwjoEsjzQbNeYRUkcppAmLErxjEdZXl2g2UkZJNEqiRvCOg2g2auyXqDEqpAmjgneUgndMImoE75hEtECiIdG4dHaFWYJ3TBO8I6SO4B37EdKEAVEjy/uMajZSBkSNUkgdwTsmkWjMQ9QQNeYRUsciBO8Y1mykXL3wMsE7umqUrr/9CuME71iUZiOlFHyCaMEkwTsmWfaOUV01sjwi0Ri17B1PugoP2T/5J/+EL3zhC3znO9/hzp073Llzhzt37nDnzh1u377N4yJ4x/qrS4yzduVbtNo9RI1RzUbK+qtLhNTRVWM37/MghDRBolG6euFl1leXmEbUmGX3wz5Z3mcaiQWTBO9oNmpINIaF1DFJlvcZCN4xqtXuIdEY58xLNUqixtpXbyFaMIuoEbxjkq4aw4J3DAupY5rgHeuvLjGwsb3HJME7RAsGgk+YJcv7BO8IqWOUqCFqTBK846BC6hgI3jGPZe/Yr5V3b3D5WodpJBql4B3TSDRCmjCvkDoOI8sjogWixizBJ4wK3jEseMcswTsWQdR4UCQapeAd4wTv6KoxELxj2PrqEuuvLvGoEzXWrtxiHqIFpeAdT5MKD9nv//7v83f/7t/l6NGjPK5EjWYj5dIbK8wSvGPUpTdWuLi6hERDtGCWLI9keZ9psjxSEi2YRrQgeMc0XTX2I3jHJKLGx770PpM0GynDgnccRPCOWUKaMCBqlC6fXWF9dYlprl54mWEhdYTUMerqhc8Q0oRRogWlLO9zplFj1KU3VhgQNWbJ8kjrZo+QOqYRNYJ3TLPV7iGxIHjHJJs7HT72pfeRaIwKacIszUZKs1FjQNQI3jEQvGMcUWMgeMc0wTsmETVKIXVMkuWRUvCOeQWfMBC8Yz9EjbUr3yLL+4wKqaMk0Qje0fnya0wj0Sg1GymTdNUI3nHp7AqzBJ9wWLsf9ikF75hlfXWJWZqNGs1GyiQSjf0IqeNhCT5hHIkFWd4neMc0ooZE42lU4SFbWlrit37rt3hmsTa291iUy9c6zEOiUQqpY1FC6gg+YZRogUSjFLxjHsE7Zln2jnFEC0SNP/yF12k2Ui6fXWHg0hsrXHpjheAdEo311SXWX10ieEfwjmm6asxy/e1XmGar/RGixrJ3jLp0doVRIXVMcvlah43tPULqKIU0YVSW9xkWfMJA8I6Brholicaydwy7uLrEwOa1DrOIGsNCmjCOqDGNaMFA8AmlkCaUgnccVEgdk0g0Ztm81mHYsnccxuWzK1w6u8IkEo31V5doNlIm6aoxLPiEadZXlxjVbNTYr4urL9JspAwL3jEgWnBxdYkzjRrzCqljlEQjeMcsEguyPLIfokbwjmm6aowTvGOg2ahx/a1XmKR1s8fTrMJD9rWvfY2f/dmf5V/9q3/FvXv3uHfvHvfu3ePevXvcu3ePJ0nwjmmyPFJaX11iHiF1TLN25RZZ3qfZqHFQIXUMiBaME9IEiUZpfXWJZiOlFNKEUV01SqIFwTtGLXuHaMEkwTtGhdQxj/VXl5gmy/tMkuV9hl16Y4VJQuq49MYK8zjzUo1JJBYE7xgIPmGS9dUlgneMWl9dIniHxIIsjwwse8ckosY8RI1JQuqYl2hBs5EyjcSClXdvEFLHqEtvrNBs1Bi4fHaFkDqmaTZqnGnUGBW8oyRqhNQxTvAJJYnGPEQNUUPUGCf4BFFjIHjHQYTUIWqIFix7x2FJNEohdYwTvKMkakxy/e1XCN5RWl9d4vpbrzAseMcwiUZIE0aFNGFY8I6B4B2ThNRxWFvtj9jY3iN4xyStdo95hNQxy/W3XqEkagSfME5IHSVRoyRqjBNSx5OswkP2+c9/ntI/+2f/jHPnznHu3DnOnTvHuXPnePPNN3lchNRxGME7RA2JxsXVFxkneIdogagxjyzvUwo+4UEQLbi4+iJnXqoxiUSjJNGYJnhHKfiEkDpGZXkfUePqhZdpNlKmETVKF1eXGBa8o/Pl12g2agwE77jfJBqzhNQhasxDtODi6ouENGGStSvfYmN7j5A6rr/9CuurSwwL3jEs+IT11SWCd9xPogWli6svMs3uh30GLp9dYVizkdJspAxcemOF4BOmuf72KzyqLp1dodlImSWkjkmCdxxE8I5h66tLXFxdYpbgHSF1lIJ3jJJohNQxzvW3T7EIwTuGBe8IPuGwsjxSCqljESQa0wTvKG21exxGs5Fy9cJneJJVeMh2d3e5c+cOd+7c4c6dO9y5c4c7d+5w584dbt++zeOgq8YkIXUMhNQxSUgTSpfOrhDShEkkGrOE1PEoCqlDtGBef/gLrzPL+uoSy94xydULL1MSNYJ3jAre0WykLIpowbJ3TCNqPEghdcyj2agx7OqFl7nfsryPRGOc4B0Dl691GLj0xgrBO0ZJNCZZ9o6SRCN4x0DwjuAd04TUEVLHKNGCgeATxpFo7Nf66hLBOxYleMd+BO8YFrxjls6XX2PZO2YJ3nEYooZEI6QJwTsmES1Yf3WJqxdeZj+Cd4wSNWbpqjFN8I7SsneMCqljnCzvE1LHLME7Ss1GjWHBO4J3PMkqPAK+973v8ff+3t/j9ddf5/XXX+ev/tW/yne/+12eJsE7SsE7gnesry4xjqhRCt4xj+AdpZA6xpFohDRhHhKNSUSNUcE7RmV5n1kkGiF1jBPShHGCd4wTvKMUvOOwgncc1rJ3lIJ3lEKaMI/gE2YJ3lEK3jFJ8AmTXHpjhf0K3jFO8AnzEjVCmjBJ8I55Be9YhK4awSccVEgTSqJG8I5S8I7gHaPONGqU1leXuP72KUZdXF1ifXWJRRItWPaOg7p0doVLZ1cQLQjesV/L3jFKonFY198+RUgd8xAtCKljmpA65iHRWKTgHfvRbNQIPiGkjlHBO36QvMCTqMJD1u/3OXXqFP/xP/5HPvWpT/GpT32K3/u932NtbY3d3V0eZVkeWXn3BhKNWYJ3XFxdYpo//IXXaTZSSlcvvMwkwTtKy94xjzONGuOIGvMI3lESNYJ3jAreUcryPiFNmCV4x34se8dA8I7rb5+iFLxjmmajRkgTSs1GyiQXV5doNmoE7/h/2YPj2DjO+8D731txLrNKwGjH2I3ocqDhrXuuvL0WVsMYJHzwOshRTSP30EplVKOtZDRNYzk9MKfD4QAfQLuXP4ormBotneIKBIrf5ppo4ddB0f4To+elUXgXiX2+pofhW+ViSbzfFKGfB5TtRbqr+DdOXwxeDN559yWXlEyJkv18Ps16hWFR4LNTK6+9SRT4bCUKfDJRxScKfKLAZyvPvPwDoorPKCuvvcnKa2+Se2JuimFRUCYTBT5RxWcrzXqFZv0AmajiMyyq+OzEE3NTFF2+MiAKfIZFgc+1iAKfKCizU1HFJ3d6eoL2mXu5fOUqo7QfPUIU+Aw7NT3BZqLA5/T0BA/UDxBVfEY5/dEJNnN6eoJm/QCnpg/SrFcYFgU+UcXn8pUBUcUnFwVlcqenJ2ifOUJm5bU3adYrDIsCn2b9AFHgc/mNq2znqy//gKjis5ko8BkWBT5buXxlQO7cycM06xVyzXqFcycPc/nKVTJR4DMsCnya9QNkLl+5ShT4bKZZr5BZee1NclHFZytRUGYnosCnWa8w7FDgk/nqyz/gwS+/SmbltTfYShT4XL5ylUe+8X+x8tobnJ6e4NT0BMOa9QqZB+46wGYOBT5R4JOJKj7tM0d4oH6ArWz880/xXlRij83Pz/Mrv/IrdDodzp8/z/nz53nhhRf4z//5P/Nv/s2/4VbWrFeIKj4rr73BocBnM816hSfmprj0+Cynpyd4N5r1CqenJ8hcenyW09MTbOWJuSkuPT7LpcdnOT09weLRKYqiwCfTrFeIAp+tREGZ0x+d4NLjs0SBz1bOnTxMs36AKPAZ5Ym5KaKKz+UrV9lMFPhcvnKVKCiTOXfyMKenJyiKKmV2on3mCFHg0z5zL+dOHmYrUeCzeHSK0x+dYDPNeoVzJw/TrFd4t5r1CpnFo1O0Hz3CKCuvvckoUaVMLqqUySwenWLYuZOHOXfyMO1Hj7B4dIpR2meOcOnxWRaPTrGVZv0AzfoBMs16hWGLR6eIKj65KCizlWb9AJko8IkqZTazODdFpv3oEc6dPMxmDgU+l68MeLeiwCdz+Y2rRBWfUS6/cZVmvcK5k4c5PT3BqekJNhNVfM6dPMyp6Qm20j5zhGa9wlYOBT4rr71JUVTx2YlmvUKufeYImctXrhIFPu/WymtvElV8RokqPiuvvckDdx0gc3p6gijwKTo9PUEU+AyLAp9c+8wRdiIKypyenmDx6BRbORT4rLz2Jts5d/IeMudOHmbx6BSjrLz2JiuvvcEzL6+zmSjwyTwxN8VXX/4BK6+9SVTxiQKfYQ/UD5CLAp9hp6cnaD96hGb9AM16hUwU+ERBmc0M7vjnvBeV2GPGGP7tv/23DPvX//pfk+n1etzKmvUKl69cZSuLR6dYPDrFbooqPqNEQZlMFPhs59T0QXYiCnzOnTxMVCmzmdPTE7TPHCEXBT7DoqDMdpr1Cs36AXYiqvjkoorPVpr1Cttp1issHp1iK6enJzh38jDbeWJuima9wiinpyfIRIHPVqKgTCYKymwlCnxyUeAzyunpCaLAZyeiwGeUKCgTBWWiwCeq+GzmUOBz+Y2rZC5fGbAbosBnlMtvXCWq+OSioMxmoorPdr768g/YzuUrVymKAp/NREGZ09MTRIHPzRAFPpko8HlibophUcVnWFQpk4sqPlHgkzkU+GwlCnyGRYHPsCgos5uiwCfXrB+gWa8wLKr47ERU8RklCnyiwGenLl+5yuUrA7YSVcocCnx2qlmvsJUo8Dl38h5OTU+QadYrnDt5mPeTEreAq1evspmrV68yNjbGrexQ4JOJAp+b4dT0QU5NT7BbokqZnWrWK0SBz3aiSpnNnJo+yKnpCc6dvIco8BklqvhsJQp8/nHp47TPHOFWElV8DgU+2zl38jDNeoVRHqgfYFhU8Rn2xNwUUeBzszTrB1icm+LdigKfTBSUiQKfoqhS5t1anJvi3MnD7IZmvUIU+FyvB+oH2A1R4NOsV7gWi0en2EpUKbOVqOIzLKr4jNKsVxglqpTZTlTxGaVZP0CuWa+weHSKYYtzU5yanmCUZr1Cs36AQ4FPLgp8NnPp8Vma9QqjRIFPFPhs5/IbA3YiCnyGRYHPsCjwiQKfUaKKz3tViT328Y9/nF/5lV+h1+uRU1U+//nPU6vV2L9/P87/q1mvcHp6gpvlgfoBHrjrANciCnwW56YY1qxXiAKfKPAZ5dT0BA/cdYDbTRSU2S3NeoVMVPHJLM5NcWp6gmGLR6doP3qEmykKfHZDVPG5UaLAZ1hUKZOJgjKjRBWfzKHAJ3NqeoJmvcJmoorPVqLAJxMFPrshqvicmp4g80D9AKemJxgWBT6nPzrBKItHpxgWBT65KChzanqC7USVMqenJ4iCMrlLj88y7IH6AXYiqvhsp1mvUNSsVxgWBT5R4DNKFPi0zxzh9PQEuWa9wk5EgU9RFPhkmvUKa1eushNR4NOsH2CUKPDZDc16hfeqEnvsS1/6EkEQMD09zZEjR/joRz/KT//0T/PXf/3XPP/889zqosAnE1XK3CoW56Y4NT3Bds6dPEwU+IxyenqCZr3CtTo9PcEo7UeP0KxX2Mzp6Qma9Qq3m3MnD3N6eoLdEAU+mQfuOkAmCnyiwGczUeBzs0RBmUxU8WnWK5yanuDU9EHejajiM0oU+ESBz3YuX7nKTpw7eZhcVPHZTBT4RIFPJgp8NhMFPocCn81Egc+NEAU+mdPTE0SBz2ZOTU8wSlQpk4kCn82cO3mYZr1CJgp8thIFPudOHubcycOMEgU+mSjw2U4UlMkcCnw2cyjwuR6HAp9RHrjrAKemD3I9oorPpcdniSo+mctvXCUKfIad/ugERVFQZitRpczpj07gjFbiFvAXf/EX/NVf/RVf+cpX+JM/+RO+9a1v8d3vfpdyucytrlmv8MTcFFHgc6uIAp8o8NnO6ekJ9koU+DijRYHPrWrx6BTnTh4mCnya9QqjXH7jKlGlTFFUKdOsV8g06xU2E1XK7JVmvUKzXmGUqOJzenqCrVx6fJbdEgU+p6Yn2E770SNEgc8oUeBz7uRhRokCn5stCsrslWa9QrNe4UZaPDpFs16hWa+wODfF4twUp6Yn2EwU+CwenWI3PHDXAcob3+O9qMQtIgxD7r33Xo4cOUIURdxOFo9O4Ti77dLjszTrFW4VUVBmt0SBz+LRKR6oH+CBuw4wSrNe4dzJw4zSrFfIREGZrUSBT/vMvexEVPG5lTTrFU5PT7CdKPDZidPTE9wszXqFf1z6ODsRVXxGiQKfqOJzq7r8xlW2EwU+p6cniAKfKPDZiSjwuV7NeoWD3/0/eC8qsQcuXrzIxYsXyVy8eJGLFy9y8eJFLl68yMWLF7l48SIXL17k4sWLOM52Tk9P0D5zBOf2c3p6gma9wnaa9QqjRIHP6ekJttOsV8hFgc/1Oj09wbmT97ATUaXM7ej09ARRpczNcmr6IKemJxilWa/QPnOEW9GhwCfXfvQIzo1XYg987nOf47Of/SyZ3/zN3+TYsWMcO3aMY8eOcezYMY4dO8axY8d46KGHcBzn/SkKfE5PT3CriSo+mSjw2U6zfoDb1bmTh4kCn9yhwOdGatYrRIHPbouCMjfT5StXiQIf58YrsQeef/55/uqv/opMu91mdXWV1dVVVldXWV1dZXV1ldXVVeI4xnGcW0NU8dmpKPC5fGXAu3Xu5GF2Kqr4RBWfW0n7zBGiwMfZOw/UD9CsV7idnTt5mGa9gvP/VWKPHTt2jB/84AcM6/V63HPPPfzoRz/CcZy9d2p6gsW5KW5Vi0enWDw6xU5FlTKnpicYFgVlnL0XBT674fT0BFHgsxuiwGcvNOsVnP+/EnvgO9/5Dg8//DAPP/ww/+t//S/Onj3Lww8/zMMPP8zDDz/Mww8/zKlTp3jnnXcYGxvDcZy9FwU+UeBzrS5fucqtKAp8Tk9PcD2iis/7SVTxcUaLAp/LVwY4N0+JPfCxj32Mq1evsr6+TmZ9fZ319XXW19dZX19nfX2df/iHf+A//If/wL59+3Ac5/YWBT63i8W5KRaPTrGVU9MTnJqe4L0iCnyc3XH5jatEgY9zc5TYI8899xwvvPAC09PT/Nf/+l954YUXeOGFF3jhhRd44YUXeP7553nkkUdwHOf2tPLam3z15R9wraLAZy9Fgc8oUeATBT6O4+ytEnvsa1/7Gh/84AcREdbW1lhbW2NtbY3vf//7LC8v4zjO7adZr5B58bU3cW5dUeATVXyc63N6eoL2mSM4e6PEHvvmN7/J9PQ0n/jEJ5ibm2Nubo65uTk+9alP8fWvfx3HcW5P504e5lpEgY9z87XPHMHZmfajR2jWK2zl8pWrRBUf5+Yosce+9KUv8bnPfY7V1VVqtRrPP/88L7/8Mh/72Mf47d/+bRzHuT1Fgc/Ka28QBT6Oc62iis+tJgp8thJVymSa9QrOzVFij/V6PX7913+dffv2cfDgQV566SXGx8f56le/yu///u/jOM77xxNzUzi3nnMn7+H09ATO9g4FPs7NUWKPeZ5HqVQi8xu/8RucP3+ezL59+/jgBz9Ir9fDcZzbT1Qpc/nKVa7F4tEpnFtPFPjcTKenJzh38h5uJ1HgEwU+zs1TYo/dddddPPnkk/T7fX7mZ36Gixcv8s4777C2tsYbb7zBBz7wARzHcZz3lyjwud1cvnKVKPBxbo4Se+zP/uzPeOmll/i93/s9Dh06RLVa5Z577mFubo7p6Wk+8IEP4DjO7ScKfDJRxcdx3g+a9QM4N0+JPfbmm2/yyiuv8Lu/+7tkXnjhBZ577jn+8i//kq997Ws4jnN7Wzw6heO8H7TPHKFZr+DcHCX22C/8wi+wvLxMUaPR4Cd/8idxHOf29sTcFI7jODdCiT2WpimHDh3CcZz3nsWjUzTrFRzHcXZbiT329NNP8+/+3b/jv/yX/8LFixdZW1tjbW2NtbU11tbWcBzHcRzHGVZij/37f//vyXzpS1/ik5/8JHNzc8zNzTE3N8cnP/lJHMdxHMdxhpXYYy+++CIXLlzgwoULXLhwgQsXLnDhwgUuXLjA6uoqjuM4juM4w0rcAn784x/zh3/4h/zCL/wC/+N//A8uXLjAq6++iuM4juM4zmZK7LF+v8/hw4c5f/48r732Gpm///u/51d/9VdZXl7GcRzHcRxnWIk99su//Mt85jOf4aWXXuKnfuqnyHz84x/nK1/5Cl/+8pdxHMdxHMcZVmKPra+v81u/9VsMu//++9m3bx+9Xg/HcRzHcZyiEntsbGyMwWDAsB//+Me8/fbbjI2N4TiO4ziOU1Rijx07doyHHnqIK1eukOv3+/zar/0aP/ETP8H+/ftxHMdxHMcpKrHHnnjiCRqNBjMzM/zd3/0djzzyCPfeey+rq6t861vfwnEcx3EcZ1iJW8AzzzzDysoKzz77LM888wzPP/88f/M3f4PneTiO4ziO4wwrsQfW1tZYW1tjbW2NtbU11tbWePvttxkfH+fAgQNk1tbWWFtbw3Ecx3EcZ1iJPfCbv/mbzM3NMTc3x9zcHHNzc8zNzTE3N8fc3Bxzc3PMzc3xyU9+EsdxHMdxnGEl9sC3vvUtVldXWV1dZXV1ldXVVVZXV1ldXWV1dZXV1VVWV1f5n//zf+I4juM4jjOsxB7Yt28f+/btY9++fezbt49/8k/+CU8//TQPPfQQf/u3f8v3v/99vvvd77Jv3z4cx3Ecx3GGldhj/X6fw4cPc/78eV577TUyf//3f8+v/uqvsry8jOM4juM4zrASe+yXf/mX+cxnPsNLL73ET/3UT5H5+Mc/zle+8hW+/OUv4ziO4ziOM6zEHltfX+e3fuu3GHb//fezb98+er0ejuM4juM4RSX22NjYGIPBgGE//vGPefvttxkbG8NxHMdxHKeoxB47duwYDz30EFeuXCHX7/f5tV/7NX7iJ36C/fv34ziO4ziOU1Rijz3xxBM0Gg1mZmb4u7/7Ox555BHuvfdeVldX+da3voXjOI7jOM6wEreAZ555hpWVFZ599lmeeeYZnn/+ef7mb/4Gz/NwHMdxHMcZVuIW8cEPfpB/8S/+BT/7sz/LP/7jP+I4juM4jrOVEnvsnXfe4ciRI5w6dYrcqVOnaDQaXL58GcdxHMdxnGEl9tiJEye49957+dM//VNyL774Io899hgnTpzAcRzHcRxnWIk9dunSJZaWlvjQhz5E0ZkzZ/jRj37ED3/4Q3bblStXeOWVV3jllVd45ZVXeOWVV+j1ejiO4ziOc3soscfGxsbo9/ts5u2336ZUKrHbvvnNb3Lq1Ck++9nP8tnPfpbPfvaz/O3f/i2O4ziO49weSuyxn//5n+cXf/EXsdaS++EPf8iZM2e488472b9/P7stjmMef/xxXn31VV599VVeffVV7r//fhzHcRzHuT2U2GNf/OIXueuuu7j//vtpNBo0Gg1+7ud+ju985zu0221uhNXVVer1OleuXEFV2Ym7776bu+++m7vvvps/FeafUwAAIABJREFU+qM/wnEcx3FuRX/0R3/E3Xffzd13383dd9/Ne1WJW8A3vvENLly4wJ//+Z/z3HPP8eKLL/LKK69wI7zzzjv87//9v/lP/+k/cezYMX72Z3+W//gf/yPbuXDhAhcuXODChQv8zu/8Do7jOI5zK/qd3/kdLly4wIULF7hw4QLvVSX22DvvvMORI0f4pV/6Je666y7uvvtuPv3pT9NoNLh8+TK77fXXX+cTn/gEf/Inf0Kn06HdbvPXf/3XfP3rX8dxHMdxnNtDiT124sQJ7r33Xv70T/+U3Isvvshjjz3GiRMneLe++MUvcuTIEY4cOcK//Jf/kjvvvJM//MM/5M477yTzkY98hH/1r/4V//2//3ccx3Ecx7k9lNhjly5dYmlpiQ996EMUnTlzhh/96Ef88Ic/5N14+OGHWV5eZnl5maWlJdbW1nj22Wcpevvtt9m3bx+O4ziO49weSuyxsbEx+v0+m3n77bcplUq8G//sn/0zZmdnmZ2d5WMf+xhXr15lcXGR73//+2Ref/11/tt/+2889NBDOI7jOI5zeyixx37+53+eX/zFX8RaS+6HP/whZ86c4c4772T//v3sprvvvpvHH3+c+fl5Tp06xSc/+Uk+85nPcP/99+M4juM4zu2hxB774he/yF133cX9999Po9Gg0Wjwcz/3c3znO9+h3W5zIzz88MO8+uqrPPPMM7z66qs88sgjOI7jOI5z+yhxC/jGN75Bt9vlz//8z3nuuedYWVnhlVdewXEcx3EcZzMlbgG9Xo9/+Id/wPM8fN/n7bff5vvf/z7Ly8s4juM4juMMK7HHvvnNbzI9Pc0nPvEJ5ubmmJubY25ujk996lN8/etfx3Ecx3EcZ1iJPfalL32Jz33uc6yurlKr1Xj++ed5+eWX+djHPsZv//Zv4ziO4ziOM6zEHuv1evz6r/86+/bt4+DBg7z00kuMj4/z1a9+ld///d/HcRzHcRxnWIk95nkepVKJzG/8xm9w/vx5Mvv27eODH/wgvV4Px3Ecx3GcohJ77K677uLJJ5+k3+/zMz/zM1y8eJF33nmHtbU13njjDT7wgQ/gOI7jOI5TVGKP/dmf/RkvvfQSv/d7v8ehQ4eoVqvcc889zM3NMT09zQc+8AEcx3Ecx3GKSuyxUqnEK6+8wu/+7u+SeeGFF3juuef4y7/8S772ta/hOI7jOI4zrMQtqNFo8JM/+ZM4juM4juNspoTjOI7jOM5tpoTjOI7jOM5tpoTjOI7jOM5tpoTjOI7jOM5tpoTjOI7jOM5tpoTjOI7jOM5tpoTjOI7j3OLUCL12C8fJlXAcx3GcG0SNsBvUChutJRwnV8JxHMdxbpBLZ+5DjeA4u62E4zg3TT/uoEZwdlc/7tCPOzi3FjWC49woJRzHuWl67RaDuIuzM2qE9eUFttNrtxjEXd7P1Aj9uMPtqB936LVbFPXaLdQIzub6cQc1wvtZCce5zagReu0Wtyu1ws0ki8dRI9yueisttqNWcCBZPMF2eu0Wu6HXbtGPO+yGQdylH3co2mgtoVYoUivshn7cQY1wO+u1W/RWWryflXCc28wg7vLWynluV2qE7fTjDttRI/TjDtsZxF2c9z61wk6sP73AblAr9NotbjUbrSXUCKP02i16Ky2c21sJx7kNpSbhvez15S/QjzuMMoi79NotdkKtcDtTI9xK+nGHftxhFDWCGuFWokbIqBFGkcXjqBFGUSPslFphO2qFIrVCUWoSdmKjtcT1UCPcCtQI68sL7IQa4f2shOO8S/24w6VH7+NWokZYX17gZlEjrC8vsFvUCttRK6gVnP+HGmGUftxhfXmB3dBrtxjEXbZz6cx97IaN1hIbrSVultQkqBXey9QIRcniCdQIRb12CzXCblEjqBG201tpoUbYDZcevY/aP/0x70UlHOcW0o877JbeSovd0I87qBG201tpsR21wk6lJuFmUSNstJa41agVdktqEtQK10qN0I877CU1ws2kVridqRWulVph2PrTC+ym9acXGMRddoNaYTtqBLXCe1UJ55agRrgV9eMO10ONMEwWj6NGGKXXbrHRWuJWMoi7bLSW2Ak1wo2gRui1W1yv1CSMolbotVs40I87DEsWT3Ct1ArvdWqFUdQKN1I/7qBGGEWN0I87FKkVdkqtsJvUCs7uKOHsOTXCpTP3sVs2Wkv04w6j9OMOsnic7by+/AX6cYdRUpMwrLfSYn15gaLUJKgVdoNa4f1mo7XEsNQk3ChqBDVC0UZrCTXC7So1CdtJFk/QjzuMolZQI+yEGmE3qBF2Qo2QUyOoEa5HahJuFjXCZlKTMMpGa4lB3GUzagQ1Qm+lxUZriVuRGuF7J+7EuT4lnBtKjXCz9dotUpOwndQkFPXaLfpxh+uhVtiOWuFmUyPcatQKRf24gxphFLXCjaZWKNpoLdFbaVHUjzsM4i67QY3QjzvcKOvLC1yv1CRsRo2w29aXF+jHHUZRK1yPjdYSvZUWey01CTulRrgWaoXN9FZabLSWuBZqhb2mVthtH/H+kfeiEs4Ntf70Ar12i51QI4zSa7dYX17gRunHHQZxl2GpSVAjXCu1wrDUJIyiVhgmi8dRI+TUCttRK1yL9eUF+nGHzagR1ArXSo2gRtjORmuJQdzleqgVRlEj7KbUJOyW3kqLjdYSRevLC/TaLYapFXJqhH7cYRQ1Qm+lRZFa4d1af3qBXrvFblIrpCZBjXAtNlpLqBH6cYd+3OFaqBWuhRrhZlAjXDpzH2qEragRdqIfdxhFrbAX1AjD1AjD1Ao7oUa49Oh9vB+VcHbNRmuJftyhKDUJ21Er7IRaQa2wG1KTsBNqhUxvpcX68gKbUStci167xUZriZ1QIwziLtuRxeP04w7XQ40wiLv02i12U2+lxUZrie2kJmEn1ArXS60wSmoSbgVqhe30VlpstJYYplYYpkYYpR93UCOMolbIqBGuVz/u0I87bCY1CZlB3EUWjzOKGqHXbpHZaC2RGcRdeu0WN4IsHmejtYRaQRaPMyw1CddKjbCZ1CQM4i4ZtcL1UCNsJjUJ1ys1Cf24Qz/uMEqv3UKNMEpqEm4EtUJGjfB+UsLZNf24Q2oSNqNGUCO8W6lJ2I5aYaf6cYd+3CGjVlAjbEaNUKRG+N6JO7keagU1wlbUCNcqNQmpSRhFjaBG2IxaYTeoFYrUCjuhVsipETJqhVxqEnZifXmBftxhN6gVNqNW+N6JO9kNqUkYplZQI1yL1CRcj43WEoO4S06NcCNstJYYxF22olZQK2xnEHdZf3qBzaQmYStqBDXC9VAjpCZhlH7cYad6Ky3WlxfYjFphWD/u0Gu3KFpfXqAfd9hKahIGcRe1QlE/7lDUjzusLy+wGTVCRq3w+vIX6LVbjLLRWmIQd9kJNUJGrbATagQ1wjC1gloho0a4dOY+1AjvFyWcXZOahK1cOnMfvZUWu2GjtcT68gKjqBVy/bjD+vICw9QKg7hLr91iO2qFTD/usNFaIqdG2IwaYSdk8Ti9dothsnicftwhp1a4HmqF3EZrid5Ki6L15QUGcZeMWmGnZPE4/bhDr92i125RpEbYTj/u8L0Td7Lb1Aq9dothaoSdWl9eoB93yKgRhvXjDhlZPM6Nolb43ok7yQ3iLrJ4nFxqEjJqhGuhVtgJtcJW1AqbkcXjqBGGqRWuhRpBjTBMraBWKFIrZNQKObWCLB5HjaBWUCOoFdafXqBIjVCkRpDF4+TUCoO4Sy41CZtRI7y+/AXUCJcevQ81wnbUCmqEjdYS2xnEXfpxh5xaYRB3SU1CRo2gRpDF4+TUCkVqhUyyeAI1Qi41Cb2VFmqEragR1AqjqBHUCmqFnVArfO/EnWyn127Ra7forbTYaC0xiloh12u36LVbqBXey0o4u0qtMEytkFEj7FQ/7tBrtyhSI2T6cQe1wrXorbTYilphJ9QKg7iLGkGtkFEr5NQI/bjDdtQIRWqFYYO4S6/d4t0axF2+d+JO1AgZNUKRWqEfd9hOahLUCJcevQ81QmoSUpPQjzu8tXKeXGoSksUT9Not1AiDuMuw1CT02i2K1AjXqx936LVbZFKToFbYzPryAr12i1HUCGqF1CRsZxB32YwaoR932Am1QlFqEjKDuEsmNQkZNcIg7qJGKLp05j7UCDk1giwe50ZJTUKRGiE3iLuoFfpxh/XlBVKTMKwfd1AjDFMjqBFSkzCIu6w/vcBm1AgZNUJOjZCahFxqEgZxl0HcpSg1CbnUJKw/vUA/7pBTKwziLjk1QkatsB21gloho1ZQI+zERmsJNcKw1CQUqRWK1ArDBnGXnBphK2qFTGoS1ApFqUnYaC3Ra7e4HmqE7agVcqlJyG20lthoLZFLTcJGa4l+3GEzqUnIpCahaBB3USv04w7vdSWcW4osHkeNMIi7vLVyHjXCZlKTsJ2N1hLrywsU9dot1Ag5NUJqEraTmoScWiE1CZnUJOTUCsniCfpxh1yv3UIWj5NTK6gV1ArbGcRditaXF9hoLZFTI2xHjZAZxF3UCoO4y/dO3EkuNQlFagQ1wmbUCmoFtYJaYf3pBXorLbxqiFoho1ZQK6gVtqJWGMRdMmqEUdQI/bhDTo2QUyNkUpOw/vQCaoTMIO6iRhimVujHHUYZxF0GcRe1wlZSk+BVQ0ZJFk+gRrj06H2sLy/wbqkVMoO4S0atoEbIqBUyg7jLIO4yiLvkBnGXXrtFr91iM6lJUCv04w69doudUCNk1ApqhUuP3kdRahLUCkWpScj02i16Ky2GqRXUCrlB3GV9eQFZPI4aIadWyKgVitQKObXCsNQkqBWKBnGXQdxlmBoho1YYRa0wbKw2SWb96QVk8Tg5tUKv3WJ9eQE1QpFaIadGKFIrqBGKUpOQS01CRq2wHTXCVtQKuX7coR93GKZW2Ck1wiipSShSK/TaLdQIObWCWiGjRlArqBG2kpqErdT+6Y95Lyrh7Do1Qj/ukFEr9OMOw9QIvXYLNUJOrTCIu6gVMoO4S2+lxWbUCttRIwziLjk1wvrTCwziLmqFnFphlH7cQa1QpFbYSmoSMqlJUCukJkGNsJnUJKgRitQKm1ErqBFyg7hLr90io1bYjFqhSK3gVUMuPXofud5Ki3JjhkxvpcVGa4nM+vICvXaLjFphK4O4S64fd9gprxpSpEboxx1yaoSN1hKvL3+BUfpxh2GDuMtW1Ai9douMGqGoH3cYRa2gVig3ZvCqIWqEYWqFzCDuolYYxF36cYf15QUuPXofw9QIw9QImX7cITOIu+zE+tMLZNQKGbWCWqEfd9jORmuJnVAr5FKToFbIpSZBrVCUmoSMGiHTa7foxx167RZqhNwg7pJRK2R6Ky0GcRe1wihqhSI1wjC1QkatkFEr5HrtFr12i167RUatMEo/7lCkVsgN4i6vL3+B1CQM68cdcqlJUCtkUpOQUSsM67VbqBVSk6BWyPTjDkVqhV67xTCvGpKahJxaIZOahFw/7rCdQdzFq4aMolYoN2borbRQI2y0lui1W+xUahLUCkVqhMwg7pIZxF0GcZdh/bhD0Vsr51EjvB+UcN4VNcKw3kqLZPEEw9QK/bhDr91CrbD+9AJqhdxGa4lMahLUCJl+3KFIrZCahGFqBDVCTo2gVsikJiHj1UKGqRVyqUlQK6gR+nGHYalJyKQmQY0wLDUJw9QIagW1QkaNMIi7DFMj5FKT4FVD1Aq51CQMe2vlPOtPL6BGyMnicdaXF8ilJsGrhqgVcuXGDBk1Qu6O+bPkBnGXTG+lRdFGa4lMahJyXjVErZCahMwg7pJRI+T6cYdLj97HVtQKmUHcJVk8QU6t0FtpUaRWuPTofagRcr2VFhm1Qu6tlfMUDeIug7hLZhB3eWvlPJlLZ+5DjTBMjZBRK2zGq4WUGzNstJbYaC2xGbVCuTGDWmEQd+mttFArrC8vkFEjZNQKaoSdUCvk1ApbSU1Crh93KFIjrC8v0I875AZxF7VCTo3Qa7dQIxSpFbajRkhNQk6tUKRW6LVbrD+9QEatMIi7bCU1CZnUJKQmIZOahExqEnJqhYxaYSupScioEXJvrZxn/ekFeistMqlJ2IoaIVk8wUZriYxaITUJXjUkNQkZtYJaITUJRYO4S06tkFMrqBUyaoUitUJqEtQKWxnEXXorLbaiRsikJqFIrTCsH3cYplYYq02S6bVbqBGGDeIumfHmPIO4S0atsJnUJOTUChm1wma8akhGrZC79Oh9FKkVigZxF7WCWuG9roRz3dQIG60lvnfiTjJqBTVCP+6wldQkbLSWyKUmITUJRWO1SXKDuIssHqdorDbJsI3WEhutJYpSk6BWyPXaLTJqhWFqhFxvpUWyeAI1Qkat4FVDcmqF3kqLcmMGtUKuH3cYplYoUitkUpOgRlArqBUunbmPXrtFRq2wmdQkFA3iLhm1QkatMEytMFabpGj8wXnGapPkvGqIVw1JTUJGrdCPO2TUCrnUJJQbM+S8aki5MYNXDSnyqiFFvXYLtYIaIVduzDBMrVCUmoSMWiGXmgS1glohU27MMOzgY08xiLvI4nGGqRUyqUnIDeIuaoWMWmG8OU8mNQmjeLWQ3kqLftxhM2qE/Y1ZMmqEXG+lRT/ukHt9+QuoFXJqBLVCzquGZMqNGTJqhEyv3SKTmoTt9FZa5AZxl95Ki6J+3CGjVsgM4i7rTy+w0VoiN4i7ZFKTkNtoLbGdXruFWqFoEHfJDOIuOa8aolbIedWQYWqFcmOGnVAr5NQIGbVCkRphK4O4i1cNSU3CMDVC7q2V82xGrZDrxx3UCkW9douMGqHIq4aoFXJqhc2oFYapFTJqhbHaJJtRK+RSkzAsNQlqhSKvGpKahLdWzjOIuwzrtVt41RCvFvLWynnUCptRK2TUChk1QpFaIadWGKtNUtSPO6gV1AiZ1CRkvGqIWiGXmoT3gxLOdfNqIb2VFhk1wjA1QiY1CeXGDKlJyA3iLhm1Qi41CZlB3CXjVUOmvvxtUpNQ9OHmp/GqIf24Q06tkFEj5NQK20lNQrkxwyDukklNghphO141ZH9jliK1glcNUSvkvGpIJjUJmUHcxauGZNQKXjUkNQkZtUJurDZJRq1wLQZxl53wqiGDuMtWUpOQUyt41RC1wrCDn3+KsdokaoWtDOIuudQkeNUQrxYyVptErZBTIxSpFbxqSFE/7pBJTULmw81PM96cZ7w5T2oS1ArlxgyZ1CTk1ApeNSQ1CWoFtcL3TtzJsEHcRa2QS02CGuHSo/ehRsiVGzPslFcNGcRdilKTkFMr9NottlJuzDD15W+zvzFL0SDuklEr5CaffBavGpLbaC0xiLtk1AiZt1bO41VDUpOQUSOkJqHcmCE1CRm1QkatkFt/eoFcahJSk6BW8KohaoScWkGtkFMr5NQK5cYMaoWi8eY8Y7VJisqNGbxqiFoho1bYidQkFPVWWqgVigZxl2HjzXm8aohaITdWmySjVlArbGYQdxmrTVLkVUMyqUnIDOIuGbVCrrfSwquGXKtB3MWrhgzzqiFFXjVErZDrxx2GqRXKjRlSk5BTKwzb35gl1487rC8v0Gu3yPTjDpnxB+cpN2YYxF1SkzBKP+6Q86oh/biDVw1JTUJqEjKpSSjyqiFqBa8aMoi7DFMjlBszjDfnUSsUfcT7Me9FJZxdMYi75FKT4FVD1AqbUStkvGqIGqHIq4aoETJ3zJ/Fq4UUlRszlBszZHrtFr12i37cITUJaoVhXjVErZDpxx28aogaYStqhd5Ki6JB3GX8wXnUCptRI2QGcRe1QkatoFYYxF0yaoVcuTGDWiE1CeXGDDk1wlb6cYeMWmEr/bhDRq0wilcN8WohaoXM5JPPMkytkFEjZMqNGcab83jVkNxHPv8HFJUbM3jVkGHjD87jVUNyk08+yx3zZylSKwziLhm1glcNyZQbM2TUChm1QkatkDv4+aco8mohXjVEraBW8Kohm/GqIeXGDGqFov2NWdQKud5KC7WCWsGrhmS8ash2BnEXrxpSdPCxp7hj/ixqhaLeSoui1CQUebUQrxqiRsipFcqNGXKTTz6LVw3JedWQTLkxQ1FqEop6Ky3GapMMKzdmGEWtsBWvGlJUbsyQu2P+LMPumD/Lwceeomh/Y5bxB+dRI+S8akiRWiE1CV41JKdWGPbh5qcZb86jRsioFbxqSNH4g/OUGzMUedWQIrWCVw1RK+S8asj+xixqhdxYbRKvGqJWyE0++SzDxmqTbMerhhSpFYYdfOwpMqlJyAziLl4tJOdVQ3orLXKpSdjMIO6SSU3CVnorLXorLfpxh8wg7jL+4Dz7G7Psb8ziVUOuhVphEHcZplbwqiG5cmOG1CSoFdQKRWO1SYYN4i7vdSWcd8WrhmTUCjm1QiY1CRm1wv7GLGqFUdQK5cYMGbXCWG2SXD/ukPlw89N4tZDxB+fJvLVyntQkqBVSk5DrrbTwqiHDyo0Z1Ao5tYJXDSk6+NhTZNQKWxmrTbKZcmOGYeXGDBmvGpLZ35ilqNyYIaNWSE1CxquGDFMreNWQonJjhtQkDEtNQsarhny4+WnUCJnJJ5/Fq4UM82ohaoXcRmsJrxqiVsiMPzjPwc8/hVcLUStk9jdmGTb1x9/mjvmzFHnVkLHaJGqFYalJyKkVyo0ZclNf/jYHP/8UuXJjhkHcJdOPO+xEahJy5cYMReXGDPsbswwrN2ZITUJGrdCPO2RSk6BWKDdm8GohW0lNwnbUCJk75s8y+eSzlBsz9OMOaoWMWsGrhmzHq4bkvGqIVwsZq02iVhirTeJVQ1KTkFErZNQKwz7c/DReNaQfdyg3ZhhvznPH/FlSk5ArN2aY+vK3KfKqIRm1QpFaoR938Kohmf2NWdQKufHmPEVeLcSrhQzzqiE5rxqS8aohaoXMRmuJzXjVkKKx2iQ5rxqyE141ZH9jlqKN1hJjtUmKxh+cJ6NGyHjVkPDJ/5NcuTGDVw3xqiGpSRglNQmb8aohaoSMVw0Zq01SVG7MMFabJOdVQ4rKjRk241VDitQKGbVC0VhtkmuhVlAjbGasNskg7uJVQ9QKuXJjhrHaJEVeLUStkJoErxaiVihSK6QmoeiO+bPcMX+W94MSzrvykc//AePNeXJqhUy5MYNaocirhvTjDhk1Qrkxg1phO+XGDL12C7VCrtyYQa2QmoS3Vs7jVUPUCkVjtUmKeistvFpIahK2Mt6cZ/zBebxqSGoScl41xKuG9OMOGa8a4lVD1Ai5cmMGrxpSpFbwqiE5rxoyVpsko1bI7G/Mcsf8WVKTkPNqIUWDuItXDRnEXdQIGa8a4lVDcoO4yx3zZ0lNQsarhtwxf5bNeNUQNcJWvGrIHfNn2QmvGlI0VptErVDkVUNSk1DkVUNyqUnwqiGZQdwl49VCcmqE/Y1ZMuPNeVKTUOTVQtQKuXJjhiKvGrK/MYtaQY2wFa8a4lVDigZxF68akktNQsarhmRSk7AZtcJYbZKx2iRqhcxYbRKvGuLVQjJeNWR/Y5b9jVkGcZeicmMGtcJm7pg/y9SXv83+xixqBLXCZsZqk4zVJvGqIUVjtUmKyo0Zig5+/im8ash2xmqTZFKTkOnHHVKTMMyrhhTdMX+W8eY8w7xqiBohM1abZNhYbZJhY7VJcuPNeQ4+9hTlxgxFXjUkN1abZPLJZxl/cB61wjA1glcNmXzyWcqNGYpSk7C/MUvujvmzjDfnKRqrTVLkVUOm/vjbeLUQtULR/sYsaoWcWmGsNknGq4aMN+cZq01SNFabZH9jliKvFuJVQ9QKGbXCeHOenFcLyZQbM6gRMl41ZKw2yTCvGpJRK+T2N2ZRK+S8aohaIaNG8KohubHaJMN67Rb9uEPGq4aUGzOM1SbJlBszZLxaSGoS1AqjlBszFKkV9jdmUStkvFrIHfNnyaQmIfPTH3qH96ISzruyvzHL/sYsaoTMIO7iVUM2M1abRK2QGcRd9jdmSU1CzquGZNQKRfsbs+TGapMMS03C+IPzeNUQtYJXDcmN1SZRI2S8aki5MYNaocirhagVMnfMnyVTbsygVsiNPzhP0f7GLEV3zJ/l4GNPsb8xixphK1N//G32N2bxqiFqBK8WMv7gPOPNeYq8aohXDUlNQkatUG7MkBtvzrMZrxqSKzdmGH9wnlxqErxqyCj9uMMd82eZfPJZxmqT7JQaYStjtUm8WohaYStqhYxXDRnmVUOKvFqIWmGU8Qfn8aohaoWx2iQf+fwfUG7M4FVDirxqiBohM96cZ/zBeYq8aohXDSk3ZsiN1SYZ1o879NotdkqNUORVQ3rtFpneSguvGjKKVw3xaiFFXi0k41VD1AiZDzc/zYebn2YraoVR1AqpSch41RCvFqJWyIw358mM1SbJ9VZaqBW8akhmrDZJZqw2SZFXC8moFTZTbszgVUMyaoXUJHzk83/AHfNnGeZVQzJqhMz4g/Psb8yiRkhNwmb2N2YpN2Yo8qohXi0k59VCMmqF3PiD85QbM+TKjRm8WohXDVEr7NTBx55iK141RI0wVptkM/sbs3jVEDXCMDXCKF41JPd/swf/MXLdZ6H/3zqcR7k7N9pa3nw+MeU8oFDTJzBFlRIlaEcheKGRQGr/KLqaJBWqKVQRblz4Y1Xpxq1iDKW9wS6xiZUSKERWUasWhwoZIRm55DoxjoKMpZa46Imjyu1ZKjIHbCuoDfTsrr7foRpp7mS9P2fXO7uf1+v2/U8y0Z5GglJXJRKUQWPNSXY9epRBt+9/kjcvvURXXZXksWBQXZX8+5c/w/cuneffv/wZZjszdE20p5loT9NotpjtzCBBuREJSlddlUhQuiQodadkKRKUuirp+un/Oce/f/kzbDUZyVDUVYkERYKyXHksqKuSfhPtaWY7M/TLY0FdlfSToMx2Zuiqq5Kx5iQLkaC8eeklxve0uX3/kzSaLfpJUHrqqkSi0k+CMtGept/4njYtdNVzAAAgAElEQVRjzUn6SVAkKv3G97QZa04iUak7JUupq5KuseYkY81Jesaak+x69CgT7WnqqqSr0WxRHDrJoPGpNnVV0i+PBXVVspC6KumRoHRJUCQqEpTZzgz9JCh1p2Q56qpkrDnJjUhU6qqkJ48FS5GgSFAWUndKehrNFmPNSepOSVej2UKi0k+ikseCHonKRHsaiUpdlXRNtKfJY0G/RrPFQv79y59hkARlUB4L6qqk31hzkn7jU22GYXyqzfhUm56x5iRdjWaLuirpkqBIVAZJVHokKONTbfpNtKfRQ88hQamrkh4JSk+j2WLXo0eRoMx2ZugnUVlIXZW8bc+DSFTyWNAjQWk0W/RIUMaakzSaLfJYUFclEpWFSFQkKnVV0iNBme3MMNuZ4UYkKishQenJY0HdKRn05qWX6Hrnye8w1pyknwRFolJXJV0SFQlKz/ieNmPNSfoVh07Sb6w5yVhzEolKjwRlkASl0WzRb3xPmzwWSFBmOzN0jU+1WQkJStebl17i37/8GWYO/i/qqqRHotJotugnQVlIHgt6xpqTTLSnaTRb9JOgdM12ZliIBKVLgrLVZCTrQqJSd0p6JCj96qqk0WzRVVcl43va3PHZl1mIBGW2M8NS8lgw25mhp9Fs0SNRaTRbdElQ6k5JVx4LbqTulHRNtKfp12i2kKh01VXJjYxPtdFDz7EcEpWeXY8eRaLSb3yqjUSln0Slq65KuiQoXRKUuipZTB4L6qqkS6KyXHksqKsSCcqNSFBmOzN0TbSnaTRbdNWdkn4SlO9dOk/XRHsaPfQcXXWnpF8eC+qqpOuOz77M+FSbQRKU1ZjtzFBXJTcy1pxEDz2HRKWuSrom2tMMevPSS9RVySCJymqMNScZlMeCuipZCwmKBKXfHZ99maWMNSdpNFssxx2ffZl+41NtJCp1VbKUPBbMdmZYSt0p6dJDzzE+1WYxdzz9MsMgQRlrTiJBme3McCN66DluRILSlceCQRPtaYpDJ+k30Z5m1/6j9EhUGs0W/SQog/TQc3RJUOqqpKs4dJJGs8VixqfaSFBuZLYzQ89Yc5I3nv8yi5GgSFCWMtGeZnxPmzwW1J0SCcpYc5JGs0W/ifY0PXVV0iNBuZGx5iS/7v+TseYkW01GMnR5LFiIBGW2M0PXWHOSLglK3SmRqKxGXZV0SVD6SVAkKDdSVyU9EpS6U9JPolJXJXks6MljwWxnhp48FgzKY0FdlQyqq5KVymNBXZX0k6DUVckgCcrt+59kIRKUNy+9xFLyWLAcs50ZBklUFiNBuREJykR7mn55LFiMBOXNSy+xUnksqKuS8T1tJtrTjDUnme3MsBQJymLqTskgCcqNzHZmqKuSPBZ0SVTqqqRHgjJIgjLbmaGuSnryWFBXJYPqqmQhjWaL4tBJJCh1p6SfRKWuSvpJUOqqZCl1p2SjSFTqqiSPBWtVVyVdE+1pJCoSlLoqWcj4VJtGs8UwSVBmOzN0jTUnkaisl0azxVIazRa79h9lOSQoSxmfajPRnkaCUlclEpSFjE+1kaj0jDUn0UPP0U+i0pXHgroq6ZKgLEfn+xkSla0mIxmqPBZ0SVDqqmQYJCp1VXIjE+1pJCoSlLoq6SoOnWSsOclSGs0WG2G2M0MeC3ryWFBXJUupOyWLkajUnZKuRrPFQiQqgyQos50ZBs12ZuhXVyVLkai88fyXkaD01FXJUvJYMNuZoZ9Epa5KlpLHgrWQqPSrOyUSlMV879J5JCiD6qrkjf/7ZbrqTkldlSxk16NHaTRbSFCWa7Yzg0SlS6JSVyVdeSy4EYnKYiQqwyRR6ZGgdOWxoK5K1kqCspDZzgxrJVHpGd/TZjF1p2SlJCh1VTIMY81J6qqkJ48FdVXST6JSVyWr8b1L58ljwWIkKHVV0tNotvjepfMsRoIyPtVmrDnJctVViUSlR4KSLCwjGSoJSj8JSlceC7rqqqRLgrIQiUpdlcx2ZpCgLMdEe5qeulOSxwKJikTlRmY7M/TksaCuSoZBgjLbmaGfBGUhs50ZJCg3IkGpqxIJymLqqqRfHgvqTslaSVSWQ4JSVyU9EpVBeSyoq5L1kseCuioZVFclg2Y7M0hQuiQqdVWykLoqkah05bFgtjPDjYw1JxlrTjJz8H/xvUvnGd/TZqI9Tb+x5iRdEpW6KllPs50ZBs12ZshjwXqoqxIJylhzkp7ZzgyDZjsz9EhQvnfpPEuZ7czQT4IySIIy25lhGGY7M+SxoEeCspQ8Fsx2ZhgGCcpsZ4Z+dVUyDHksqKuSfhKVhdRVyUIkKBKUPBbUVcli8liwmLoqkaisVh4L8liwXWUkQ7Vr/1H00HN0zXZmyGPBRHuazSSPBV0T7Wkm2tN0zXZm6CdBme3MsBgJymxnhn4Slboq2SgSlGGRoNRVyWIkKHVVspEkKG9eegkJSr+6KsljwY1IVAZJUOqqZNgkKBKUuiqZ7cwgURmfarMaux49ikRltSQoS8ljQV2V9JOgzHZm6JfHgu9dOs9iJCiznRm6du0/Sj8JyiAJSj8JymLqqmSl8lhQVyX9JCiznRnWSqJSVyXDIFGpq5L1NNuZIY8FNzLWnGS2M0O/PBbcSF2V9Mx2ZhgmCcpsZ4bl2vXoUSba0ywkjwV1p2Qry0iGYrYzw41MtKdpNFv0SFAazRZdeSyoq5KlSFBmOzP0q6uSfhKVuipZLglKV6PZoq5KtpK6KrmZJCiznRmWUlclW4FEZaI9zVhzkrUan2qzVnVVIkHpyWNBXZWslARltjPDYvJYMCwSlLoq6ZGoDFNdlUhQeiQodVWylLoqkaDcTLOdGSQoXRKU2c4MKyVBme3M0G+2M8NKFIdOIlGRoHTNdmaQoPRIVDaKRKXRbNFVVyUSlO0kIxmaPBb05LGgrkoWcvv+JxmfarMWEpUuCcpGqquSQXVVkseCfrOdGSQoK1VXJRKVLonKbGeGlZKgzHZmkKj0SFDqqmQ58ljQb7YzgwRl2CQoXXks6JGgzHZmWCkJypuXXkKCshp1VbJWEhUJSl2VLESislYSlNnODD0SlNnODKsx25lhUF2VSFB6JCrLUVcleSzYLCQos50ZhiGPBbOdGfpJUN689BISlMXMdmaQqAybRKWuSuqqRKLSM9uZoV8eC+qqZDXqTslCJCg9dVWyGAlKT90pGQYJymxnhgQykptutjPDoLoq2azyWNAlUamrko0225khjwVdeSyY7cywUhKVuipZjroqWY26KhlUVyUSlc1EgjIsEpVRIEFZi7oqkahsZnVVspHqqiSPBYvJY0FdlfSToLx56SUkKDciQamrksVIUJarrkokKl15LKirkn4SlLoqWYxEZbkkKsnwZSRDUVclayFBGSRRWS91VTJIgtKvrkokKJtNXZWslkRlJSQoq5HHgkGznRm2KgnKjUhQlkOCspQ8FtRVSY9Epa5KVqOuSpYiQamrkmGQoNRVyXLksWAtJCqrkceCuipJfkCCslrvPPkdJCpdEpS6KlktCUryVhnJmuWxYJAEZdjqqkSCslJ1VSJBGZTHgpuprkryWLASeSwYJgnKRpCgbFVjzUnyWLAe6qpkOSQoqyFBWSsJyiAJSl2VbGcSlboq2Uh1VSJBGYbZzgz98liwFrOdGfJYMAx1VSJBWUpdlUhUtqqMZF1IVLokKIuRoNRVyWrlsaBHgjLbmaGfRGVYJCgLme3MMAwSlFGRx4LlkKCsVF2VSFCGYbYzgwRlMXksePPSS+SxoEeCslwT7WkazRb9JCirIUHpkahsRhKUjVZXJRKVxdRViQSlX12VSFSSlZGoLFddlUhUNopEJfmBjGSkSVD61VWJBGWt8liwHBKU9SJBqauSZHUkKHVVMix1VZLHgp48FiRrM9uZYaXyWFBXJcMkUbkRCUpdlSQ/IFFZjARlo+WxYDvKSIZGgjJIotIjUVkOCcqgPBaMCglKXZX05LGgrkokKKshUbkRCUpdleSxIBkuCcpKSVCWa7YzgwRlNSQoG0mishQJynJIVLryWJCMNgnKcuSxoK5KlqOuSiQoayVBme3MsJVlbHEvvvgiCynLkjNnzuDuJOtDonKz1FXJqJCgrJQEZTUkKl15LEiSG5GgrCcJyo3ksaCuSm62uirZCBKU9VJXJVtZxhb29NNPc+DAAQadOnWKhx56iNOnT7Nv3z6OHTvGRpKgDMpjwVrlsWA5JChdEpRhyGPBsEhQ1kqicjNIVNaDRGUpEpXNQoKSx4KbJY8FgyQoXRKULonKqJOgrFUeC7YaicqNSFTqqmS5JCobSaIybBKUrSpjC7p+/TqPPfYYn/vc5xg0NzfHwYMHOXHiBIcPH+bkyZM8++yzXLlyhY0iUVmKBKWfBOVmkKgMkqD0e/PSS0hQNhsJympIUAZJVLYzCcpy5bGg0WyxGAlKXZUshwRlVOWxIFkdCcookKAkN0fGFnT06FF27tzJpz71KQa98MIL7Nixg927d9O1c+dO7r//fs6dO8daSVRWSqIybBKVxTSaLYZNorIaEpSePBZsVRKVuiqRoKxWHgvWkwSlXx4LhkWCMiokKhulrko2mkRl2CQom5kEpZ8EZSM1mi3GmpNIUHokKDeSx4JkaRlb0OOPP87HPvYxxsbGGHT9+nXuvPNO+t166628+uqrLMbMMDPMjKeeeoqbRaJyIxKU1ZKg5LHgZpKgLEWC0iVBWSkJylaXx4KNIkEZNNac5G17HmS5JCrbXR4L1oME5WaSoNxsEpSlSFA2ggRlKRKU5ZCgLESi0vXUU0/xla98ha7//dj/ZqvK2IKyLONG5ubmyLKMflmWMT8/z2LcHXfH3fnoRz/KckhQJCijSIKyHGPNSba7PBYMkqB0SVQ2OwnKsEhUxqfarEYeC5LRIlHpJ0HpyWNBlwQlgfGpNuNTbTbCRz/6Ud7//vfT9X8+/X/YqjJG3Cc/+Unuuusu7rrrLn72Z3+Wpdxyyy3Mzc3Rb35+njzPSZZHorKRJChLkaBsBXks6JKorCcJSo9EZSuSqIwqCUqPBCX5gTwW3EgeC9ZCgtIjQemSoCwmjwXL1Wi2aDRbLIcEZSl5LNjuMkbcBz7wAY4fP87x48f5zGc+w1JijLzyyiv0u3btGnfffTdrJUFZKQnKaklQliuPBYMkKqNOorIWeSxYCYnKIAnKRpOo3AwSlEESlJUaa06ykDwWbAUSldWQqGw0CcpiJCrraaw5SZdEZaUkKBtFgjJIgjLWnGSQBCVZfxkj7sd//MdptVq0Wi3uvfdelnLPPffQdfbsWbouX77M+fPnmZycZK3GmpMMymPBckhQtioJSpdEZSXyWDAseSy42SQoS5GgrIQEpZ8EZZBEZa0kKqNMojJMEpS1ajRbLJcEZTESlfWWx4Lk5shjwSAJynaXsc1kWcaRI0c4cOAAe/fu5eGHH+aJJ57gtttuY60kKutJgrIYicpmkMeCpUhQ1osEpSuPBWuVx4LtQqIyiiQow5DHglGXx4K1kKB0SVQ2E4nKIAnKaow1J1lKHgsWIkG5GRrNFislUdnqMrawn/u5n+PFF19k0M/8zM/w93//95w4cYILFy7wi7/4iySrI1GRoGwUicqwjDUn2cryWLBcEpTNRoLSlceCpeSxYDkkKqNAotKTx4KNIlFZjjwWrIYEZZTksWC95bEgWZ2MZN2MT7WRoAyDRGW5JChrJVFZCwnKViZBGSRB2ewkKGshUVkpiUoeC4ZBgtIlURmGPBb0SFBGiURlVElUbkSCMkiCslVJUFYqjwXLlceCrSojWTcT7WkkKhstjwUrlceC9SZBGSRRWUoeC24WCYoEZVjyWJD8gARlkARlo0hQlkuCshISlIVIUNZKgjJIgrKUPBbcDHks2G7yWLBcEpVBEpVkaRnJpiBR6ZfHgmGQoHRJUFZDgrJSEpTVymNBMhwSlFEiQVmIBGUlJCjJjUlQbiYJyiAJyrBIUFZCorIWEpXk5shINp2x5iTJ6uWxYCtqNFssRoKyGhKUQXksWG95LNjs8lgwKI8F/SQqayFB2QgSlGGSoKxGXZWslgRlGPJYsBgJSpcEZaXyWJBsjIxkw0hQxpqTDEseCyQqqyFBGWUSlX4SlbWSqGwWEpRhkaBsZXksWIgEZa0kKKMgjwULkaCMKonKUvJY0NNotrgRiUqy9WQkm06j2WIzkqgsJI8Fi5Go3AwSlM1MorJaEpRkfUhQVkqCshAJSk8eCzZSHguGSYLSI0FZiERlWCQoo0iC8rY9D7IaeSwYFgmKBEWCslVlJEMhQdkMikMnaTRbDJKo9OSxYLnyWLAYCcqwSFQWIkG5mfJYsFISlYVIUBYjUdkIEpXNLo8FgyQq60WishYSlUESlWHIY8FySFBuJgnKUiQoK5XHgvUmURkGicr4VJv1JlHpkaDciERlq8pIbqo8FiyHBGU5Gs0WqyFBGaY8FixFgpJsDnksSLaePBasB4nKsEhU8liw2UlUBklUNhMJynaSkYyMPBYMkqAsJo8FEpR+EpSx5iTDMNacRIIyDBKUpUhQBklQRkEeCxYiURkFEpR+EpW1kKAMgwRlOSQoW4kEZa0kKEuRoCSjKY8FW1lGsmYSlOXIY8EgCcpGk6js2n+UQRKUfhKUpUhUdu0/ymYjQZGo9BtrTjIsEpWlSFA2gzwWrCeJykpJVFZLgrJSEpSFSFDWiwRlUB4LBjWaLVZCorLeJCirkceCjZLHgvUmUVnM+J42E+1pVkuCMkiCshwSlO0uI9kwEpRBEpXi0EmWMtacRA89x0o1mi0m2tMsh0RlM5GgLESCMkiC0tNotshjwXqRoCxHXZX0k6AMkqBIUNaDRGUtJCjrZaw5iURlKRIUicpKSFSWI48FqyFBWUoeC/JYsBx1VbIRJCprIVFZK4nKQiQoN5MEZTUkKsnNk5FsGImKHnqOQY1mi0ESlH4SldUan2qzkSQqq5HHgn4SldXSQ8+xHBKUQRKVHonKsEhUVkuCshCJylIkKIsZn2qjh56jnwRlWCQogxrNFhPtafo1mi1GRR4LBklQ1oMEZZRJUJLhkqAkkJEkNzDRnqbRbLFRGs0Wux49yqiRqHTlsaBHgrIWeSxYqTwWLIcEZTkkKhspjwWNZoutSoKy1UlQhiWPBduNBCWPBUuRqOSxYDF5LNjqMpI1k6jcvv9JtprxqTbDlMeCpUhUBklUevJYMCiPBetJgjIME+1pdu0/ys2Ux4KbQYKylEazxfhUmx4JyiAJSh4L1lMeC7YCCcpy5LFgMXkskKCslASlS6Jys0hUVkOiMr6nzaA8FqwniYoeeo6lTLSnaTRbbHcZyVA0mi2SHxhrTjIsxaGT9Gs0W4w1JxkFEpTlyGPBYiQo25FE5Y7PvsxySFBWQ6IySIKyVo1mi12PHmU5JCjbnURlkATlZtq1/yijQqIySILytj0PspVlJMkKSFA2UqPZYtCu/UdZKQnKIAnKMElQtjoJSr87nn6ZzUCishoSlPUiUdmM8ljQT4IyKI8FwyBBWcpYcxIJyjCM72mTx4IeCcpCJChbnURlfKrNVpaRbDp5LJCojKpGs4Ueeo5BEpQeiUoyevJY0E+ispQ8FjSaLUaFRGVQHgvWSx4LbhaJyiAJykZqNFuMT7UZhl37j9JotlgPEpTNLo8F20lGsuk0mi0m2tNsNhKUPBZsVRIUCcqw5LFgtSQow1AcOolE5WZqNFuMT7XZbCQom40ERYIyaiQqXRKVHonK+FSbYZKorJQEJdmaMpLk/7dr/1Em2tOsFwnKetFDzzE+1WY9SFAazRajqtFskSzfO09+h9XKY8FW1Gi2uOOzLzMojwVLmWhPs9lIUBrNFsnoy0hG1q5HjzLWnCRZP+NTbcan2mykseYkqyVRSZZnoj3N+FSblZKoDIsERYKyGIlKo9minwRlIRKVfhKU1ZCgrNQ7T36HZGXyWLCUPBYkC8tIRpZERaIyCsan2khUbhaJyqBGs8Vq5LFgIRKVlcpjwSA99BwSlZ48FuSxYDkkKOtloj1No9niZpOgrNT4VJs7Pvsy/SQqS5GoDJKgSFD65bFgWPJY0G+iPc1Ee5qtQIKyWhKUYZhoTzM+1SYZfRlJsgEm2tNsNnksGJ9qczM1mi2WIkFZiARlvUy0p5loT9NvfKrNzTbWnGTX/qNsFbfvfxKJSk8eC1ZLgjJIorJSeSxYDgnKaow1J1mMRKVLotLTaLaYaE/TT4KyGhKVQWPNSZLRk5EkN0keCzaSRKVfo9mi0WxxM020p2k0WyxGoqKHnqPf+FSb8ak2/SQoS2k0W4xPtVmKRGUzkqgsRIIyihrNFqsx1pwkjwXDIFHpp4eeQ6JyM93x9MsMGp9qs1J5LBgmCcqwjDUn2fXoUZLVy0iSZRprTtJothgGCcpGGt/TZqI9zXp558nvsNEm2tOsxkR7mq1OgrKR3rbnQZYy0Z6m0WwxDLv2H6XRbNEjQRlrTrIadadkNSQqKzXWnGSiPc1SJCrLIUEZVRIViUqyehlJskwSlYn2NKNIorLVTbSnGWtOst1MtKcZn2rTL48FG2l8qk2j2WIxEpWJ9jSLaTRbvG3Pg6yURGXX/qMs5Z0nv0Oj2aJnfE+b8ak2G0WiIlFJkmHISJKbYKw5ydv2PMhmk8eCpUhQljI+1eZtex5kI0lUJCrbjURl0Nv2PEgeC0bR+FSbYWg0W4w1J1mMRKXRbJEkoygjSW4Cicr4VJutbHyqTXJzjE+1aTRbDEuj2aInjwWNZovNbnyqTaPZIhmOPBbcLBKU5K0ykiRJkmVrNFuMT7XZziQoEpTNToIyLI1mi137j5JsHhlJkvy3PBYsh0Ql2T7eefI7JP+v8ak241Ntks1DgrLdZCRJkiTJiJloTzM+1Sb5gdv3P4kEZTvJSJJkRcb3tJloT5Mkyc0jUWk0WwzDRHua8T1tNqs8Fiyl0WwhUdlOMpIkWRGJSpIkm59E5fb9T7IUiYpEJRktGUmSJEmyRTWaLZKtKSNJkv8mQXnbngdJkiRJNr+MJEn+m0RlfKpNkiRJsvllJEmSJEmSjJiMJEmSJEmSEZORJEmSJMmmpIeeo9FskbxVRpIkSZIkyYjJSJIkSZIkGTEZSZIkSZIkIyYjSZIkSZJkxGQkSZIkSZKMmIwkSZIkSZIRk5EkSZIkSTJiMpIkSZIkSUZMRpIkSZIkyYjJSJIkSZIkGTEZSZIkSZIkIyYjSZIkSZJkxGQkSZIkSZKMmIwkSZIkSZIRk5EkSZIkSTJiMpIkSZIkSUZMRpIkSZIkyYjJSJIkSZIkGTEZSZIkSZIkIyYjSdboqaeeIrmxp556imRxTz31FMmNPfXUUyQ39tRTT5FsPxlb3Isvvsigq1evcuHCBS5cuMCFCxe4cOECb7zxBsnqHD9+nOTGjh8/TrK448ePk9zY8ePHSW7s+PHjJNtPxhb29NNPc+DAAQZ95StfYe/evTzyyCM88sgjPPLII3z9618nSZIkSZLRkLEFXb9+nccee4zPfe5zLOTSpUt8/OMf5+LFi1y8eJGLFy9y3333kSRJkiTJaMjYgo4ePcrOnTv51Kc+xUK+8Y1v8I53vIOrV69S1zVLuffeezEzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDOjy8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzIwuM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8yMLjPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDO6zAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzOgyM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwz495772UrytiCHn/8cT72sY8xNjbGoLm5Ob797W/zu7/7u7z3ve/l3e9+N5/4xCdYzOc//3ncHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcUHtDGoAABNVSURBVHfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXc+//nPsxVlbEFZlnEjr7/+Ou95z3v44z/+Y86fP8/zzz/Piy++yBe/+EWSJEmSJBkNGSPuk5/8JHfddRd33XUXP/uzP8tS3v72t/OHf/iHvP3tb6fr9ttv54EHHuAf//EfSZIkSZJkNGSMuA984AMcP36c48eP85nPfIalfOtb3+LkyZP0+/73v88P/dAPkSRJkiTJaMgYcT/+4z9Oq9Wi1Wpx7733spT//M//5ODBg7z22mt0vf7663z1q1/lfe97H0mSJEmSjIaMbcbM+PjHP0673Wbv3r380i/9Eh/+8Ie57777SJIkSZJkNGRsYT/3cz/Hiy++yKAPfOADXLx4kRMnTnDx4kU+9KEPkSRJkiTJ6MhIkiRJkiQZMRlJkiRJkiQjJiO5obIsOXPmDO5O8v968cUXGVSWJWfOnMHd2c5ee+01zpw5w8WLFxlUliVnzpzB3dmu3J0zZ85w5coVBpVlyZkzZ3B3truvfe1rVFVFv7IsOXPmDO7OdnX16lUuXLjAhQsXuHDhAhcuXOCNN96gpyxLzpw5g7uzXV29epW/+7u/4+WXX2ZQWZacOXMGd2fUZSQLOnXqFA899BCnT59m3759HDt2jOQHnn76aQ4cOEC/U6dO8dBDD3H69Gn27dvHsWPH2I4++clP8sgjj3D69GkOHTrEBz7wAf7rv/6LrlOnTvHQQw9x+vRp9u3bx7Fjx9hunnzyST760Y/y1a9+lQ9/+MM888wz9Jw6dYqHHnqI06dPs2/fPo4dO8Z29dprr/Erv/IrfO1rX6Pn1KlTPPTQQ5w+fZp9+/Zx7NgxtqOvfOUr7N27l0ceeYRHHnmERx55hK9//et0nTp1ioceeojTp0+zb98+jh07xnZz9uxZ3vve9/I3f/M3HDlyhF/5lV9hfn6erlOnTvHQQw9x+vRp9u3bx7FjxxhlGclbzM3NcfDgQU6cOMHhw4c5efIkzz77LFeuXGE7u379Oo899hif+9zn6Dc3N8fBgwc5ceIEhw8f5uTJkzz77LNcuXKF7eSf//mf+dKXvsRf/uVfcvjwYf7qr/6K//iP/+DUqVPMzc1x8OBBTpw4weHDhzl58iTPPvssV65cYbu4fPkyf/Znf8aXv/xlPv3pT/PFL36RY8eOcfXqVebm5jh48CAnTpzg8OHDnDx5kmeffZYrV66w3dR1zfT0NLfddhs9c3NzHDx4kBMnTnD48GFOnjzJs88+y5UrV9huLl26xMc//nEuXrzIxYsXuXjxIvfddx9zc3McPHiQEydOcPjwYU6ePMmzzz7LlStX2C7m5uZ47LHHOHr0KEeOHOEv/uIvuH79On/7t3/L3NwcBw8e5MSJExw+fJiTJ0/y7LPPcuXKFUZVRvIWL7zwAjt27GD37t107dy5k/vvv59z586xnR09epSdO3fyqU99in4vvPACO3bsYPfu3XTt3LmT+++/n3PnzrGd7Nixg2eeeYYdO3bQc8cdd/Cd73yHF154gR07drB79266du7cyf3338+5c+fYLt7xjnfwla98hR07dtAlIszNzVHXNS+88AI7duxg9+7ddO3cuZP777+fc+fOsd38wR/8Ab/wC7/AO9/5TnpeeOEFduzYwe7du+nauXMn999/P+fOnWO7+cY3vsE73vEOrl69Sl3X9Lzwwgvs2LGD3bt307Vz507uv/9+zp07x3Zx9uxZfuRHfoR7772Xnr/+67/mF3/xF3nhhRfYsWMHu3fvpmvnzp3cf//9nDt3jlGVkbzF9evXufPOO+l366238uqrr7KdPf7443zsYx9jbGyMftevX+fOO++k36233sqrr77KdvLDP/zDtFoter71rW/x/PPP88ADD3D9+nXuvPNO+t166628+uqrbBdZlrF7927m5ub40pe+xN69e3n00Ue5/fbbuX79OnfeeSf9br31Vl599VW2k3/4h3/g5Zdf5jd/8zfpd/36de6880763Xrrrbz66qtsJ3Nzc3z729/md3/3d3nve9/Lu9/9bj7xiU/Qdf36de6880763Xrrrbz66qtsF9euXUNVefzxx3n3u9/NXXfdxZ/+6Z/Sdf36de6880763Xrrrbz66quMqozkLebm5siyjH5ZljE/P892lmUZC5mbmyPLMvplWcb8/Dzb1euvv86v/uqv8pGPfISf/MmfZG5ujizL6JdlGfPz82w3V69e5b/+67+IMfL3f//3XL9+nbm5ObIso1+WZczPz7NdvPHGGzz++OP8wR/8AYPm5ubIsox+WZYxPz/PdvL666/znve8hz/+4z/m/PnzPP/887z44ot88YtfZG5ujizL6JdlGfPz82wXr732GqdPn6bZbPK1r32NL37xi/zRH/0R586dY25ujizL6JdlGfPz84yqjOQtbrnlFubm5ug3Pz9Pnuckb3XLLbcwNzdHv/n5efI8Zzv6p3/6J97//vfzwQ9+kH379tF1yy23MDc3R7/5+XnyPGe7CSHwwQ9+kD/5kz/hf/yP/8GJEye45ZZbmJubo9/8/Dx5nrNd/P7v/z4/9VM/xbe+9S3Onj3L1atXuXTpEu7OLbfcwtzcHP3m5+fJ85zt5O1vfzt/+Id/yNvf/na6br/9dh544AH+8R//kVtuuYW5uTn6zc/Pk+c528WP/uiP8mM/9mM8+OCDdJkZDzzwAH/zN3/DLbfcwtzcHP3m5+fJ85xRlZG8RYyRV155hX7Xrl3j7rvvJnmrGCOvvPIK/a5du8bdd9/NdnP+/Hl+7dd+jd/+7d/mQx/6ED0xRl555RX6Xbt2jbvvvpvt4pvf/CZ//ud/Tr9du3bxr//6r8QYeeWVV+h37do17r77braLEALf/e53+cIXvsAXvvAF/uVf/oWzZ89y/vx5Yoy88sor9Lt27Rp3330328m3vvUtTp48Sb/vf//7/NAP/RAxRl555RX6Xbt2jbvvvpvtYmJigkFZlpFlGTFGXnnlFfpdu3aNu+++m1GVkbzFPffcQ9fZs2fpunz5MufPn2dycpLkre655x66zp49S9fly5c5f/48k5OTbCdlWbJ//35+//d/n6mpKeq6pq5r5ubmuOeee+g6e/YsXZcvX+b8+fNMTk6yXczNzfHpT3+ab37zm3T927/9G+fOneOBBx7gnnvuoevs2bN0Xb58mfPnzzM5Ocl28Vu/9Vs888wzPPPMMzzzzDP89E//NB/5yEf40Ic+xD333EPX2bNn6bp8+TLnz59ncnKS7eQ///M/OXjwIK+99hpdr7/+Ol/96ld53/vexz333EPX2bNn6bp8+TLnz59ncnKS7WJqaoqrV6/y/PPP03X16lVefPFF3ve+93HPPffQdfbsWbouX77M+fPnmZycZFRlJG+RZRlHjhzhwIED7N27l4cffpgnnniC2267jeStsizjyJEjHDhwgL179/Lwww/zxBNPcNttt7GdfOELX+C73/0uv/Ebv8G73vUu3vWud/Gud72L3/u93yPLMo4cOcKBAwfYu3cvDz/8ME888QS33XYb28VP/MRP8IlPfIJf/uVf5td//dd5z3vewwc/+EF+/ud/nizLOHLkCAcOHGDv3r08/PDDPPHEE9x2220kkGUZR44c4cCBA+zdu5eHH36YJ554gttuu43txMz4+Mc/TrvdZu/evfzSL/0SH/7wh7nvvvvIsowjR45w4MAB9u7dy8MPP8wTTzzBbbfdxnYhIhw/fpzf+Z3f4f9rD/5Cmuz7AA5/+A1T7wRh6EQhKknsxDJxTUYwiiEj6B9Ym2FZRkl600FKK4kVHhT9GURUN2Ii2UmRRRlEppgVg5ymSQd1V0jWQURpR2ZaycN4GciLZfA8FJvf6/L5fBQXF+P1enE4HCilOH36NHV1dZSXl1NaWsqJEydIS0sjVinEjBwOB6FQCMMwCIfDeDwexP+4XC4ePXrEdA6Hg1AohGEYhMNhPB4Pc43f78c0TUzTxDRNTNPENE0CgQARDoeDUCiEYRiEw2E8Hg9zTWlpKf39/Zw4cYInT55QWVlJlMPhIBQKYRgG4XAYj8fDXNbQ0IDb7SbK4XAQCoUwDINwOIzH42Eu2rp1K319fRiGQV9fHzt37iTK4XAQCoUwDINwOIzH42GuKSws5P79+zQ3N9PT00NVVRVRDoeDUCiEYRiEw2E8Hg+xTCF+SdM0lFKI36NpGkopxM9pmoZSirlKKUVaWhoWi4WZaJqGUgoxM03TUEoxlyml0DQNpRQz0TQNpRRzWXJyMhaLhZlomoZSilinEEIIIYSIMQohhBBCiBijEEIIIYSIMQohhBBCiBijEEIIIYSIMQohhBBCiBijEEIIIYSIMQohhJhBS0sLuq6j6zq6rqPrOrquo+s6uq7j9/uJ0HWdwcFB/rTy8nJevXrFbHw+H0NDQwgh4otCCCFmkJqais1mw2azYbVa6ejoYGJiApvNhs1mIz09nYhv374xNTXFn3Tp0iWysrLIyclhNvv27aOmpgYhRHxRCCHEDDZs2EAgECAQCHDw4EEivF4vgUCAQCBAbW0tEQ0NDaxYsYI/ZXx8HMMw2L17N7/D6XQScefOHYQQ8UMhhBD/QmVlJQMDA0RUVlbS2dnJtm3bKCgowOfzMTw8zPXr13G73djtdo4fP07UxMQEp06dwuVyUVhYSFVVFcPDw/xKW1sbmZmZZGdnE9XV1cXmzZspKCiguLiYCxcuMN369etpaWlBCBE/FEII8S90d3czMjJCRHd3N0eOHGHLli2cO3eOiYkJysrKuHfvHvX19dTV1XH58mXu3btHRE1NDQ8ePCAYDNLW1obNZqO0tJTR0VF+prOzk6KiIqKGh4fZu3cvXq+Xhw8fcujQIS5evEhraytRRUVFDAwMMDo6ihAiPiiEEOI/VFFRwbp163A6nZSVlTEyMkIwGMTpdLJp0yaWLVtGb28vz58/p6OjgzNnzlBYWEhWVhZHjx7FarVy9epVfubx48fk5eUR9ebNGywWC06nk5SUFFavXk1TUxN5eXlE5ebmYrFY6O/vRwgRHxRCCPEfWrhwIVHJyckkJSWRkpJCVGpqKj9+/ODly5dENDY24vf78fv9+P1+xsbGePHiBTP59u0bk5OTzJ8/n6hVq1axdOlS3G43Pp+Ps2fPkpSURG5uLlFKKebNm8fY2BhCiPigEEKIv+D79+/MmzcPu92O3W7Hbrdjt9uprq7G6/XyuywWC62trZw/f57s7Gxu3rzJxo0baWpqYjqlFEKI+KEQQoi/wGq1Mjk5icvloqSkhJKSEkpKSkhPTyc5OZmZJCQkYLFYGB8fJ2poaIi7d++yevVqjh07RldXFzt27KCxsZHpvn79yvz58xFCxAeFEEL8BS6XiwULFnD48GG+fPlCRFdXF3v27OHz58/8TF5eHqZpEvXp0ydqa2vp6ekhYmpqirdv35KTk0PU0NAQP378YMmSJQgh4oNCCCH+AqUUzc3NjI6OYrfbyc/PZ//+/Rw4cIA1a9bwMy6Xi97eXqJWrlxJVVUVFRUV5Ofns3z5cj58+MDJkyeJ6u3tZfHixSxatAghRHxQCCHELDRNwzRN3G43/880TdxuNxGmaeJ2u4lau3Yt/f39TNfQ0EAgECBiwYIFXLt2jadPn9Le3k5/fz+7du3iV3w+H4ODg7x//54oXdd59uwZ7e3t9PX1cePGDTIzM4m6ffs2ZWVlCCHih0IIIf6yhIQEMjIyUEoxG6vVyvbt27ly5QrTKaXIyMggMTGR6V6/fs27d+/wer0IIeKHQgghYkx1dTXt7e18/PiR2QSDQerr60lISEAIET8UQggRYzRN49atW1itVmYTDAZxuVwIIeKLQgghYlBiYiIWi4XZaJqGECL+KIQQQgghYoxCCCGEECLG/AMYVo5GckztVwAAAABJRU5ErkJggg==" style="width: 100%; height: auto;"></div></div></div></div></div></div></div><h2  class = 'S13' id = 'H_77261343' ><img class = "imageNode" src = "data:image/png;base64,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" width = "373" height = "195" alt = "" style = "vertical-align: baseline"></img></h2><h2  class = 'S13' id = 'H_4A6AF4FC' ><span></span></h2><h2  class = 'S13' id = 'H_CA962223' ><span>Calculate parameters;</span></h2><div  class = 'S8'><span>calculate only for the first participant;</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span>inputData = AccData;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>WindowLength = FS*10;               </span><span style="color: rgb(2, 128, 9);">% why FS*10? </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>ApplyRealignment = true;            </span><span style="color: rgb(2, 128, 9);">% reorder data to  1 = V; 2= ML, 3 = AP</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>ApplyRemoveSteps = false;           </span><span style="color: rgb(2, 128, 9);">% if true - removes first 30 and last 30 steps</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre;"><span>[</span><span class="warning_squiggle_rte857602304">ResultStruct</span><span>] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLength,ApplyRealignment,ApplyRemoveSteps)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="3B28D337" data-testid="output_3" data-width="859" data-height="734" data-hashorizontaloverflow="false" data-scroll-top="473" data-scroll-left="0" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.3908
+                   StrideRegularity_ML: 0.4191
+                   StrideRegularity_AP: 0.1101
+                  StrideRegularity_All: 0.3529
+           RelativeStrideVariability_V: 0.6092
+          RelativeStrideVariability_ML: 0.5809
+          RelativeStrideVariability_AP: 0.8899
+         RelativeStrideVariability_All: 0.6471
+                     StrideTimeSamples: 132
+                     StrideTimeSeconds: 1.3200
+                            GaitSymm_V: 16.8830
+                           GaitSymm_AP: NaN
+                         GaitSymmIndex: 2.8065
+                        StepLengthMean: 11.5894
+                              Distance: 1.8825e+03
+                      WalkingSpeedMean: 7.8279
+                 StrideTimeVariability: 0.1931
+                StrideSpeedVariability: 1.1210
+               StrideLengthVariability: 0.7532
+      StrideTimeVariabilityOmitOutlier: 14.7357
+     StrideSpeedVariabilityOmitOutlier: 0.8713
+    StrideLengthVariabilityOmitOutlier: 0.4511
+                    IndexHarmonicity_V: 0.6094
+                   IndexHarmonicity_ML: 0.8041
+                   IndexHarmonicity_AP: 0.9122
+                  IndexHarmonicity_All: 0.6742
+                       HarmonicRatio_V: 2.4928
+                      HarmonicRatio_ML: 2.8449
+                      HarmonicRatio_AP: 1.6364
+                      HarmonicRatioP_V: 9.1114
+                     HarmonicRatioP_ML: 14.1449
+                     HarmonicRatioP_AP: 5.6995
+                FrequencyVariability_V: 0.5234
+               FrequencyVariability_ML: 0.6435
+               FrequencyVariability_AP: 0.6281
+                       StrideFrequency: 0.7367
+                        LyapunovWolf_V: 1.3653
+                       LyapunovWolf_ML: 1.1477
+                       LyapunovWolf_AP: 1.4231
+                       LyapunovRosen_V: 1.0151
+                      LyapunovRosen_ML: 0.7871
+                      LyapunovRosen_AP: 0.9792
+                       SampleEntropy_V: 0.1999
+                      SampleEntropy_ML: 0.2537
+                      SampleEntropy_AP: 0.2710
+               LyapunovPerStrideWolf_V: 1.8534
+              LyapunovPerStrideWolf_ML: 1.5579
+              LyapunovPerStrideWolf_AP: 1.9318
+              LyapunovPerStrideRosen_V: 1.3780
+             LyapunovPerStrideRosen_ML: 1.0685
+             LyapunovPerStrideRosen_AP: 1.3292
+</div></div></div></div></div></div><div  class = 'S14'><span style=' font-weight: bold;'>output:</span></div><div  class = 'S8'><span>- </span><span style=' text-decoration: underline;'>NaN GaitSymm_V: </span></div><ul  class = 'S15'><li  class = 'S16'><span>Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in                         transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4</span></li></ul><div  class = 'S8'><span>- SampEn has two advantages over ApEn: data length independence and a relatively trouble-free implementation.</span></div><div  class = 'S8'><span>- Some Settings ResulStruct</span></div><div  class = 'S8'><span style=' font-weight: bold;'>IgnoreMinMaxStrides = 0.10; </span><span>                  % Number or percentage of highest&amp;lowest values ignored for improved variability estimation</span></div><div  class = 'S8'><span style=' font-weight: bold;'>N_Harm = 12;     </span><span>                                        % Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation</span></div><div  class = 'S8'><span style=' font-weight: bold;'>Lyap_m = 7;  </span><span>                                              % Embedding dimension (used in Lyapunov estimations)</span></div><div  class = 'S8'><span style=' font-weight: bold;'>Lyap_FitWinLen</span><span> = round(60/100*FS);       % Fitting window length (used in Lyapunov estimations Rosenstein's method)</span></div><div  class = 'S8'><span style=' font-weight: bold;'>Sen_m = 5;</span><span>                                                 % Dimension, the length of the subseries to be matched (used in sample entropy estimation)</span></div><div  class = 'S8'><span style=' font-weight: bold;'>Sen_r = 0.3;</span><span>                                                % Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation)</span></div><h3  class = 'S17' id = 'H_2EA43FAC' ><span>Index of harmonicity (Lamoth et al. 2002)</span></h3><div  class = 'S8'><span>by means of a discrete Fourier transform (DFT). The peak power at the first six harmonics was estimated and, subsequently, the index of harmonicity was defined as ; </span><span style=' font-weight: bold;'>FORMULA</span></div><div  class = 'S8'><span>where P0 is the power spectral density of the fundamental frequency (first harmonic) and $ Pi the cumulative sum of power spectral density of the fundamental frequency and the first five superharmonics. A power ratio of 1 indicates that the rotation of the pelvis or the thorax is perfectly harmonic. In view of possible drift, which could lead to missing or widening peaks, the power spectral density of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of the peak frequency value. All power spectral densities were normalized by dividing the power by the sum of the total power spectrum, which equals the variance.</span></div><h3  class = 'S17' id = 'H_3E396779' ><span>Lyapunov exponents (Wolfs vs. Rosenstein)</span></h3><div  class = 'S8' id = 'H_B1B85E8D' ><span>The W-algorithm is advocated for use when examining local dynamic stability with small gait data sets.</span></div><h2  class = 'S13' id = 'H_9FD2A70A' ><span>Visualize step detection;</span></h2><div  class = 'S8'><span>function [ResultStruct] = GaitVariabilityAnalysisIH_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);</span></div><div  class = 'S8'><span style=' font-weight: bold;'>script for analysing straight parts</span></div><div  class = 'S8'><span>(1) Realign Data</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%% Realign data</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>data = inputData(:, [3,2,4]); </span><span style="color: rgb(2, 128, 9);">% reorder data to  1 = V; 2= ML, 3 = AP</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%Realign sensor data to VT-ML-AP frame</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">if </span><span>ApplyRealignment </span><span style="color: rgb(2, 128, 9);">% apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span><span style="color: rgb(2, 128, 9);">% Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    dataAcc = RealignedAcc;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'></div></div></div><div  class = 'S14'><span>(2) Filter Data</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%% Filter data strongly &amp; Determine location of steps</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Filter  data</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>[B,A] = butter(2,3/(FS/2),</span><span style="color: rgb(170, 4, 249);">'low'</span><span>); </span><span style="color: rgb(2, 128, 9);">% Filters data very strongly which is needed to determine turns correctly</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>dataStepDetection = filtfilt(B,A,dataAcc);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'></div></div></div><div  class = 'S14'><span>(3) Determine Location of steps</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Determine steps;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%%</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% such as steps taken while walking, regardless of device orientation.</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>magNoGfilt = magfilt - mean(magfilt);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>minPeakHeight2 = std(magNoGfilt);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>[pks, locs] = findpeaks(magNoGfilt, </span><span style="color: rgb(170, 4, 249);">'MINPEAKHEIGHT'</span><span>, minPeakHeight2); </span><span style="color: rgb(2, 128, 9);">% for step detection</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre;"><span>numStepsOption2_filt = numel(pks); </span><span style="color: rgb(2, 128, 9);">% counts number of steps;</span></span></div></div></div><div  class = 'S14'><span>(4) Determine location of turns</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%% Determine locations of turns;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>diffLocs = diff(locs); </span><span style="color: rgb(2, 128, 9);">% calculates difference in step location</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>avg_diffLocs = mean(diffLocs); </span><span style="color: rgb(2, 128, 9);">% average distance between steps</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>std_diffLocs = std(diffLocs); </span><span style="color: rgb(2, 128, 9);">% standard deviation of distance between steps</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>figure(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>findpeaks(diffLocs, </span><span style="color: rgb(170, 4, 249);">'MINPEAKHEIGHT'</span><span>, avg_diffLocs, </span><span style="color: rgb(170, 4, 249);">'MINPEAKDISTANCE'</span><span>,5); </span><span style="color: rgb(2, 128, 9);">% these values have been chosen based on visual inspection of the signal</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre;"><span>line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="034BC009" data-testid="output_4" style="width: 889px;"><div class="figureElement" style="cursor: default;"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_164_6" widgetid="uniqName_164_6" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_164_8" widgetid="uniqName_164_8" src="data:image/png;base64,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" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre;"><span>[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, </span><span style="color: rgb(170, 4, 249);">'MINPEAKHEIGHT'</span><span>, avg_diffLocs,</span><span style="color: rgb(170, 4, 249);">'MINPEAKDISTANCE'</span><span>,5);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>locsTurns =  [locs(locs_diffLocs),  locs(locs_diffLocs+1)];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'></div></div></div><div  class = 'S14'><span>(5) Visualize turns</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%% Visualizing turns</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Duplying signal + visualing</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% to make second signal with the locations of the turns filled with NaN, so</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% that both signals can be plotted above each other in a different colour</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>magNoGfilt_copy = magNoGfilt;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">for </span><span>k = 1: size(locsTurns,1)</span><span class="warning_squiggle_rte857602304 warningHighlight857602304">;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% visualising signal;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>figure;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>subplot(2,1,1)</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(170, 4, 249);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>plot(magNoGfilt,</span><span style="color: rgb(170, 4, 249);">'b'</span><span>)</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>plot(magNoGfilt_copy, </span><span style="color: rgb(170, 4, 249);">'r'</span><span>);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>title(</span><span style="color: rgb(170, 4, 249);">'Inside Straight: Filtered data with turns highlighted in blue'</span><span>)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(170, 4, 249);">off</span><span>;</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="12F11B09" data-testid="output_5" style="width: 889px;"><div class="figureElement" style="cursor: default;"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_164_9" widgetid="uniqName_164_9" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_164_11" widgetid="uniqName_164_11" src="data:image/png;base64,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" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper"><div  class = 'S18'></div></div></div><div  class = 'S14'><span>(6) CALCULATIONS</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">%% Calculation</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% VRAAG LAURENS zie blauwe blaadje</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>startPos = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">for </span><span>i = 1: size(locsTurns,1)</span><span class="warning_squiggle_rte857602304 warningHighlight857602304">;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    endPos = locsTurns(i,1)-1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    inputData = dataAcc(startPos:endPos,:);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    WindowLen = size(inputData,1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    ApplyRealignment = false;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); </span><span style="color: rgb(2, 128, 9);">% Naam van deze moet nog aangepast.</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span><span style="color: rgb(14, 0, 255);">if </span><span>i ==1 </span><span style="color: rgb(2, 128, 9);">% only the firs time</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>        Parameters = fieldnames(ResultStruct);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>        NrParameters = length(Parameters);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span><span style="color: rgb(14, 0, 255);">for </span><span>j = 1:NrParameters </span><span style="color: rgb(2, 128, 9);">% only works if for every bin we get the same outcomes (which is the case in this script)</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>        DataStraight.(</span><span class="warning_squiggle_rte857602304">[</span><span>char(Parameters(j))])(i) = ResultStruct.(</span><span class="warning_squiggle_rte857602304">[</span><span>char(Parameters(j))]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    startPos = locsTurns(i,2)+1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>clear </span><span style="color: rgb(170, 4, 249);">ResultStruct</span><span>;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(2, 128, 9);">% straight;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">for </span><span>j = 1:NrParameters</span><span class="warning_squiggle_rte857602304 warningHighlight857602304">;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre;"><span>    ResultStruct.(</span><span class="warning_squiggle_rte857602304">[</span><span>char(Parameters(j))]) = nanmean(DataStraight.(</span><span class="warning_squiggle_rte857602304">[</span><span>char(Parameters(j))]))</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre;"><span style="color: rgb(14, 0, 255);">end</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="1B4993C4" data-testid="output_6" data-width="859" data-height="34" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>    StrideRegularity_V: 0.9005
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="32207BDD" data-testid="output_7" data-width="859" data-height="48" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>     StrideRegularity_V: 0.9005
+    StrideRegularity_ML: 0.7110
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="6761CFE5" data-testid="output_8" data-width="859" data-height="62" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>     StrideRegularity_V: 0.9005
+    StrideRegularity_ML: 0.7110
+    StrideRegularity_AP: 0.6643
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="C7C8DF4E" data-testid="output_9" data-width="859" data-height="76" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>      StrideRegularity_V: 0.9005
+     StrideRegularity_ML: 0.7110
+     StrideRegularity_AP: 0.6643
+    StrideRegularity_All: 0.7765
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="94F0ACE6" data-testid="output_10" data-width="859" data-height="90" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>             StrideRegularity_V: 0.9005
+            StrideRegularity_ML: 0.7110
+            StrideRegularity_AP: 0.6643
+           StrideRegularity_All: 0.7765
+    RelativeStrideVariability_V: 0.0995
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="27BD8749" data-testid="output_11" data-width="859" data-height="104" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>              StrideRegularity_V: 0.9005
+             StrideRegularity_ML: 0.7110
+             StrideRegularity_AP: 0.6643
+            StrideRegularity_All: 0.7765
+     RelativeStrideVariability_V: 0.0995
+    RelativeStrideVariability_ML: 0.2890
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="BF42401A" data-testid="output_12" data-width="859" data-height="118" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>              StrideRegularity_V: 0.9005
+             StrideRegularity_ML: 0.7110
+             StrideRegularity_AP: 0.6643
+            StrideRegularity_All: 0.7765
+     RelativeStrideVariability_V: 0.0995
+    RelativeStrideVariability_ML: 0.2890
+    RelativeStrideVariability_AP: 0.3357
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="8236BCAC" data-testid="output_13" data-width="859" data-height="132" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="AAEBEE1A" data-testid="output_14" data-width="859" data-height="146" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="ACDA8683" data-testid="output_15" data-width="859" data-height="160" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="4D05722A" data-testid="output_16" data-width="859" data-height="174" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="16632CD8" data-testid="output_17" data-width="859" data-height="188" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="21DBD72B" data-testid="output_18" data-width="859" data-height="202" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="42E9BB1C" data-testid="output_19" data-width="859" data-height="216" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="782B8BFF" data-testid="output_20" data-width="859" data-height="230" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="0AC89326" data-testid="output_21" data-width="859" data-height="244" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="E2515463" data-testid="output_22" data-width="859" data-height="258" data-hashorizontaloverflow="false" style="width: 889px; max-height: 269px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+            StrideTimeVariability: 0.0200
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="2BAC16DE" data-testid="output_23" data-width="859" data-height="272" data-hashorizontaloverflow="false" style="width: 889px; max-height: 283px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+            StrideTimeVariability: 0.0200
+           StrideSpeedVariability: 0.1990
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="810B2656" data-testid="output_24" data-width="859" data-height="286" data-hashorizontaloverflow="false" style="width: 889px; max-height: 297px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+            StrideTimeVariability: 0.0200
+           StrideSpeedVariability: 0.1990
+          StrideLengthVariability: 0.5502
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="41675B62" data-testid="output_25" data-width="859" data-height="300" data-hashorizontaloverflow="false" style="width: 889px; max-height: 311px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                  StrideRegularity_V: 0.9005
+                 StrideRegularity_ML: 0.7110
+                 StrideRegularity_AP: 0.6643
+                StrideRegularity_All: 0.7765
+         RelativeStrideVariability_V: 0.0995
+        RelativeStrideVariability_ML: 0.2890
+        RelativeStrideVariability_AP: 0.3357
+       RelativeStrideVariability_All: 0.2235
+                   StrideTimeSamples: 217.5556
+                   StrideTimeSeconds: 2.1756
+                          GaitSymm_V: 5.7952
+                         GaitSymm_AP: 7.0128
+                       GaitSymmIndex: 2.3916
+                      StepLengthMean: 11.9893
+                            Distance: 83.0819
+                    WalkingSpeedMean: 6.1785
+               StrideTimeVariability: 0.0200
+              StrideSpeedVariability: 0.1990
+             StrideLengthVariability: 0.5502
+    StrideTimeVariabilityOmitOutlier: 6.0071
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="A31B96BE" data-testid="output_26" data-width="859" data-height="314" data-hashorizontaloverflow="false" style="width: 889px; max-height: 325px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                   StrideRegularity_V: 0.9005
+                  StrideRegularity_ML: 0.7110
+                  StrideRegularity_AP: 0.6643
+                 StrideRegularity_All: 0.7765
+          RelativeStrideVariability_V: 0.0995
+         RelativeStrideVariability_ML: 0.2890
+         RelativeStrideVariability_AP: 0.3357
+        RelativeStrideVariability_All: 0.2235
+                    StrideTimeSamples: 217.5556
+                    StrideTimeSeconds: 2.1756
+                           GaitSymm_V: 5.7952
+                          GaitSymm_AP: 7.0128
+                        GaitSymmIndex: 2.3916
+                       StepLengthMean: 11.9893
+                             Distance: 83.0819
+                     WalkingSpeedMean: 6.1785
+                StrideTimeVariability: 0.0200
+               StrideSpeedVariability: 0.1990
+              StrideLengthVariability: 0.5502
+     StrideTimeVariabilityOmitOutlier: 6.0071
+    StrideSpeedVariabilityOmitOutlier: 0.2542
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="BA375E3D" data-testid="output_27" data-width="859" data-height="328" data-hashorizontaloverflow="false" style="width: 889px; max-height: 339px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="58267D3A" data-testid="output_28" data-width="859" data-height="342" data-hashorizontaloverflow="false" style="width: 889px; max-height: 353px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="E076968E" data-testid="output_29" data-width="859" data-height="356" data-hashorizontaloverflow="false" style="width: 889px; max-height: 367px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="F7A7D385" data-testid="output_30" data-width="859" data-height="370" data-hashorizontaloverflow="false" style="width: 889px; max-height: 381px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="DDA96C2D" data-testid="output_31" data-width="859" data-height="384" data-hashorizontaloverflow="false" style="width: 889px; max-height: 395px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="FC3B4915" data-testid="output_32" data-width="859" data-height="398" data-hashorizontaloverflow="false" style="width: 889px; max-height: 409px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="C841B74A" data-testid="output_33" data-width="859" data-height="412" data-hashorizontaloverflow="false" style="width: 889px; max-height: 423px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="EB0B8CF8" data-testid="output_34" data-width="859" data-height="426" data-hashorizontaloverflow="false" style="width: 889px; max-height: 437px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="6CA34B0E" data-testid="output_35" data-width="859" data-height="440" data-hashorizontaloverflow="false" style="width: 889px; max-height: 451px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="5D4CF778" data-testid="output_36" data-width="859" data-height="454" data-hashorizontaloverflow="false" style="width: 889px; max-height: 465px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="E329E1E0" data-testid="output_37" data-width="859" data-height="468" data-hashorizontaloverflow="false" style="width: 889px; max-height: 479px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="51C2AD0B" data-testid="output_38" data-width="859" data-height="482" data-hashorizontaloverflow="false" style="width: 889px; max-height: 493px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="5F0960B2" data-testid="output_39" data-width="859" data-height="496" data-hashorizontaloverflow="false" style="width: 889px; max-height: 507px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="65F21AC5" data-testid="output_40" data-width="859" data-height="510" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="0E2A6448" data-testid="output_41" data-width="859" data-height="524" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="4FCE20FF" data-testid="output_42" data-width="859" data-height="538" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="267BCB0B" data-testid="output_43" data-width="859" data-height="552" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="97C3E506" data-testid="output_44" data-width="859" data-height="566" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="36CB609B" data-testid="output_45" data-width="859" data-height="580" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="A0A175C0" data-testid="output_46" data-width="859" data-height="594" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="8A8D774A" data-testid="output_47" data-width="859" data-height="608" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="7CF019CC" data-testid="output_48" data-width="859" data-height="622" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="A9B7C1A9" data-testid="output_49" data-width="859" data-height="636" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="EC0450B0" data-testid="output_50" data-width="859" data-height="650" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="76ECEE0E" data-testid="output_51" data-width="859" data-height="664" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+               LyapunovPerStrideWolf_V: 2.3378
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="555BDE94" data-testid="output_52" data-width="859" data-height="678" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+               LyapunovPerStrideWolf_V: 2.3378
+              LyapunovPerStrideWolf_ML: 2.0830
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="4E516E6D" data-testid="output_53" data-width="859" data-height="692" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+               LyapunovPerStrideWolf_V: 2.3378
+              LyapunovPerStrideWolf_ML: 2.0830
+              LyapunovPerStrideWolf_AP: 2.4095
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="6D782227" data-testid="output_54" data-width="859" data-height="706" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+               LyapunovPerStrideWolf_V: 2.3378
+              LyapunovPerStrideWolf_ML: 2.0830
+              LyapunovPerStrideWolf_AP: 2.4095
+              LyapunovPerStrideRosen_V: 1.5914
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="3396F107" data-testid="output_55" data-width="859" data-height="720" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;" data-scroll-top="459" data-scroll-left="0"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+               LyapunovPerStrideWolf_V: 2.3378
+              LyapunovPerStrideWolf_ML: 2.0830
+              LyapunovPerStrideWolf_AP: 2.4095
+              LyapunovPerStrideRosen_V: 1.5914
+             LyapunovPerStrideRosen_ML: 1.2964
+</div></div></div><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="45400C78" data-testid="output_56" data-width="859" data-height="734" data-hashorizontaloverflow="false" style="width: 889px; max-height: 261px;" data-scroll-top="0" data-scroll-left="0"><div class="textElement"><div><span class="variableNameElement">ResultStruct = <span class="headerElement">struct with fields:</span></span></div><div>                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+                       SampleEntropy_V: 0.2014
+                      SampleEntropy_ML: 0.2358
+                      SampleEntropy_AP: 0.2608
+               LyapunovPerStrideWolf_V: 2.3378
+              LyapunovPerStrideWolf_ML: 2.0830
+              LyapunovPerStrideWolf_AP: 2.4095
+              LyapunovPerStrideRosen_V: 1.5914
+             LyapunovPerStrideRosen_ML: 1.2964
+             LyapunovPerStrideRosen_AP: 1.7481
+</div></div></div></div></div></div><h2  class = 'S13' id = 'H_4847D0D3' ><span>AggregateFunction;</span></h2></div>
+<br>
+<!-- 
+##### SOURCE BEGIN #####
+%% Gait Variability Analysis CLBP
+
+% Gait Variability Analysis 
+% Script created for MAP 2020-2021
+% adapted from Claudine Lamoth and Iris Hagoort
+% version1 October 2020
+
+% Input: needs mat file which contains all raw accelerometer data
+% Input: needs excel file containing the participant information including
+% leg length.
+
+
+%% Clear and close;
+
+clear;
+close all;
+%% Load data;
+% Select 1 trial. *For loop to import all data will be used at a later stage*
+
+[FNaam,FilePad] = uigetfile('*.xls','Load phyphox data...');
+filename =[FilePad FNaam];
+PhyphoxData = xlsread(filename)
+
+%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData
+%load('ExcelInfo.mat');
+%Participants = fields(AccData);
+%% Settings;
+
+LegLength = 98 % LegLength info not available!
+%LegLengths = excel.data.GeneralInformation(:,5); % leglength info is in 5th column
+LegLengthsM = LegLength/100;           % convert to m
+t1 = length(PhyphoxData(:,1));         % Number of Samples
+
+
+FS = 100;                                  % sample frequency
+Time_ms = PhyphoxData(:,1);
+accX = PhyphoxData(:,2);
+accY = PhyphoxData(:,3);
+accZ = PhyphoxData(:,4);
+AccData = (PhyphoxData(:,[1 2 3 4]));      % matrix with accelerometer data
+
+Start = 1;                  % Start time (s) for plot
+End = 60;                   % End time (s) for plot
+T1 = Start*FS;              % Start time calculated from Hz
+T2 = End*FS;                % End time calculated from Hz
+c = (Start:(1/FS):End)';    % Time STEPSIZE = 1/100
+%% Plot the data;
+% *(1) first step in notebook*
+% 
+% 1st column is time data (ms)
+% 
+% 2nd column is X, medio-lateral: + left, - right
+% 
+% 3rd column is Y, vertical: + downwards, - upwards
+% 
+% 4th column is Z, anterior- posterior : + forwards, - backwards
+
+AccX = accX(T1:T2);                         % Signal over timeframe
+AccY = accY(T1:T2);                         % Signal over timeframe
+AccZ = accZ(T1:T2);                         % Signal over timeframe
+
+figure(1);
+plot(c,AccX,c,AccY,c,AccZ);                % Plot signal over timeframe
+title('acc signal not filtered - First Minute')
+xlabel('Time (s)');
+ylabel('acceleration (g)');
+legend('X - ML','Y - Vertical','Z - AP')
+%% 
+%% 
+%% Calculate parameters;
+% calculate only for the first participant;
+
+inputData = AccData;
+WindowLength = FS*10;               % why FS*10? 
+ApplyRealignment = true;            % reorder data to  1 = V; 2= ML, 3 = AP
+ApplyRemoveSteps = false;           % if true - removes first 30 and last 30 steps
+[ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLength,ApplyRealignment,ApplyRemoveSteps)
+%% 
+% *output:*
+% 
+% - NaN GaitSymm_V: 
+%% 
+% * Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi 
+% M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in                         
+% transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 
+% 7:4
+%% 
+% - SampEn has two advantages over ApEn: data length independence and a relatively 
+% trouble-free implementation.
+% 
+% - Some Settings ResulStruct
+% 
+% *IgnoreMinMaxStrides = 0.10;*                   % Number or percentage of 
+% highest&lowest values ignored for improved variability estimation
+% 
+% *N_Harm = 12;*                                             % Number of harmonics 
+% used for harmonic ratio, index of harmonicity and phase fluctuation
+% 
+% *Lyap_m = 7;*                                                % Embedding dimension 
+% (used in Lyapunov estimations)
+% 
+% *Lyap_FitWinLen* = round(60/100*FS);       % Fitting window length (used in 
+% Lyapunov estimations Rosenstein's method)
+% 
+% *Sen_m = 5;*                                                 % Dimension, 
+% the length of the subseries to be matched (used in sample entropy estimation)
+% 
+% *Sen_r = 0.3;*                                                % Tolerance, 
+% the maximum distance between two samples to qualify as match, relative to std 
+% of DataIn (used in sample entropy estimation)
+% Index of harmonicity (Lamoth et al. 2002)
+% by means of a discrete Fourier transform (DFT). The peak power at the first 
+% six harmonics was estimated and, subsequently, the index of harmonicity was 
+% defined as ; *FORMULA*
+% 
+% where P0 is the power spectral density of the fundamental frequency (first 
+% harmonic) and $ Pi the cumulative sum of power spectral density of the fundamental 
+% frequency and the first five superharmonics. A power ratio of 1 indicates that 
+% the rotation of the pelvis or the thorax is perfectly harmonic. In view of possible 
+% drift, which could lead to missing or widening peaks, the power spectral density 
+% of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of 
+% the peak frequency value. All power spectral densities were normalized by dividing 
+% the power by the sum of the total power spectrum, which equals the variance.
+% Lyapunov exponents (Wolfs vs. Rosenstein)
+% The W-algorithm is advocated for use when examining local dynamic stability 
+% with small gait data sets.
+%% Visualize step detection;
+% function [ResultStruct] = GaitVariabilityAnalysisIH_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);
+% 
+% *script for analysing straight parts*
+% 
+% (1) Realign Data
+
+%% Realign data
+data = inputData(:, [3,2,4]); % reorder data to  1 = V; 2= ML, 3 = AP
+
+%Realign sensor data to VT-ML-AP frame
+if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
+    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
+    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
+    dataAcc = RealignedAcc;
+end
+
+%% 
+% (2) Filter Data
+
+
+%% Filter data strongly & Determine location of steps
+
+% Filter  data
+[B,A] = butter(2,3/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly
+dataStepDetection = filtfilt(B,A,dataAcc);
+
+%% 
+% (3) Determine Location of steps
+
+% Determine steps;
+
+%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%%
+
+% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html
+% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,
+% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,
+% such as steps taken while walking, regardless of device orientation.
+
+magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));
+magNoGfilt = magfilt - mean(magfilt);
+minPeakHeight2 = std(magNoGfilt);
+[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2); % for step detection
+numStepsOption2_filt = numel(pks); % counts number of steps;
+%% 
+% (4) Determine location of turns
+
+%% Determine locations of turns;
+
+diffLocs = diff(locs); % calculates difference in step location
+avg_diffLocs = mean(diffLocs); % average distance between steps
+std_diffLocs = std(diffLocs); % standard deviation of distance between steps
+
+figure(2);
+findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',5); % these values have been chosen based on visual inspection of the signal
+line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])
+[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',5);
+locsTurns =  [locs(locs_diffLocs),  locs(locs_diffLocs+1)];
+
+%% 
+% (5) Visualize turns
+
+%% Visualizing turns
+
+% Duplying signal + visualing
+% to make second signal with the locations of the turns filled with NaN, so
+% that both signals can be plotted above each other in a different colour
+
+magNoGfilt_copy = magNoGfilt;
+for k = 1: size(locsTurns,1);
+    magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
+end
+
+
+% visualising signal;
+figure;
+subplot(2,1,1)
+hold on;
+plot(magNoGfilt,'b')
+plot(magNoGfilt_copy, 'r');
+title('Inside Straight: Filtered data with turns highlighted in blue')
+hold off;
+
+%% 
+% (6) CALCULATIONS
+
+%% Calculation
+% VRAAG LAURENS zie blauwe blaadje
+
+startPos = 1;
+for i = 1: size(locsTurns,1);
+    endPos = locsTurns(i,1)-1;
+    
+    inputData = dataAcc(startPos:endPos,:);
+    WindowLen = size(inputData,1);
+    ApplyRealignment = false;
+    [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); % Naam van deze moet nog aangepast.
+    
+    if i ==1 % only the firs time
+        Parameters = fieldnames(ResultStruct);
+        NrParameters = length(Parameters);
+    end
+    
+    for j = 1:NrParameters % only works if for every bin we get the same outcomes (which is the case in this script)
+        DataStraight.([char(Parameters(j))])(i) = ResultStruct.([char(Parameters(j))]);
+    end
+    startPos = locsTurns(i,2)+1;
+    
+end
+
+clear ResultStruct;
+
+% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside
+% straight;
+
+for j = 1:NrParameters;
+    ResultStruct.([char(Parameters(j))]) = nanmean(DataStraight.([char(Parameters(j))]))
+end
+%% AggregateFunction;
+##### SOURCE END #####
+--></body></html>
\ No newline at end of file
diff --git a/GaitVariabilityAnalysisLD.mlx b/GaitVariabilityAnalysisLD.mlx
new file mode 100644
index 0000000..edd5a70
Binary files /dev/null and b/GaitVariabilityAnalysisLD.mlx differ
diff --git a/GaitVariabilityAnalysisLD.tex b/GaitVariabilityAnalysisLD.tex
new file mode 100644
index 0000000..9b5130d
--- /dev/null
+++ b/GaitVariabilityAnalysisLD.tex
@@ -0,0 +1,1944 @@
+% This LaTeX was auto-generated from MATLAB code.
+% To make changes, update the MATLAB code and export to LaTeX again.
+
+\documentclass{article}
+
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{lmodern}
+\usepackage{graphicx}
+\usepackage{color}
+\usepackage{hyperref}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{epstopdf}
+\usepackage[table]{xcolor}
+\usepackage{matlab}
+
+\sloppy
+\epstopdfsetup{outdir=./}
+\graphicspath{ {./GaitVariabilityAnalysisLD_images/} }
+
+\matlabhastoc
+
+\begin{document}
+
+\label{T_D37184EE}
+\matlabtitle{Gait Variability Analysis CLBP}
+
+\matlabtableofcontents{Table of Contents}
+\begin{matlabcode}
+% Gait Variability Analysis 
+% Script created for MAP 2020-2021
+% adapted from Claudine Lamoth and Iris Hagoort
+% version1 October 2020
+
+% Input: needs mat file which contains all raw accelerometer data
+% Input: needs excel file containing the participant information including
+% leg length.
+
+
+\end{matlabcode}
+
+
+\label{H_7D86BAF1}
+\matlabheading{Clear and close;}
+
+\begin{matlabcode}
+clear;
+close all;
+\end{matlabcode}
+
+
+\label{H_82614DF8}
+\matlabheading{Load data;}
+
+\begin{par}
+\begin{flushleft}
+Select 1 trial. \textbf{For loop to import all data will be used at a later stage}
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+[FNaam,FilePad] = uigetfile('*.xls','Load phyphox data...');
+filename =[FilePad FNaam];
+PhyphoxData = xlsread(filename)
+\end{matlabcode}
+\begin{matlaboutput}
+PhyphoxData = 24092x4    
+         0    0.4018   -8.5041    4.8779
+    0.0100    0.3962   -8.4703    4.7944
+    0.0199    0.4335   -8.4378    4.7159
+    0.0299    0.5209   -8.3889    4.6266
+    0.0399    0.6495   -8.3796    4.5437
+    0.0498    0.7528   -8.3817    4.4288
+    0.0598    0.8820   -8.3622    4.3134
+    0.0697    0.9841   -8.4321    4.2221
+    0.0797    1.1041   -8.5237    4.1916
+    0.0897    1.1959   -8.5418    4.1310
+
+\end{matlaboutput}
+\begin{matlabcode}
+
+%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData
+%load('ExcelInfo.mat');
+%Participants = fields(AccData);
+\end{matlabcode}
+
+
+\label{H_04FF0795}
+\matlabheading{Settings;}
+
+\begin{matlabcode}
+LegLength = 98 % LegLength info not available!
+\end{matlabcode}
+\begin{matlaboutput}
+LegLength = 98
+\end{matlaboutput}
+\begin{matlabcode}
+%LegLengths = excel.data.GeneralInformation(:,5); % leglength info is in 5th column
+LegLengthsM = LegLength/100;           % convert to m
+t1 = length(PhyphoxData(:,1));         % Number of Samples
+
+
+FS = 100;                                  % sample frequency
+Time_ms = PhyphoxData(:,1);
+accX = PhyphoxData(:,2);
+accY = PhyphoxData(:,3);
+accZ = PhyphoxData(:,4);
+AccData = (PhyphoxData(:,[1 2 3 4]));      % matrix with accelerometer data
+
+Start = 1;                  % Start time (s) for plot
+End = 60;                   % End time (s) for plot
+T1 = Start*FS;              % Start time calculated from Hz
+T2 = End*FS;                % End time calculated from Hz
+c = (Start:(1/FS):End)';    % Time STEPSIZE = 1/100
+\end{matlabcode}
+
+
+\label{H_A9CB60AB}
+\matlabheading{Plot the data;}
+
+\begin{par}
+\begin{flushleft}
+\textbf{(1) first step in notebook}
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+1st column is time data (ms)
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+2nd column is X, medio-lateral: + left, - right
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+3rd column is Y, vertical: + downwards, - upwards
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+4th column is Z, anterior- posterior : + forwards, - backwards
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+AccX = accX(T1:T2);                         % Signal over timeframe
+AccY = accY(T1:T2);                         % Signal over timeframe
+AccZ = accZ(T1:T2);                         % Signal over timeframe
+
+figure(1);
+plot(c,AccX,c,AccY,c,AccZ);                % Plot signal over timeframe
+title('acc signal not filtered - First Minute')
+xlabel('Time (s)');
+ylabel('acceleration (g)');
+legend('X - ML','Y - Vertical','Z - AP')
+\end{matlabcode}
+\begin{center}
+\includegraphics[width=\maxwidth{56.196688409433015em}]{figure_0.eps}
+\end{center}
+
+\label{H_77261343}
+\matlabheading{\includegraphics[width=\maxwidth{37.431008529854495em}]{image_0}}
+
+\label{H_4A6AF4FC}
+\vspace{1em}
+
+\label{H_CA962223}
+\matlabheading{Calculate parameters;}
+
+\begin{par}
+\begin{flushleft}
+calculate only for the first participant;
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+inputData = AccData;
+WindowLength = FS*10;               % why FS*10? 
+ApplyRealignment = true;            % reorder data to  1 = V; 2= ML, 3 = AP
+ApplyRemoveSteps = false;           % if true - removes first 30 and last 30 steps
+[ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLength,ApplyRealignment,ApplyRemoveSteps)
+\end{matlabcode}
+\begin{matlaboutput}
+ResultStruct = 
+                    StrideRegularity_V: 0.3908
+                   StrideRegularity_ML: 0.4191
+                   StrideRegularity_AP: 0.1101
+                  StrideRegularity_All: 0.3529
+           RelativeStrideVariability_V: 0.6092
+          RelativeStrideVariability_ML: 0.5809
+          RelativeStrideVariability_AP: 0.8899
+         RelativeStrideVariability_All: 0.6471
+                     StrideTimeSamples: 132
+                     StrideTimeSeconds: 1.3200
+                            GaitSymm_V: 16.8830
+                           GaitSymm_AP: NaN
+                         GaitSymmIndex: 2.8065
+                        StepLengthMean: 11.5894
+                              Distance: 1.8825e+03
+                      WalkingSpeedMean: 7.8279
+                 StrideTimeVariability: 0.1931
+                StrideSpeedVariability: 1.1210
+               StrideLengthVariability: 0.7532
+      StrideTimeVariabilityOmitOutlier: 14.7357
+     StrideSpeedVariabilityOmitOutlier: 0.8713
+    StrideLengthVariabilityOmitOutlier: 0.4511
+                    IndexHarmonicity_V: 0.6094
+                   IndexHarmonicity_ML: 0.8041
+                   IndexHarmonicity_AP: 0.9122
+                  IndexHarmonicity_All: 0.6742
+                       HarmonicRatio_V: 2.4928
+                      HarmonicRatio_ML: 2.8449
+                      HarmonicRatio_AP: 1.6364
+                      HarmonicRatioP_V: 9.1114
+                     HarmonicRatioP_ML: 14.1449
+                     HarmonicRatioP_AP: 5.6995
+                FrequencyVariability_V: 0.5234
+               FrequencyVariability_ML: 0.6435
+               FrequencyVariability_AP: 0.6281
+                       StrideFrequency: 0.7367
+                        LyapunovWolf_V: 1.3653
+                       LyapunovWolf_ML: 1.1477
+                       LyapunovWolf_AP: 1.4231
+                       LyapunovRosen_V: 1.0151
+                      LyapunovRosen_ML: 0.7871
+                      LyapunovRosen_AP: 0.9792
+                       SampleEntropy_V: 0.1999
+                      SampleEntropy_ML: 0.2537
+                      SampleEntropy_AP: 0.2710
+               LyapunovPerStrideWolf_V: 1.8534
+              LyapunovPerStrideWolf_ML: 1.5579
+              LyapunovPerStrideWolf_AP: 1.9318
+              LyapunovPerStrideRosen_V: 1.3780
+             LyapunovPerStrideRosen_ML: 1.0685
+             LyapunovPerStrideRosen_AP: 1.3292
+
+\end{matlaboutput}
+
+\begin{par}
+\begin{flushleft}
+\textbf{output:}
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+- \underline{NaN GaitSymm\_V: }
+\end{flushleft}
+\end{par}
+
+\begin{itemize}
+\setlength{\itemsep}{-1ex}
+   \item{\begin{flushleft} Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in                         transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4 \end{flushleft}}
+\end{itemize}
+
+\begin{par}
+\begin{flushleft}
+- SampEn has two advantages over ApEn: data length independence and a relatively trouble-free implementation.
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+- Some Settings ResulStruct
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{IgnoreMinMaxStrides = 0.10; }                  \% Number or percentage of highest\&lowest values ignored for improved variability estimation
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{N\_Harm = 12;     }                                        \% Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{Lyap\_m = 7;  }                                              \% Embedding dimension (used in Lyapunov estimations)
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{Lyap\_FitWinLen} = round(60/100*FS);       \% Fitting window length (used in Lyapunov estimations Rosenstein's method)
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{Sen\_m = 5;}                                                 \% Dimension, the length of the subseries to be matched (used in sample entropy estimation)
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{Sen\_r = 0.3;}                                                \% Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation)
+\end{flushleft}
+\end{par}
+
+\label{H_2EA43FAC}
+\matlabheadingtwo{Index of harmonicity (Lamoth et al. 2002)}
+
+\begin{par}
+\begin{flushleft}
+by means of a discrete Fourier transform (DFT). The peak power at the first six harmonics was estimated and, subsequently, the index of harmonicity was defined as ; \textbf{FORMULA}
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+where P0 is the power spectral density of the fundamental frequency (first harmonic) and \$ Pi the cumulative sum of power spectral density of the fundamental frequency and the first five superharmonics. A power ratio of 1 indicates that the rotation of the pelvis or the thorax is perfectly harmonic. In view of possible drift, which could lead to missing or widening peaks, the power spectral density of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of the peak frequency value. All power spectral densities were normalized by dividing the power by the sum of the total power spectrum, which equals the variance.
+\end{flushleft}
+\end{par}
+
+\label{H_3E396779}
+\matlabheadingtwo{Lyapunov exponents (Wolfs vs. Rosenstein)}
+
+\label{H_B1B85E8D}
+\begin{par}
+\begin{flushleft}
+The W-algorithm is advocated for use when examining local dynamic stability with small gait data sets.
+\end{flushleft}
+\end{par}
+
+\label{H_9FD2A70A}
+\matlabheading{Visualize step detection;}
+
+\begin{par}
+\begin{flushleft}
+function [ResultStruct] = GaitVariabilityAnalysisIH\_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+\textbf{script for analysing straight parts}
+\end{flushleft}
+\end{par}
+
+\begin{par}
+\begin{flushleft}
+(1) Realign Data
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+%% Realign data
+data = inputData(:, [3,2,4]); % reorder data to  1 = V; 2= ML, 3 = AP
+
+%Realign sensor data to VT-ML-AP frame
+if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
+    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
+    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
+    dataAcc = RealignedAcc;
+end
+
+\end{matlabcode}
+
+\begin{par}
+\begin{flushleft}
+(2) Filter Data
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+
+%% Filter data strongly & Determine location of steps
+
+% Filter  data
+[B,A] = butter(2,3/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly
+dataStepDetection = filtfilt(B,A,dataAcc);
+
+\end{matlabcode}
+
+\begin{par}
+\begin{flushleft}
+(3) Determine Location of steps
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+% Determine steps;
+
+%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%%
+
+% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html
+% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,
+% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,
+% such as steps taken while walking, regardless of device orientation.
+
+magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));
+magNoGfilt = magfilt - mean(magfilt);
+minPeakHeight2 = std(magNoGfilt);
+[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2); % for step detection
+numStepsOption2_filt = numel(pks); % counts number of steps;
+\end{matlabcode}
+
+\begin{par}
+\begin{flushleft}
+(4) Determine location of turns
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+%% Determine locations of turns;
+
+diffLocs = diff(locs); % calculates difference in step location
+avg_diffLocs = mean(diffLocs); % average distance between steps
+std_diffLocs = std(diffLocs); % standard deviation of distance between steps
+
+figure(2);
+findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',5); % these values have been chosen based on visual inspection of the signal
+line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])
+\end{matlabcode}
+\begin{center}
+\includegraphics[width=\maxwidth{56.196688409433015em}]{figure_1.eps}
+\end{center}
+\begin{matlabcode}
+[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',5);
+locsTurns =  [locs(locs_diffLocs),  locs(locs_diffLocs+1)];
+
+\end{matlabcode}
+
+\begin{par}
+\begin{flushleft}
+(5) Visualize turns
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+%% Visualizing turns
+
+% Duplying signal + visualing
+% to make second signal with the locations of the turns filled with NaN, so
+% that both signals can be plotted above each other in a different colour
+
+magNoGfilt_copy = magNoGfilt;
+for k = 1: size(locsTurns,1);
+    magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
+end
+
+
+% visualising signal;
+figure;
+subplot(2,1,1)
+hold on;
+plot(magNoGfilt,'b')
+plot(magNoGfilt_copy, 'r');
+title('Inside Straight: Filtered data with turns highlighted in blue')
+hold off;
+\end{matlabcode}
+\begin{center}
+\includegraphics[width=\maxwidth{56.196688409433015em}]{figure_2.eps}
+\end{center}
+\begin{matlabcode}
+
+\end{matlabcode}
+
+\begin{par}
+\begin{flushleft}
+(6) CALCULATIONS
+\end{flushleft}
+\end{par}
+
+\begin{matlabcode}
+%% Calculation
+% VRAAG LAURENS zie blauwe blaadje
+
+startPos = 1;
+for i = 1: size(locsTurns,1);
+    endPos = locsTurns(i,1)-1;
+    
+    inputData = dataAcc(startPos:endPos,:);
+    WindowLen = size(inputData,1);
+    ApplyRealignment = false;
+    [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); % Naam van deze moet nog aangepast.
+    
+    if i ==1 % only the firs time
+        Parameters = fieldnames(ResultStruct);
+        NrParameters = length(Parameters);
+    end
+    
+    for j = 1:NrParameters % only works if for every bin we get the same outcomes (which is the case in this script)
+        DataStraight.([char(Parameters(j))])(i) = ResultStruct.([char(Parameters(j))]);
+    end
+    startPos = locsTurns(i,2)+1;
+    
+end
+
+clear ResultStruct;
+
+% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside
+% straight;
+
+for j = 1:NrParameters;
+    ResultStruct.([char(Parameters(j))]) = nanmean(DataStraight.([char(Parameters(j))]))
+end
+\end{matlabcode}
+\begin{matlaboutput}
+ResultStruct = 
+    StrideRegularity_V: 0.9005
+
+ResultStruct = 
+     StrideRegularity_V: 0.9005
+    StrideRegularity_ML: 0.7110
+
+ResultStruct = 
+     StrideRegularity_V: 0.9005
+    StrideRegularity_ML: 0.7110
+    StrideRegularity_AP: 0.6643
+
+ResultStruct = 
+      StrideRegularity_V: 0.9005
+     StrideRegularity_ML: 0.7110
+     StrideRegularity_AP: 0.6643
+    StrideRegularity_All: 0.7765
+
+ResultStruct = 
+             StrideRegularity_V: 0.9005
+            StrideRegularity_ML: 0.7110
+            StrideRegularity_AP: 0.6643
+           StrideRegularity_All: 0.7765
+    RelativeStrideVariability_V: 0.0995
+
+ResultStruct = 
+              StrideRegularity_V: 0.9005
+             StrideRegularity_ML: 0.7110
+             StrideRegularity_AP: 0.6643
+            StrideRegularity_All: 0.7765
+     RelativeStrideVariability_V: 0.0995
+    RelativeStrideVariability_ML: 0.2890
+
+ResultStruct = 
+              StrideRegularity_V: 0.9005
+             StrideRegularity_ML: 0.7110
+             StrideRegularity_AP: 0.6643
+            StrideRegularity_All: 0.7765
+     RelativeStrideVariability_V: 0.0995
+    RelativeStrideVariability_ML: 0.2890
+    RelativeStrideVariability_AP: 0.3357
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+            StrideTimeVariability: 0.0200
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+            StrideTimeVariability: 0.0200
+           StrideSpeedVariability: 0.1990
+
+ResultStruct = 
+               StrideRegularity_V: 0.9005
+              StrideRegularity_ML: 0.7110
+              StrideRegularity_AP: 0.6643
+             StrideRegularity_All: 0.7765
+      RelativeStrideVariability_V: 0.0995
+     RelativeStrideVariability_ML: 0.2890
+     RelativeStrideVariability_AP: 0.3357
+    RelativeStrideVariability_All: 0.2235
+                StrideTimeSamples: 217.5556
+                StrideTimeSeconds: 2.1756
+                       GaitSymm_V: 5.7952
+                      GaitSymm_AP: 7.0128
+                    GaitSymmIndex: 2.3916
+                   StepLengthMean: 11.9893
+                         Distance: 83.0819
+                 WalkingSpeedMean: 6.1785
+            StrideTimeVariability: 0.0200
+           StrideSpeedVariability: 0.1990
+          StrideLengthVariability: 0.5502
+
+ResultStruct = 
+                  StrideRegularity_V: 0.9005
+                 StrideRegularity_ML: 0.7110
+                 StrideRegularity_AP: 0.6643
+                StrideRegularity_All: 0.7765
+         RelativeStrideVariability_V: 0.0995
+        RelativeStrideVariability_ML: 0.2890
+        RelativeStrideVariability_AP: 0.3357
+       RelativeStrideVariability_All: 0.2235
+                   StrideTimeSamples: 217.5556
+                   StrideTimeSeconds: 2.1756
+                          GaitSymm_V: 5.7952
+                         GaitSymm_AP: 7.0128
+                       GaitSymmIndex: 2.3916
+                      StepLengthMean: 11.9893
+                            Distance: 83.0819
+                    WalkingSpeedMean: 6.1785
+               StrideTimeVariability: 0.0200
+              StrideSpeedVariability: 0.1990
+             StrideLengthVariability: 0.5502
+    StrideTimeVariabilityOmitOutlier: 6.0071
+
+ResultStruct = 
+                   StrideRegularity_V: 0.9005
+                  StrideRegularity_ML: 0.7110
+                  StrideRegularity_AP: 0.6643
+                 StrideRegularity_All: 0.7765
+          RelativeStrideVariability_V: 0.0995
+         RelativeStrideVariability_ML: 0.2890
+         RelativeStrideVariability_AP: 0.3357
+        RelativeStrideVariability_All: 0.2235
+                    StrideTimeSamples: 217.5556
+                    StrideTimeSeconds: 2.1756
+                           GaitSymm_V: 5.7952
+                          GaitSymm_AP: 7.0128
+                        GaitSymmIndex: 2.3916
+                       StepLengthMean: 11.9893
+                             Distance: 83.0819
+                     WalkingSpeedMean: 6.1785
+                StrideTimeVariability: 0.0200
+               StrideSpeedVariability: 0.1990
+              StrideLengthVariability: 0.5502
+     StrideTimeVariabilityOmitOutlier: 6.0071
+    StrideSpeedVariabilityOmitOutlier: 0.2542
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+                      HarmonicRatio_AP: 2.5141
+                      HarmonicRatioP_V: 21.1829
+                     HarmonicRatioP_ML: 19.5829
+                     HarmonicRatioP_AP: 12.5052
+                FrequencyVariability_V: 0.2160
+               FrequencyVariability_ML: 0.4381
+               FrequencyVariability_AP: 0.1981
+                       StrideFrequency: 0.6364
+                        LyapunovWolf_V: 1.4401
+                       LyapunovWolf_ML: 1.2904
+                       LyapunovWolf_AP: 1.5238
+                       LyapunovRosen_V: 0.9931
+                      LyapunovRosen_ML: 0.8064
+                      LyapunovRosen_AP: 1.0984
+
+ResultStruct = 
+                    StrideRegularity_V: 0.9005
+                   StrideRegularity_ML: 0.7110
+                   StrideRegularity_AP: 0.6643
+                  StrideRegularity_All: 0.7765
+           RelativeStrideVariability_V: 0.0995
+          RelativeStrideVariability_ML: 0.2890
+          RelativeStrideVariability_AP: 0.3357
+         RelativeStrideVariability_All: 0.2235
+                     StrideTimeSamples: 217.5556
+                     StrideTimeSeconds: 2.1756
+                            GaitSymm_V: 5.7952
+                           GaitSymm_AP: 7.0128
+                         GaitSymmIndex: 2.3916
+                        StepLengthMean: 11.9893
+                              Distance: 83.0819
+                      WalkingSpeedMean: 6.1785
+                 StrideTimeVariability: 0.0200
+                StrideSpeedVariability: 0.1990
+               StrideLengthVariability: 0.5502
+      StrideTimeVariabilityOmitOutlier: 6.0071
+     StrideSpeedVariabilityOmitOutlier: 0.2542
+    StrideLengthVariabilityOmitOutlier: 0.3722
+                    IndexHarmonicity_V: 0.4981
+                   IndexHarmonicity_ML: 0.7579
+                   IndexHarmonicity_AP: 0.8968
+                  IndexHarmonicity_All: 0.6349
+                       HarmonicRatio_V: 3.6118
+                      HarmonicRatio_ML: 3.4148
+