This GIT respository contains all files needed for an adequate analysis of the gait (6MWT) accelerometer data.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

83 lines
3.9 KiB

function [ResultStruct] = HarmonicityFrequency(dataAccCut_filt,P,F, StrideFrequency,dF,LowFrequentPowerThresholds,N_Harm,FS,AccVectorLen,ResultStruct)
% Add sum of power spectra (as a rotation-invariant spectrum)
P = [P,sum(P,2)];
PS = sqrt(P);
% Calculate the measures for the power per separate dimension
for i=1:size(P,2);
% Relative cumulative power and frequencies that correspond to these cumulative powers
PCumRel = cumsum(P(:,i))/sum(P(:,i));
PSCumRel = cumsum(PS(:,i))/sum(PS(:,i));
FCumRel = F+0.5*dF;
% Derive relative cumulative power for threshold frequencies
Nfreqs = size(LowFrequentPowerThresholds,2);
LowFrequentPercentage(i,1:Nfreqs) = interp1(FCumRel,PCumRel,LowFrequentPowerThresholds)*100;
% Calculate relative power of first 10 harmonics, taking the power of each harmonic with a band of + and - 10% of the first
% harmonic around it
PHarm = zeros(N_Harm,1);
PSHarm = zeros(N_Harm,1);
for Harm = 1:N_Harm,
FHarmRange = (Harm+[-0.1 0.1])*StrideFrequency;
PHarm(Harm) = diff(interp1(FCumRel,PCumRel,FHarmRange));
PSHarm(Harm) = diff(interp1(FCumRel,PSCumRel,FHarmRange));
end
% Derive index of harmonicity
if i == 2 % for ML we expect odd instead of even harmonics
IndexHarmonicity(i) = PHarm(1)/sum(PHarm(1:2:12));
elseif i == 4
IndexHarmonicity(i) = sum(PHarm(1:2))/sum(PHarm(1:12));
else
IndexHarmonicity(i) = PHarm(2)/sum(PHarm(2:2:12));
end
% Calculate the phase speed fluctuations
StrideFreqFluctuation = nan(N_Harm,1);
StrSamples = round(FS/StrideFrequency);
for h=1:N_Harm,
CutOffs = [StrideFrequency*(h-(1/3)) , StrideFrequency*(h+(1/3))]/(FS/2);
if all(CutOffs<1) % for Stride frequencies above FS/20/2, the highest harmonics are not represented in the power spectrum
[b,a] = butter(2,CutOffs);
if i==4 % Take the vector length as a rotation-invariant signal
AccFilt = filtfilt(b,a,AccVectorLen);
else
AccFilt = filtfilt(b,a,dataAccCut_filt(:,i));
end
Phase = unwrap(angle(hilbert(AccFilt)));
SmoothPhase = sgolayfilt(Phase,1,2*(floor(FS/StrideFrequency/2))+1); % This is in fact identical to a boxcar filter with linear extrapolation at the edges
StrideFreqFluctuation(h) = std(Phase(1+StrSamples:end,1)-Phase(1:end-StrSamples,1));
end
end
FrequencyVariability(i) = nansum(StrideFreqFluctuation./(1:N_Harm)'.*PHarm)/nansum(PHarm);
if i<4,
% Derive harmonic ratio (two variants)
if i == 2 % for ML we expect odd instead of even harmonics
HarmonicRatio(i) = sum(PSHarm(1:2:end-1))/sum(PSHarm(2:2:end)); % relative to summed 3d spectrum
HarmonicRatioP(i) = sum(PHarm(1:2:end-1))/sum(PHarm(2:2:end)); % relative to own spectrum
else
HarmonicRatio(i) = sum(PSHarm(2:2:end))/sum(PSHarm(1:2:end-1));
HarmonicRatioP(i) = sum(PHarm(2:2:end))/sum(PHarm(1:2:end-1));
end
end
end
ResultStruct.IndexHarmonicity_V = IndexHarmonicity(1); % higher smoother more regular patter
ResultStruct.IndexHarmonicity_ML = IndexHarmonicity(2); % higher smoother more regular patter
ResultStruct.IndexHarmonicity_AP = IndexHarmonicity(3); % higher smoother more regular patter
ResultStruct.IndexHarmonicity_All = IndexHarmonicity(4);
ResultStruct.HarmonicRatio_V = HarmonicRatio(1);
ResultStruct.HarmonicRatio_ML = HarmonicRatio(2);
ResultStruct.HarmonicRatio_AP = HarmonicRatio(3);
ResultStruct.HarmonicRatioP_V = HarmonicRatioP(1);
ResultStruct.HarmonicRatioP_ML = HarmonicRatioP(2);
ResultStruct.HarmonicRatioP_AP = HarmonicRatioP(3);
ResultStruct.FrequencyVariability_V = FrequencyVariability(1);
ResultStruct.FrequencyVariability_ML = FrequencyVariability(2);
ResultStruct.FrequencyVariability_AP = FrequencyVariability(3);
ResultStruct.StrideFrequency = StrideFrequency;