This GIT respository contains all files needed for an adequate analysis of the gait (6MWT) accelerometer data.
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function [dataAccCut,dataAccCut_filt,StrideTimeSamples] = StepDetectionFunc(FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange)
%% Step detecection
% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed
ResultStruct = struct();
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