35 lines
1.4 KiB
Mathematica
35 lines
1.4 KiB
Mathematica
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function [dataAccCut,dataAccCut_filt,StrideTimeSamples] = StepDetectionFunc(FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange)
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%% Step detecection
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% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed
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ResultStruct = struct();
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if ApplyRemoveSteps
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% In order to run the step detection script we first need to run an autocorrelation function;
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[ResultStruct] = AutocorrStrides(dataAcc_filt,FS,StrideTimeRange,ResultStruct);
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% StrideTimeSamples is needed as an input for the stepcountFunc;
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StrideTimeSamples = ResultStruct.StrideTimeSamples;
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% Calculate the number of steps;
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[PksAndLocsCorrected] = StepcountFunc(dataAcc_filt,StrideTimeSamples,FS);
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% This function selects steps based on negative and positive values.
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% However to determine the steps correctly we only need one of these;
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LocsSteps = PksAndLocsCorrected(1:2:end,2);
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%% Cut data & remove currents results
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% Remove 20 steps in the beginning and end of data
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dataAccCut = dataAcc(LocsSteps(31):LocsSteps(end-30),:);
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dataAccCut_filt = dataAcc_filt(LocsSteps(31):LocsSteps(end-30),:);
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% Clear currently saved results from Autocorrelation Analysis
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clear ResultStruct;
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clear PksAndLocsCorrected;
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clear LocsSteps;
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else;
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dataAccCut = dataAcc;
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dataAccCut_filt = dataAcc_filt;
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end
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