109 lines
3.9 KiB
Matlab
109 lines
3.9 KiB
Matlab
function [ResultStruct] = GaitVariabilityAnalysisIH_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);
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% SCRIPT FOR ANAlysis straight parts
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% NOG GOEDE BESCHRIJVING TOEVOEGEN.
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%% Realign data
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data = inputData(:, [3,2,4]); % reorder data to 1 = V; 2= ML, 3 = AP
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%Realign sensor data to VT-ML-AP frame
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if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
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% Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
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[RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
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dataAcc = RealignedAcc;
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end
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%% Filter data strongly & Determine location of steps
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% Filter data
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[B,A] = butter(2,3/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly
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dataStepDetection = filtfilt(B,A,dataAcc);
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% Determine steps;
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%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%%
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% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html
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% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,
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% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,
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% such as steps taken while walking, regardless of device orientation.
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magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));
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magNoGfilt = magfilt - mean(magfilt);
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minPeakHeight2 = std(magNoGfilt);
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[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2); % for step detection
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numStepsOption2_filt = numel(pks); % counts number of steps;
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%%%%%%%%%%%%%%%%%%%%%%%% TOT HIER %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Determine locations of turns;
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diffLocs = diff(locs); % calculates difference in step location
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avg_diffLocs = mean(diffLocs); % average distance between steps
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std_diffLocs = std(diffLocs); % standard deviation of distance between steps
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figure;
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findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',5); % these values have been chosen based on visual inspection of the signal
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line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])
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[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',5);
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locsTurns = [locs(locs_diffLocs), locs(locs_diffLocs+1)];
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%% Visualizing turns
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% Duplying signal + visualing
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% to make second signal with the locations of the turns filled with NaN, so
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% that both signals can be plotted above each other in a different colour
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magNoGfilt_copy = magNoGfilt;
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for k = 1: size(locsTurns,1);
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magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
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end
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% visualising signal;
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figure;
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subplot(2,1,1)
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hold on;
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plot(magNoGfilt,'b')
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plot(magNoGfilt_copy, 'r');
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title('Inside Straight: Filtered data with turns highlighted in blue')
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hold off;
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%% Calculation
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% VRAAG LAURENS zie blauwe blaadje
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startPos = 1;
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for i = 1: size(locsTurns,1);
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endPos = locsTurns(i,1)-1;
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inputData = dataAcc(startPos:endPos,:);
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WindowLen = size(inputData,1);
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ApplyRealignment = false;
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[ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); % Naam van deze moet nog aangepast.
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if i ==1 % only the firs time
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Parameters = fieldnames(ResultStruct);
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NrParameters = length(Parameters);
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end
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for j = 1:NrParameters % only works if for every bin we get the same outcomes (which is the case in this script)
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DataStraight.([char(Parameters(j))])(i) = ResultStruct.([char(Parameters(j))]);
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end
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startPos = locsTurns(i,2)+1;
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end
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clear ResultStruct;
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% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside
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% straight;
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for j = 1:NrParameters;
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ResultStruct.([char(Parameters(j))]) = nanmean(DataStraight.([char(Parameters(j))]))
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end
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end
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