89 lines
4.6 KiB
Matlab
89 lines
4.6 KiB
Matlab
function [locsTurns,FilteredData,FootContacts] = DetermineLocationTurnsFunc(inputData,FS,ApplyRealignment,plotit,Distance,ResultStruct)
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% Description: Determine the location of turns, plot for visual inspection
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% Input: Acc Data (in previous stage realigned?)
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% Realigned sensor data to VT-ML-AP frame
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if ApplyRealignment % apply realignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
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data = inputData(:,[3,2,4]); % reorder data to 1 = V; 2 = ML; 3 = AP
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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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else % realignment/detrend performed in earlier stage!
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data = inputData;
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dataAcc = inputData;
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end
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% Filter data
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[B,A] = butter(2,20/(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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VTAcc = data(:,1);
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APAcc = data(:,3);
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%% Determine Steps -
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% USE THE AP ACCELERATION: WIEBREN ZIJLSTRA: ASSESSMENT OF SPATIO-TEMPORAL PARAMTERS DURING UNCONSTRAINED WALKING (2004)
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% In order to run the step detection script we first need to run an autocorrelation function;
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StrideTimeRange = [0.2 4.0]; % Range to search for stride time (seconds)
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[ResultStruct] = AutocorrStrides(dataStepDetection,FS, StrideTimeRange,ResultStruct); % first guess of stridetimesamples
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% StrideTimeSamples is needed as an input for the stepcountFunc;
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StrideTimeSamples = ResultStruct.StrideTimeSamples;
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[PksAndLocsCorrected] = StepcountFunc(data,StrideTimeSamples,FS);
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FootContacts = PksAndLocsCorrected; % previous version LD: (1:2:end,2);
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numStepsOption2_filt = numel(FootContacts); % counts number of steps;
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%% Determine Turns
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% 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 = 1*std(magNoGfilt); % used to be 2 % based on visual inspection, parameter tuning was performed on *X*standard deviation from MInPeak (used to be 1 SD)
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[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2,'MINPEAKDISTANCE',0.4*FS); % for step detection
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numStepsOption2_filt = numel(pks); % counts number of locs for turn detection
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diffLocs = diff(locs); % calculates difference in location magnitude differences
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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',0.2*FS); % 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',0.2*FS); % values were initially 5
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locsTurns = [locs(locs_diffLocs), locs(locs_diffLocs+1)];
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%magNoGfilt_copy = magNoGfilt;
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VTAcc_copy = data(:,1);
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APAcc_copy = data(:,3);
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for k = 1: size(locsTurns,1);
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VTAcc_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
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APAcc_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
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end
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% Visualising signal;
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if plotit
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figure()
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subplot(2,1,1)
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hold on;
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plot(VTAcc,'b')
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plot(VTAcc_copy, 'r');
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title('Inside Straight: Filtered data VT with turns highlighted in blue')
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line([6000,12000,18000,24000,30000,36000;6000,12000,18000,24000,30000,36000],[-7,-7,-7,-7,-7,-7;7,7,7,7,7,7],'LineWidth',2,'Linestyle','--','color','k')
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hold on;
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subplot(2,1,2)
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plot(APAcc,'g')
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plot(APAcc_copy, 'r');
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title('Inside Straight: Filtered data AP with turns highlighted in blue')
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line([6000,12000,18000,24000,30000,36000;6000,12000,18000,24000,30000,36000],[-7,-7,-7,-7,-7,-7;7,7,7,7,7,7],'LineWidth',2,'Linestyle','--','color','k')
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hold off;
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end
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% Check if number of turns * 20 m are making sense based on total
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% distance measured by researcher.
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disp(['Number of turns detected = ' num2str(size(locsTurns,1))])
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disp(['Total distance measured by researcher was = ' num2str(Distance)])
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FilteredData = dataAcc;
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end |