'README.md' updaten

DetermineLocationTurnsFunc.m

@@ -1,68 +0,0 @@
-function [locsTurns,FilteredData] = DetermineLocationTurnsFunc(inputData,FS,ApplyRealignment,plotit,Distance)
-% Description: Determine the location of turns, plot for visual inspection
-
-% Input: Acc Data (not yet realigned)
-
-%Realign sensor data to VT-ML-AP frame
-
-if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
-    data = inputData(:,[3,2,4]); % reorder data to 1 = V; 2 = ML; 3 = AP
-    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
-    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
-    dataAcc = RealignedAcc;
-end
-% Filter data
-[B,A] = butter(2,3/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly
-dataStepDetection = filtfilt(B,A,dataAcc);
-% Determine steps
-% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html
-% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,
-% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,
-% such as steps taken while walking, regardless of device orientation.
-magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));
-magNoGfilt = magfilt - mean(magfilt);
-minPeakHeight2 = 1.2*std(magNoGfilt);                                   % based on visual inspection, parameter tuning was performed on standard deviation from MInPeak (used to be 1 SD)
-[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2);   % for step detection
-numStepsOption2_filt = numel(pks);                                      % counts number of steps;
-
-diffLocs = diff(locs); % calculates difference in step location
-avg_diffLocs = mean(diffLocs); % average distance between steps
-std_diffLocs = std(diffLocs); % standard deviation of distance between steps
-
-figure;
-findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',9);                                    % these values have been chosen based on visual inspection of the signal
-line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])
-[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',10);    % values were initially 5
-locsTurns =  [locs(locs_diffLocs),  locs(locs_diffLocs+1)];
-
-magNoGfilt_copy = magNoGfilt;
-for k = 1: size(locsTurns,1);
-    magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
-end
-
-% Visualising signal;
-if plotit
-    figure;
-    subplot(2,1,1)
-    hold on;
-    plot(magNoGfilt,'b')
-    plot(magNoGfilt_copy, 'r');
-    title('Inside Straight: Filtered data with turns highlighted in blue')
-    line([6000,12000,18000,24000,30000,36000;6000,12000,18000,24000,30000,36000],[-4,-4,-4,-4,-4,-4;4,4,4,4,4,4],'LineWidth',2,'Linestyle','--','color','k')
-%     hold on;
-%     for m = 1:size(locsTurns,1)
-%         plot(locsTurns(m),DataStraight.([char(Participants(i))]).LyapunovPerStrideRosen_ML(:,m),'--gs',...
-%         'LineWidth',2,...
-%         'MarkerSize',10,...
-%         'MarkerEdgeColor','g',...
-%         'MarkerFaceColor',[0.5,0.5,0.5])
-%     end
-    hold off;
-end
-% Check if number of turns * 20 m are making sense based on total
-% distance measured by researcher.
-disp(['Number of turns detected = ' num2str(size(locsTurns,1))])
-disp(['Total distance measured by researcher was = ' num2str(Distance)])
-
-FilteredData = dataAcc;
-end

GaitOutcomesTrunkAccFuncIH.m

@@ -2,7 +2,7 @@ function [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,Wind
 
 % DESCRIPTON: Trunk analysis of Iphone data without the need for step detection
 % CL Nov 2019
-% Adapted LD feb 2021 (IH feb 2020)
+% Adapted IH feb-april 2020
 
 % koloms data of smartphone
 % 1st column is time data;
@@ -73,7 +73,7 @@ end
 
 
 %% Step dectection
-% Determines the number of steps in the signal so that the first 1 and last step in the signal can be removed
+% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed
 
 if ApplyRemoveSteps
     
@@ -90,9 +90,9 @@ if ApplyRemoveSteps
     LocsSteps = PksAndLocsCorrected(1:2:end,2);
     
     %% Cut data & remove currents results
-    % Remove 1 step in the beginning and end of data
+    % Remove 20 steps in the beginning and end of data
-    dataAccCut = dataAcc(LocsSteps(1):LocsSteps(end-1),:);
+    dataAccCut = dataAcc(LocsSteps(31):LocsSteps(end-30),:);
-    dataAccCut_filt = dataAcc_filt(LocsSteps(1):LocsSteps(end-1),:);
+    dataAccCut_filt = dataAcc_filt(LocsSteps(31):LocsSteps(end-30),:);
     
     % Clear currently saved results from Autocorrelation Analysis
     
@@ -100,9 +100,6 @@ if ApplyRemoveSteps
     clear PksAndLocsCorrected;
     clear LocsSteps;
     
-    % Change window length if ApplyRemoveSteps (16-2-2021 LD)
-    WindowLen = size(dataAccCut,1);
-    
 else;
     dataAccCut = dataAcc;
     dataAccCut_filt = dataAcc_filt;

README.md

@@ -1,3 +1,5 @@
 # MAP_Gait_Dynamics
 
-This GIT respository contains all files needed for an adequate analysis of the gait (6MWT) accelerometer data. 
+This GIT respository contains all files needed for the analysis of the gait (6MWT) accelerometer data. 
+
+Main file (including all comments) = 'Main_GaitVariabilityAnalysis_LD'