ApplyRealignment changed to data of controls (order) - Variability outcomes were calculated for Controls (in seperate loops) --> outcome measures in one struct; OutcomesAcc.

DetermineLocationTurnsFunc.m

@@ -25,9 +25,9 @@ minPeakHeight2 = 1.2*std(magNoGfilt);                                   % based
 [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
+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

GaitOutcomesTrunkAccFuncIH.m

@@ -65,9 +65,9 @@ if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M,
     dataAcc = RealignedAcc;
     [B,A] = butter(2,20/(FS/2),'low');
     dataAcc_filt = filtfilt(B,A,dataAcc);
-else % we asume tat data is already reorderd to 1 = V; 2= ML, 3 = AP in an earlier stage;
+else % we asume that data for CONTROLS is already detrended and in order 1 = AP, 2 = ML, 3 = VT in an earlier stage;
     [B,A] = butter(2,20/(FS/2),'low');
-    dataAcc = inputData;
+    dataAcc = inputData(:, [3,2,1]);
     dataAcc_filt = filtfilt(B,A,dataAcc);
 end
 
@@ -101,6 +101,7 @@ if ApplyRemoveSteps
     clear LocsSteps;
     
     % Change window length if ApplyRemoveSteps (16-2-2021 LD)
+    
     WindowLen = size(dataAccCut,1);
     
 else;

resources/project/uuid-397832ba-e50d-439c-bd33-8c866c35eb59.xml

@@ -0,0 +1,2 @@
+<?xml version='1.0' encoding='UTF-8'?>
+<Info />