diff --git a/AutocorrStrides.m b/AutocorrStrides.m index b49a726..4133568 100644 --- a/AutocorrStrides.m +++ b/AutocorrStrides.m @@ -4,10 +4,10 @@ function [ResultStruct] = AutocorrStrides(data,FS, StrideTimeRange,ResultStruct % Stride time and regularity from auto correlation (according to Moe-Nilssen and Helbostad, Estimation of gait cycle characteristics by trunk accelerometry. J Biomech, 2004. 37: 121-6.) RangeStart = round(FS*StrideTimeRange(1)); RangeEnd = round(FS*StrideTimeRange(2)); -[AutocorrResult,Lags]=xcov(data,RangeEnd,'unbiased'); -AutocorrSum = sum(AutocorrResult(:,[1 5 9]),2); % This sum is independent of sensor re-orientation, as long as axes are kept orthogonal -AutocorrResult2= [AutocorrResult(:,[1 5 9]),AutocorrSum]; -IXRange = (numel(Lags)-(RangeEnd-RangeStart)):numel(Lags); +[AutocorrResult,Lags]=xcov(data,RangeEnd,'unbiased'); % The unbiased alternative is produced by dividing the raw autocorrelation coefficient by the number of samples representing the overlapping part of the time series and the time-lagged replication: +AutocorrSum = sum(AutocorrResult(:,[1 5 9]),2); % This sum is independent of sensor re-orientation, as long as axes are kept orthogonal. 1: VT-VT 5:ML-ML 9:AP-AP +AutocorrResult2= [AutocorrResult(:,[1 5 9]),AutocorrSum]; % column 4 represents the sum of autocorr [1 ... 9] +IXRange = (numel(Lags)-(RangeEnd-RangeStart)):numel(Lags); % Lags from -400 / 400, IXrange: 421 - 801 % check that autocorrelations are positive for any direction, AutocorrPlusTrans = AutocorrResult+AutocorrResult(:,[1 4 7 2 5 8 3 6 9]); @@ -25,19 +25,13 @@ if isempty(IXRangeNew) else StrideTimeSamples = Lags(IXRangeNew(AutocorrSum(IXRangeNew)==max(AutocorrSum(IXRangeNew)))); - StrideRegularity = AutocorrResult2(Lags==StrideTimeSamples,:)./AutocorrResult2(Lags==0,:); % Moe-Nilssen&Helbostatt,2004 + StrideRegularity = AutocorrResult2(Lags==StrideTimeSamples,:)./AutocorrResult2(Lags==0,:); % Moe-Nilssen&Helbostatt,2004 / stride regularity was shifted to the average stride time RelativeStrideVariability = 1-StrideRegularity; StrideTimeSeconds = StrideTimeSamples/FS; ResultStruct.StrideRegularity_V = StrideRegularity(1); - ResultStruct.StrideRegularity_ML = StrideRegularity(2); ResultStruct.StrideRegularity_AP = StrideRegularity(3); - ResultStruct.StrideRegularity_All = StrideRegularity(4); - ResultStruct.RelativeStrideVariability_V = RelativeStrideVariability(1); - ResultStruct.RelativeStrideVariability_ML = RelativeStrideVariability(2); - ResultStruct.RelativeStrideVariability_AP = RelativeStrideVariability(3); - ResultStruct.RelativeStrideVariability_All = RelativeStrideVariability(4); ResultStruct.StrideTimeSamples = StrideTimeSamples; - ResultStruct.StrideTimeSeconds = StrideTimeSeconds; + %ResultStruct.StrideTimeSeconds = StrideTimeSeconds; end \ No newline at end of file diff --git a/CalcMaxLyapWolfFixedEvolv.m b/CalcMaxLyapWolfFixedEvolv.m index 8021140..c9bbd5d 100644 --- a/CalcMaxLyapWolfFixedEvolv.m +++ b/CalcMaxLyapWolfFixedEvolv.m @@ -42,10 +42,10 @@ function [L_Estimate,ExtraArgsOut] = CalcMaxLyapWolfFixedEvolv(ThisTimeSeries,FS % 23 October 2012, use fixed evolve time instead of adaptable if nargin > 2 - if isfield(ExtraArgsIn,'J') + if isfield(ExtraArgsIn,'J') % logical 0, based on MutualInformation. J=ExtraArgsIn.J; end - if isfield(ExtraArgsIn,'m') + if isfield(ExtraArgsIn,'m') % logical 1, embedding dimension of 7. m=ExtraArgsIn.m; end end @@ -75,9 +75,9 @@ if ~(nanstd(ThisTimeSeries) > 0) return; end -%% Determine J +%% Determine J (Embedding Delay as the first minimum of the average mutual information : Fraser, A.M., Swinney, H.L., 1986. Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134–1140.) if ~exist('J','var') || isempty(J) - % Calculate mutual information and take first local minimum Tau as J + % Calculate mutual information and take first local minimum Tau as J; To obtain coordinates for time delayed phase-space embedding that are as independent as possible bV = min(40,floor(sqrt(size(ThisTimeSeries,1)))); tauVmax = 70; [mutMPro,cummutMPro,minmuttauVPro] = MutualInformationHisPro(ThisTimeSeries,(0:tauVmax),bV,1); % (xV,tauV,bV,flag) @@ -113,6 +113,9 @@ end ExtraArgsOut.m=m; %% Create state space based upon J and m +% the procedure of computing the LYE involves first the calculation of a phasespace reconstruction for each acceleration time-series, +% by creating time-delayed copies of the time-series X(t) , X(t1 + tau), +% x(t2 + 2tau), ... N_ss = size(ThisTimeSeries,1)-(m-1)*J; StateSpace=nan(N_ss,m*size(ThisTimeSeries,2)); for dim=1:size(ThisTimeSeries,2), @@ -121,13 +124,13 @@ for dim=1:size(ThisTimeSeries,2), end end -%% Parameters for Lyapunov estimation -CriticalLen=J*m; -max_dist = sqrt(sum(std(StateSpace).^2))/10; -max_dist_mult = 5; -min_dist = max_dist/2; -max_theta = 0.3; -evolv = J; +%% Parameters for Lyapunov estimation % SEE: Rispens, S. M., Pijnappels, M. A. G. M., Van Dieën, J. H., Van Schooten, K. S., Beek, P. J., & Daffertshofer, A. (2014). A benchmark test of accuracy and precision in estimating dynamical systems characteristics from a time series. Journal of biomechanics, 47(2), 470-475. +CriticalLen=J*m; % After a fixed period (here the embedding delay) the nearby point is replaced. +max_dist = sqrt(sum(std(StateSpace).^2))/10; % The maximum distance to which the divergence may grow before replacing the neighbour. +max_dist_mult = 5; % Whenever points cannot be found, the distance limit is increased stepwise to maximally five times the original limit. +min_dist = max_dist/2; % The new nearby point must not be closer than 1/20 of the signal's SD to avoid noise-related artifacts. +max_theta = 0.3; % The new nearby point has to point in the same direction in state space as the old nearby point, as seen from the current reference (at least within 0.3 rad) +evolv = J; % Embedding delay (time step after which the neighbour is replaced) (approximately 10 % of gait cycle?) %% Calculate Lambda [L_Estimate]=div_wolf_fixed_evolv(StateSpace, FS, min_dist, max_dist, max_dist_mult, max_theta, CriticalLen, evolv); diff --git a/ControlsData.mat b/ControlsData.mat new file mode 100644 index 0000000..bbe2179 Binary files /dev/null and b/ControlsData.mat differ diff --git a/ControlsDetermineLocationTurnsFunc.m b/ControlsDetermineLocationTurnsFunc.m new file mode 100644 index 0000000..f2b38fa --- /dev/null +++ b/ControlsDetermineLocationTurnsFunc.m @@ -0,0 +1,91 @@ +function [locsTurns,FilteredData,FootContacts] = ControlsDetermineLocationTurnsFunc(inputData,FS,ApplyRealignment,plotit,ResultStruct) +% 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(:,[3,2,1]); % NOT THE SAME AS IN THE CLBP DATA; 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; +else + data = inputData; % Realignment performed in previous stage + dataAcc = inputData; +end + +% Filter data +[B,A] = butter(2,20/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly +dataStepDetection = filtfilt(B,A,dataAcc); +VTAcc = data(:,1); +APAcc = data(:,3); + +%% Determine Steps - +% USE THE AP ACCELERATION: WIEBREN ZIJLSTRA: ASSESSMENT OF SPATIO-TEMPORAL PARAMTERS DURING UNCONSTRAINED WALKING (2004) + +% In order to run the step detection script we first need to run an autocorrelation function; +StrideTimeRange = [0.2 4.0]; % Range to search for stride time (seconds) + +[ResultStruct] = AutocorrStrides(dataStepDetection,FS, StrideTimeRange,ResultStruct); % first guess of stridetimesamples + +% StrideTimeSamples is needed as an input for the stepcountFunc; +StrideTimeSamples = ResultStruct.StrideTimeSamples; + +[PksAndLocsCorrected] = StepcountFunc(data,StrideTimeSamples,FS); +FootContacts = PksAndLocsCorrected; % (1:2:end,2); previous version +numStepsOption2_filt = numel(FootContacts); % counts number of steps; + +%% Determine Turns +% 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*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) +[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2,'MINPEAKDISTANCE',0.3*FS); % for step detection +numStepsOption2_filt = numel(pks); % counts number of locs for turn detection + +diffLocs = diff(locs); % calculates difference in location magnitude differences +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',0.2*FS); % 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',0.2*FS); % values were initially 5 +locsTurns = [locs(locs_diffLocs), locs(locs_diffLocs+1)]; + +%magNoGfilt_copy = magNoGfilt; +VTAcc_copy = data(:,1); +APAcc_copy = data(:,3); +for k = 1: size(locsTurns,1); + VTAcc_copy(locsTurns(k,1):locsTurns(k,2)) = NaN; + APAcc_copy(locsTurns(k,1):locsTurns(k,2)) = NaN; +end + +% Visualising signal; +if plotit + figure() + subplot(2,1,1) + hold on; + plot(VTAcc,'b') + plot(VTAcc_copy, 'r'); + title('Inside Straight: Filtered data VT with turns highlighted in blue') + line([6000,12000,18000,24000,30000,36000;6000,12000,18000,24000,30000,36000],[5,5,5,5,5,5;10,10,10,10,10,10],'LineWidth',2,'Linestyle','--','color','k') + hold on; + subplot(2,1,2) + plot(APAcc,'g') + plot(APAcc_copy, 'r'); + title('Inside Straight: Filtered data AP with turns highlighted in blue') + line([6000,12000,18000,24000,30000,36000;6000,12000,18000,24000,30000,36000],[0,0,0,0,0,0;10,10,10,10,10,10],'LineWidth',2,'Linestyle','--','color','k') + 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 \ No newline at end of file diff --git a/DetermineLocationTurnsFunc.m b/DetermineLocationTurnsFunc.m new file mode 100644 index 0000000..f971096 --- /dev/null +++ b/DetermineLocationTurnsFunc.m @@ -0,0 +1,89 @@ +function [locsTurns,FilteredData,FootContacts] = DetermineLocationTurnsFunc(inputData,FS,ApplyRealignment,plotit,Distance,ResultStruct) +% Description: Determine the location of turns, plot for visual inspection + +% Input: Acc Data (in previous stage realigned?) +% Realigned sensor data to VT-ML-AP frame + +if ApplyRealignment % apply realignment 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; +else % realignment/detrend performed in earlier stage! + data = inputData; + dataAcc = inputData; +end +% Filter data +[B,A] = butter(2,20/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly +dataStepDetection = filtfilt(B,A,dataAcc); +VTAcc = data(:,1); +APAcc = data(:,3); + +%% Determine Steps - +% USE THE AP ACCELERATION: WIEBREN ZIJLSTRA: ASSESSMENT OF SPATIO-TEMPORAL PARAMTERS DURING UNCONSTRAINED WALKING (2004) + +% In order to run the step detection script we first need to run an autocorrelation function; +StrideTimeRange = [0.2 4.0]; % Range to search for stride time (seconds) + +[ResultStruct] = AutocorrStrides(dataStepDetection,FS, StrideTimeRange,ResultStruct); % first guess of stridetimesamples + +% StrideTimeSamples is needed as an input for the stepcountFunc; +StrideTimeSamples = ResultStruct.StrideTimeSamples; + +[PksAndLocsCorrected] = StepcountFunc(data,StrideTimeSamples,FS); +FootContacts = PksAndLocsCorrected; % previous version LD: (1:2:end,2); +numStepsOption2_filt = numel(FootContacts); % counts number of steps; + +%% Determine Turns +% 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*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) +[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2,'MINPEAKDISTANCE',0.4*FS); % for step detection +numStepsOption2_filt = numel(pks); % counts number of locs for turn detection + +diffLocs = diff(locs); % calculates difference in location magnitude differences +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',0.2*FS); % 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',0.2*FS); % values were initially 5 +locsTurns = [locs(locs_diffLocs), locs(locs_diffLocs+1)]; + +%magNoGfilt_copy = magNoGfilt; +VTAcc_copy = data(:,1); +APAcc_copy = data(:,3); +for k = 1: size(locsTurns,1); + VTAcc_copy(locsTurns(k,1):locsTurns(k,2)) = NaN; + APAcc_copy(locsTurns(k,1):locsTurns(k,2)) = NaN; +end + +% Visualising signal; +if plotit + figure() + subplot(2,1,1) + hold on; + plot(VTAcc,'b') + plot(VTAcc_copy, 'r'); + title('Inside Straight: Filtered data VT with turns highlighted in blue') + 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') + hold on; + subplot(2,1,2) + plot(APAcc,'g') + plot(APAcc_copy, 'r'); + title('Inside Straight: Filtered data AP with turns highlighted in blue') + 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') + 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 \ No newline at end of file diff --git a/FINAL_MAP_CLBP_Controls_23052021.xls b/FINAL_MAP_CLBP_Controls_23052021.xls new file mode 100644 index 0000000..47c1dd3 Binary files /dev/null and b/FINAL_MAP_CLBP_Controls_23052021.xls differ diff --git a/FINAL_NPRSBaseline_Minute_23052021.xls b/FINAL_NPRSBaseline_Minute_23052021.xls new file mode 100644 index 0000000..ad9c983 Binary files /dev/null and b/FINAL_NPRSBaseline_Minute_23052021.xls differ diff --git a/FINAL_NPRS_GaitCharacteristics_23052021.xls b/FINAL_NPRS_GaitCharacteristics_23052021.xls new file mode 100644 index 0000000..d78e92c Binary files /dev/null and b/FINAL_NPRS_GaitCharacteristics_23052021.xls differ diff --git a/FalseNearestNeighborsSR.m b/FalseNearestNeighborsSR.m new file mode 100644 index 0000000..7e2d4f8 --- /dev/null +++ b/FalseNearestNeighborsSR.m @@ -0,0 +1,126 @@ +function fnnM = FalseNearestNeighborsSR(xV,tauV,mV,escape,theiler) +% fnnM = FalseNearestNeighbors(xV,tauV,mV,escape,theiler) +% FALSENEARESTNEIGHBORS computes the percentage of false nearest neighbors +% for a range of delays in 'tauV' and embedding dimensions in 'mV'. +% INPUT +% xV : Vector of the scalar time series +% tauV : A vector of the delay times. +% mV : A vector of the embedding dimension. +% escape : A factor of escaping from the neighborhood. Default=10. +% theiler : the Theiler window to exclude time correlated points in the +% search for neighboring points. Default=0. +% OUTPUT: +% fnnM : A matrix of size 'ntau' x 'nm', where 'ntau' is the number of +% given delays and 'nm' is the number of given embedding +% dimensions, containing the percentage of false nearest +% neighbors. +%======================================================================== +% , v 1.0 2010/02/11 22:09:14 Kugiumtzis & Tsimpiris +% This is part of the MATS-Toolkit http://eeganalysis.web.auth.gr/ + +%======================================================================== +% Copyright (C) 2010 by Dimitris Kugiumtzis and Alkiviadis Tsimpiris +% + +%======================================================================== +% Version: 1.0 + +% LICENSE: +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 3 of the License, or +% any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program. If not, see http://www.gnu.org/licenses/>. + +%========================================================================= +% Reference : D. Kugiumtzis and A. Tsimpiris, "Measures of Analysis of Time Series (MATS): +% A Matlab Toolkit for Computation of Multiple Measures on Time Series Data Bases", +% Journal of Statistical Software, in press, 2010 + +% Link : http://eeganalysis.web.auth.gr/ +%========================================================================= +% Updates: +% Sietse Rispens, January 2012: use a different kdtree algorithm, +% searching k nearest neighbours, to improve performance +% +%========================================================================= +fthres = 0.1; % A factor of the data SD to be used as the maximum radius + % for searching for valid nearest neighbor. +propthres = fthres; % Limit for the proportion of valid points, i.e. points + % for which a nearest neighbor was found. If the proporion + % of valid points is beyond this limit, do not compute + % FNN. + +n = length(xV); +if isempty(escape), escape=10; end +if isempty(theiler), theiler=0; end +% Rescale to [0,1] and add infinitesimal noise to have distinct samples +xmin = min(xV); +xmax = max(xV); +xV = (xV - xmin) / (xmax-xmin); +xV = AddNoise(xV,10^(-10)); +ntau = length(tauV); +nm = length(mV); +fnnM = NaN*ones(ntau,nm); +for itau = 1:ntau + tau = tauV(itau); + for im=1:nm + m = mV(im); + nvec = n-m*tau; % to be able to add the component x(nvec+tau) for m+1 + if nvec-theiler < 2 + break; + end + xM = NaN*ones(nvec,m); + for i=1:m + xM(:,m-i+1) = xV(1+(i-1)*tau:nvec+(i-1)*tau); + end + % k-d-tree data structure of the training set for the given m + TreeRoot=kdtree_build(xM); + % For each target point, find the nearest neighbor, and check whether + % the distance increase over the escape distance by adding the next + % component for m+1. + idxV = NaN*ones(nvec,1); + distV = NaN*ones(nvec,1); + k0 = 2; % The initial number of nearest neighbors to look for + kmax = min(2*theiler + 2,nvec); % The maximum number of nearest neighbors to look for + for i=1:nvec + tarV = xM(i,:); + iV = []; + k=k0; + kmaxreached = 0; + while isempty(iV) && ~kmaxreached + [neiindV, neidisV] = kdtree_k_nearest_neighbors(TreeRoot,tarV,k); +% [neiM,neidisV,neiindV]=kdrangequery(TreeRoot,tarV,rthres*sqrt(m)); + [oneidisV,oneiindV]=sort(neidisV); + neiindV = neiindV(oneiindV); + neidisV = neidisV(oneiindV); + iV = find(abs(neiindV(1)-neiindV(2:end))>theiler); + if k >= kmax + kmaxreached = 1; + elseif isempty(iV) + k = min(kmax,k*2); + end + end + if ~isempty(iV) + idxV(i) = neiindV(iV(1)+1); + distV(i) = neidisV(iV(1)+1); + end + end % for i + iV = find(~isnan(idxV)); + nproper = length(iV); + % Compute fnn only if there is a sufficient number of target points + % having nearest neighbor (in R^m) within the threshold distance + if nproper>propthres*nvec + nnfactorV = 1+(xV(iV+m*tau)-xV(idxV(iV)+m*tau)).^2./distV(iV).^2; + fnnM(itau,im) = length(find(nnfactorV > escape^2))/nproper; + end + kdtree_delete(TreeRoot); % Free the pointer to k-d-tree + end +end diff --git a/GaitOutcomesTrunkAccFuncIH.m b/GaitOutcomesTrunkAccFuncIH.m index 7fa4d7a..9c85b1d 100644 --- a/GaitOutcomesTrunkAccFuncIH.m +++ b/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 IH feb-april 2020 +% Adapted LD feb 2021 (IH feb 2020) % koloms data of smartphone % 1st column is time data; @@ -47,7 +47,6 @@ N_Harm = 12; % Number of harmonics used for harmonic ratio LowFrequentPowerThresholds = ... [0.7 1.4]; % Threshold frequencies for estimation of low-frequent power percentages Lyap_m = 7; % Embedding dimension (used in Lyapunov estimations) -Lyap_FitWinLen = round(60/100*FS); % Fitting window length (used in Lyapunov estimations Rosenstein's method) Sen_m = 5; % Dimension, the length of the subseries to be matched (used in sample entropy estimation) Sen_r = 0.3; % Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation) NStartEnd = [100]; @@ -59,21 +58,25 @@ ResultStruct = struct(); % Apply Realignment & Filter data 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% + data = inputData; % ALREADY REORDERD: 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; [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; reorder data to 1 = V; 2 = ML; 3 = AP + %data = inputData(:,[3,2,1]); + %[RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS); might not be necessary + %dataAcc = RealignedAcc; + data = inputData; + dataAcc = inputData; [B,A] = butter(2,20/(FS/2),'low'); - dataAcc = inputData; - dataAcc_filt = filtfilt(B,A,dataAcc); + dataAcc_filt = filtfilt(B,A,inputData); end -%% Step dectection -% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed +%% Step detection +% Determines the number of steps in the signal so that the first 1 and last step in the signal can be removed if ApplyRemoveSteps @@ -84,15 +87,15 @@ if ApplyRemoveSteps StrideTimeSamples = ResultStruct.StrideTimeSamples; % Calculate the number of steps; - [PksAndLocsCorrected] = StepcountFunc(dataAcc_filt,StrideTimeSamples,FS); + [PksAndLocsCorrected] = StepcountFunc(data,StrideTimeSamples,FS); % This function selects steps based on negative and positive values. % However to determine the steps correctly we only need one of these; - LocsSteps = PksAndLocsCorrected(1:2:end,2); + LocsStepsLD = PksAndLocsCorrected; %% Cut data & remove currents results - % Remove 20 steps in the beginning and end of data - dataAccCut = dataAcc(LocsSteps(31):LocsSteps(end-30),:); - dataAccCut_filt = dataAcc_filt(LocsSteps(31):LocsSteps(end-30),:); + % Remove 1 step in the beginning and end of data + dataAccCut = dataAcc(LocsStepsLD(1):LocsStepsLD(end-1),:); + dataAccCut_filt = dataAcc_filt(LocsStepsLD(1):LocsStepsLD(end-1),:); % Clear currently saved results from Autocorrelation Analysis @@ -100,6 +103,10 @@ if ApplyRemoveSteps clear PksAndLocsCorrected; clear LocsSteps; + % Change window length necessary if ApplyRemoveSteps? (16-2-2013 LD) + + WindowLen = 10*FS; + else; dataAccCut = dataAcc; dataAccCut_filt = dataAcc_filt; @@ -108,49 +115,10 @@ end %% Calculate stride parameters ResultStruct = struct; % create empty struct -% Run function AutoCorrStrides, Outcomeparameters: StrideRegularity,RelativeStrideVariability,StrideTimeSamples,StrideTime +% Run function AutoCorrStrides, Outcomeparameters: StrideRegularity AP/VT ,StrideTimeSamples,StrideTime [ResultStruct] = AutocorrStrides(dataAccCut_filt,FS, StrideTimeRange,ResultStruct); StrideTimeSamples = ResultStruct.StrideTimeSamples; % needed as input for other functions -% Calculate Step symmetry --> method 1 -ij = 1; -dirSymm = [1,3]; % Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4. - -for jk=1:length(dirSymm) - [C, lags] = AutocorrRegSymmSteps(dataAccCut_filt(:,dirSymm(jk))); - [Ad,p] = findpeaks(C,'MinPeakProminence',0.2, 'MinPeakHeight', 0.2); - - if size(Ad,1) > 1 - Ad1 = Ad(1); - Ad2 = Ad(2); - GaitSymm(:,ij) = abs((Ad1-Ad2)/mean([Ad1+Ad2]))*100; - else - GaitSymm(:,ij) = NaN; - end - ij = ij +1; -end -% Save outcome in struct; -ResultStruct.GaitSymm_V = GaitSymm(1); -ResultStruct.GaitSymm_AP = GaitSymm(2); - -% Calculate Step symmetry --> method 2 -[PksAndLocsCorrected] = StepcountFunc(dataAccCut_filt,StrideTimeSamples,FS); -LocsSteps = PksAndLocsCorrected(2:2:end,2); - -if rem(size(LocsSteps,1),2) == 0; % is number of steps is even - LocsSteps2 = LocsSteps(1:2:end); -else - LocsSteps2 = LocsSteps(3:2:end); -end - -LocsSteps1 = LocsSteps(2:2:end); -DiffLocs2 = diff(LocsSteps2); -DiffLocs1 = diff(LocsSteps1); -StepTime2 = DiffLocs2(1:end-1)/FS; % leave last one out because it is higher -StepTime1 = DiffLocs1(1:end-1)/FS; -SI = abs((2*(StepTime2-StepTime1))./(StepTime2+StepTime1))*100; -ResultStruct.GaitSymmIndex = nanmean(SI); - %% Calculate spatiotemporal stride parameters % Measures from height variation by double integration of VT accelerations and high-pass filtering @@ -158,53 +126,45 @@ ResultStruct.GaitSymmIndex = nanmean(SI); %% Measures derived from spectral analysis -AccVectorLen = sqrt(sum(dataAccCut_filt(:,1:3).^2,2)); +AccVectorLen = sqrt(sum(dataAccCut_filt(:,1:3).^2,2)); % WindowLen -> 10*Fs OR on InputSignal [ResultStruct] = SpectralAnalysisGaitfunc(dataAccCut_filt,WindowLen,FS,N_Harm,LowFrequentPowerThresholds,AccVectorLen,ResultStruct); %% Calculation non-linear parameters; % cut into windows of size WindowLen -N_Windows = floor(size(dataAccCut,1)/WindowLen); -N_SkipBegin = ceil((size(dataAccCut,1)-N_Windows*WindowLen)/2); +N_Windows = floor(size(dataAccCut,1)/WindowLen); % Not sure if WindowLen should be different? +N_SkipBegin = ceil((size(dataAccCut,1)-N_Windows*WindowLen)/2); LyapunovWolf = nan(N_Windows,3); -LyapunovRosen = nan(N_Windows,3); SE= nan(N_Windows,3); for WinNr = 1:N_Windows; AccWin = dataAccCut(N_SkipBegin+(WinNr-1)*WindowLen+(1:WindowLen),:); for j=1:3 [LyapunovWolf(WinNr,j),~] = CalcMaxLyapWolfFixedEvolv(AccWin(:,j),FS,struct('m',Lyap_m)); - [LyapunovRosen(WinNr,j),outpo] = CalcMaxLyapConvGait(AccWin(:,j),FS,struct('m',Lyap_m,'FitWinLen',Lyap_FitWinLen)); [SE(WinNr,j)] = funcSampleEntropy(AccWin(:,j), Sen_m, Sen_r); % no correction for FS; SE does increase with higher FS but effect is considered negligible as range is small (98-104HZ). Might consider updating r to account for larger ranges. end end LyapunovWolf = nanmean(LyapunovWolf,1); -LyapunovRosen = nanmean(LyapunovRosen,1); SampleEntropy = nanmean(SE,1); ResultStruct.LyapunovWolf_V = LyapunovWolf(1); ResultStruct.LyapunovWolf_ML = LyapunovWolf(2); ResultStruct.LyapunovWolf_AP = LyapunovWolf(3); -ResultStruct.LyapunovRosen_V = LyapunovRosen(1); -ResultStruct.LyapunovRosen_ML = LyapunovRosen(2); -ResultStruct.LyapunovRosen_AP = LyapunovRosen(3); ResultStruct.SampleEntropy_V = SampleEntropy(1); ResultStruct.SampleEntropy_ML = SampleEntropy(2); ResultStruct.SampleEntropy_AP = SampleEntropy(3); -if isfield(ResultStruct,'StrideFrequency') - LyapunovPerStrideWolf = LyapunovWolf/ResultStruct.StrideFrequency; - LyapunovPerStrideRosen = LyapunovRosen/ResultStruct.StrideFrequency; -end +%% Calculate RMS in each direction: added february 2021 by LD, CONSTRUCT: 'Pace' +% Sekine, M., Tamura, T., Yoshida, M., Suda, Y., Kimura, Y., Miyoshi, H., ... & Fujimoto, T. (2013). A gait abnormality measure based on root mean square of trunk acceleration. Journal of neuroengineering and rehabilitation, 10(1), 1-7. -ResultStruct.LyapunovPerStrideWolf_V = LyapunovPerStrideWolf(1); -ResultStruct.LyapunovPerStrideWolf_ML = LyapunovPerStrideWolf(2); -ResultStruct.LyapunovPerStrideWolf_AP = LyapunovPerStrideWolf(3); -ResultStruct.LyapunovPerStrideRosen_V = LyapunovPerStrideRosen(1); -ResultStruct.LyapunovPerStrideRosen_ML = LyapunovPerStrideRosen(2); -ResultStruct.LyapunovPerStrideRosen_AP = LyapunovPerStrideRosen(3); +Data_Centered = normalize(dataAcc_filt,'center','mean'); % The RMS coincides with the Sd since the Acc signals are transformed to give a mean equal to zero +RMS = rms(Data_Centered); + +ResultStruct.RMS_V = RMS(1); +ResultStruct.RMS_ML = RMS(2); +ResultStruct.RMS_AP = RMS(3); end \ No newline at end of file diff --git a/GaitOutcomesTrunkAccFuncLD.m b/GaitOutcomesTrunkAccFuncLD.m new file mode 100644 index 0000000..c1141cf --- /dev/null +++ b/GaitOutcomesTrunkAccFuncLD.m @@ -0,0 +1,210 @@ +function [ResultStruct] = GaitOutcomesTrunkAccFuncLD(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps) + +% Description: Trunk analysis of Iphone data without the need for step detection +% CL Nov 2019 +% Adapted IH feb-april 2020 + +% koloms data of smartphone +% 1st column is time data; +% 2nd column is X, medio-lateral: + left, - right +% 3rd column is Y, vertical: + downwards, - upwards +% 4th column is Z, anterior- posterior : + forwards, - backwards + +%% Input Trunk accelerations during locomotion in VT, ML, AP direction +% InputData: Acceleration signal with time and accelerations in VT,ML and +% AP direction. +% FS: sample frequency of the Accdata +% LegLength: length of the leg of the participant in m; + + +%% Output +% ResultStruct: structure coninting all outcome measured calculated +% Spectral parameters, spatiotemporal gait parameters, non-linear +% parameters +% fields and subfields: include the multiple measurements of a subject + +%% Literature +% Richman & Moorman, 2000; [ sample entropy] +% Bisi & Stagni Gait & Posture 2016, 47 (6) 37-42 +% Kavagnah et al., Eur J Appl Physiol 2005 94: 468?475; Human Movement Science 24(2005) 574?587 [ synchrony] +% Moe-Nilsen J Biomech 2004 37, 121-126 [ autorcorrelation step regularity and symmetry +% Kobsar et al. Gait & Posture 2014 39, 553?557 [ synchrony ] +% Rispen et al; Gait & Posture 2014, 40, 187 - 192 [realignment axes] +% Zijlstra & HofGait & Posture 2003 18,2, 1-10 [spatiotemporal gait variables] +% Lamoth et al, 2002 [index of harmonicity] +% Costa et al. 2003 Physica A 330 (2003) 53–60 [ multiscale entropy] +% Cignetti F, Decker LM, Stergiou N. Ann Biomed Eng. 2012 +% May;40(5):1122-30. doi: 10.1007/s10439-011-0474-3. Epub 2011 Nov 25. [ +% Wofl vs. Rosenstein Lyapunov] + + +%% Settings +Gr = 9.81; % Gravity acceleration, multiplication factor for accelerations +StrideFreqEstimate = 1.00; % Used to set search for stride frequency from 0.5*StrideFreqEstimate until 2*StrideFreqEstimate +StrideTimeRange = [0.2 4.0]; % Range to search for stride time (seconds) +IgnoreMinMaxStrides = 0.10; % Number or percentage of highest&lowest values ignored for improved variability estimation +N_Harm = 12; % Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation +LowFrequentPowerThresholds = ... + [0.7 1.4]; % Threshold frequencies for estimation of low-frequent power percentages +Lyap_m = 7; % Embedding dimension (used in Lyapunov estimations) +Lyap_FitWinLen = round(60/100*FS); % Fitting window length (used in Lyapunov estimations Rosenstein's method) +Sen_m = 5; % Dimension, the length of the subseries to be matched (used in sample entropy estimation) +Sen_r = 0.3; % Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation) +NStartEnd = [100]; +M = 5; % maximum template length +ResultStruct = struct(); + +%% Filter and Realign Accdata + +% Apply Realignment & Filter data + +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; + [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; + [B,A] = butter(2,20/(FS/2),'low'); + dataAcc = inputData; + dataAcc_filt = filtfilt(B,A,dataAcc); +end + + +%% Step dectection +% 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 + + % In order to run the step detection script we first need to run an autocorrelation function; + [ResultStruct] = AutocorrStrides(dataAcc_filt,FS, StrideTimeRange,ResultStruct); + + % StrideTimeSamples is needed as an input for the stepcountFunc; + StrideTimeSamples = ResultStruct.StrideTimeSamples; + + % Calculate the number of steps; + [PksAndLocsCorrected] = StepcountFunc(dataAcc_filt,StrideTimeSamples,FS); + % This function selects steps based on negative and positive values. + % However to determine the steps correctly we only need one of these; + LocsSteps = PksAndLocsCorrected(1:2:end,2); + + %% Cut data & remove currents results + % Remove 1 step in the beginning and end of data (comment Bert Otten) + dataAccCut = dataAcc(LocsSteps(1):LocsSteps(end-1),:); + dataAccCut_filt = dataAcc_filt(LocsSteps(1):LocsSteps(end-1),:); + + % Clear currently saved results from Autocorrelation Analysis + + clear ResultStruct; + clear PksAndLocsCorrected; + clear LocsSteps; + +else; + dataAccCut = dataAcc; + dataAccCut_filt = dataAcc_filt; +end + +%% Calculate stride parameters +ResultStruct = struct; % create empty struct + +% Run function AutoCorrStrides, Outcomeparameters: StrideRegularity,RelativeStrideVariability,StrideTimeSamples,StrideTime +[ResultStruct] = AutocorrStrides(dataAccCut_filt,FS, StrideTimeRange,ResultStruct); +StrideTimeSamples = ResultStruct.StrideTimeSamples; % needed as input for other functions + +% Calculate Step symmetry --> method 1 +ij = 1; +dirSymm = [1,3]; % Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4. + +for jk=1:length(dirSymm) + [C, lags] = AutocorrRegSymmSteps(dataAccCut_filt(:,dirSymm(jk))); + [Ad,p] = findpeaks(C,'MinPeakProminence',0.2, 'MinPeakHeight', 0.2); + + if size(Ad,1) > 1 + Ad1 = Ad(1); + Ad2 = Ad(2); + GaitSymm(:,ij) = abs((Ad1-Ad2)/mean([Ad1+Ad2]))*100; + else + GaitSymm(:,ij) = NaN; + end + ij = ij +1; +end +% Save outcome in struct; +ResultStruct.GaitSymm_V = GaitSymm(1); +ResultStruct.GaitSymm_AP = GaitSymm(2); + +% Calculate Step symmetry --> method 2 +[PksAndLocsCorrected] = StepcountFunc(dataAccCut_filt,StrideTimeSamples,FS); +LocsSteps = PksAndLocsCorrected(2:2:end,2); + +if rem(size(LocsSteps,1),2) == 0; % is number of steps is even + LocsSteps2 = LocsSteps(1:2:end); +else + LocsSteps2 = LocsSteps(3:2:end); +end + +LocsSteps1 = LocsSteps(2:2:end); +DiffLocs2 = diff(LocsSteps2); +DiffLocs1 = diff(LocsSteps1); +StepTime2 = DiffLocs2(1:end-1)/FS; % leave last one out because it is higher +StepTime1 = DiffLocs1(1:end-1)/FS; +SI = abs((2*(StepTime2-StepTime1))./(StepTime2+StepTime1))*100; +ResultStruct.GaitSymmIndex = nanmean(SI); + +%% Calculate spatiotemporal stride parameters + +% Measures from height variation by double integration of VT accelerations and high-pass filtering +[ResultStruct] = SpatioTemporalGaitParameters(dataAccCut_filt,StrideTimeSamples,ApplyRealignment,LegLength,FS,IgnoreMinMaxStrides,ResultStruct); + +%% Measures derived from spectral analysis + +AccVectorLen = sqrt(sum(dataAccCut_filt(:,1:3).^2,2)); +[ResultStruct] = SpectralAnalysisGaitfunc(dataAccCut_filt,WindowLen,FS,N_Harm,LowFrequentPowerThresholds,AccVectorLen,ResultStruct); + + +%% Calculation non-linear parameters; + +% cut into windows of size WindowLen +N_Windows = floor(size(dataAccCut,1)/WindowLen); +N_SkipBegin = ceil((size(dataAccCut,1)-N_Windows*WindowLen)/2); +LyapunovWolf = nan(N_Windows,3); +LyapunovRosen = nan(N_Windows,3); +SE= nan(N_Windows,3); + +for WinNr = 1:N_Windows; + AccWin = dataAccCut(N_SkipBegin+(WinNr-1)*WindowLen+(1:WindowLen),:); + for j=1:3 + [LyapunovWolf(WinNr,j),~] = CalcMaxLyapWolfFixedEvolv(AccWin(:,j),FS,struct('m',Lyap_m)); + [LyapunovRosen(WinNr,j),outpo] = CalcMaxLyapConvGait(AccWin(:,j),FS,struct('m',Lyap_m,'FitWinLen',Lyap_FitWinLen)); + [SE(WinNr,j)] = funcSampleEntropy(AccWin(:,j), Sen_m, Sen_r); + % no correction for FS; SE does increase with higher FS but effect is considered negligible as range is small (98-104HZ). Might consider updating r to account for larger ranges. + end +end + +LyapunovWolf = nanmean(LyapunovWolf,1); +LyapunovRosen = nanmean(LyapunovRosen,1); +SampleEntropy = nanmean(SE,1); + +ResultStruct.LyapunovWolf_V = LyapunovWolf(1); +ResultStruct.LyapunovWolf_ML = LyapunovWolf(2); +ResultStruct.LyapunovWolf_AP = LyapunovWolf(3); +ResultStruct.LyapunovRosen_V = LyapunovRosen(1); +ResultStruct.LyapunovRosen_ML = LyapunovRosen(2); +ResultStruct.LyapunovRosen_AP = LyapunovRosen(3); +ResultStruct.SampleEntropy_V = SampleEntropy(1); +ResultStruct.SampleEntropy_ML = SampleEntropy(2); +ResultStruct.SampleEntropy_AP = SampleEntropy(3); + +if isfield(ResultStruct,'StrideFrequency') + LyapunovPerStrideWolf = LyapunovWolf/ResultStruct.StrideFrequency; + LyapunovPerStrideRosen = LyapunovRosen/ResultStruct.StrideFrequency; +end + +ResultStruct.LyapunovPerStrideWolf_V = LyapunovPerStrideWolf(1); +ResultStruct.LyapunovPerStrideWolf_ML = LyapunovPerStrideWolf(2); +ResultStruct.LyapunovPerStrideWolf_AP = LyapunovPerStrideWolf(3); +ResultStruct.LyapunovPerStrideRosen_V = LyapunovPerStrideRosen(1); +ResultStruct.LyapunovPerStrideRosen_ML = LyapunovPerStrideRosen(2); +ResultStruct.LyapunovPerStrideRosen_AP = LyapunovPerStrideRosen(3); + +end \ No newline at end of file diff --git a/GaitVarOutcomesCombined_230221.mat b/GaitVarOutcomesCombined_230221.mat new file mode 100644 index 0000000..92626ed Binary files /dev/null and b/GaitVarOutcomesCombined_230221.mat differ diff --git a/GaitVariabilityAnalysisLD.html b/GaitVariabilityAnalysisLD.html deleted file mode 100644 index 122281a..0000000 --- a/GaitVariabilityAnalysisLD.html +++ /dev/null @@ -1,1709 +0,0 @@ - -Gait Variability Analysis CLBP

Gait Variability Analysis CLBP

Table of Contents
Clear and close; -Load data; -Settings; -Plot the data; -Calculate parameters; - Index of harmonicity (Lamoth et al. 2002) - Lyapunov exponents (Wolfs vs. Rosenstein) -Visualize step detection;
% Gait Variability Analysis
% Script created for MAP 2020-2021
% adapted from Claudine Lamoth and Iris Hagoort
% version1 October 2020
% Input: needs mat file which contains all raw accelerometer data
% Input: needs excel file containing the participant information including
% leg length.

Clear and close;

clear;
close all;

Load data;

Select 1 trial. For loop to import all data will be used at a later stage
[FNaam,FilePad] = uigetfile('*.xls','Load phyphox data...');
filename =[FilePad FNaam];
PhyphoxData = xlsread(filename)
PhyphoxData = 24092×4
0 0.4018 -8.5041 4.8779 - 0.0100 0.3962 -8.4703 4.7944 - 0.0199 0.4335 -8.4378 4.7159 - 0.0299 0.5209 -8.3889 4.6266 - 0.0399 0.6495 -8.3796 4.5437 - 0.0498 0.7528 -8.3817 4.4288 - 0.0598 0.8820 -8.3622 4.3134 - 0.0697 0.9841 -8.4321 4.2221 - 0.0797 1.1041 -8.5237 4.1916 - 0.0897 1.1959 -8.5418 4.1310 -
%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData
%load('ExcelInfo.mat');
%Participants = fields(AccData);

Settings;

LegLength = 98 % LegLength info not available!
LegLength = 98
%LegLengths = excel.data.GeneralInformation(:,5); % leglength info is in 5th column
LegLengthsM = LegLength/100; % convert to m
t1 = length(PhyphoxData(:,1)); % Number of Samples
FS = 100; % sample frequency
Time_ms = PhyphoxData(:,1);
accX = PhyphoxData(:,2);
accY = PhyphoxData(:,3);
accZ = PhyphoxData(:,4);
AccData = (PhyphoxData(:,[1 2 3 4])); % matrix with accelerometer data
Start = 1; % Start time (s) for plot
End = 60; % End time (s) for plot
T1 = Start*FS; % Start time calculated from Hz
T2 = End*FS; % End time calculated from Hz
c = (Start:(1/FS):End)'; % Time STEPSIZE = 1/100

Plot the data;

(1) first step in notebook
1st column is time data (ms)
2nd column is X, medio-lateral: + left, - right
3rd column is Y, vertical: + downwards, - upwards
4th column is Z, anterior- posterior : + forwards, - backwards
AccX = accX(T1:T2); % Signal over timeframe
AccY = accY(T1:T2); % Signal over timeframe
AccZ = accZ(T1:T2); % Signal over timeframe
figure(1);
plot(c,AccX,c,AccY,c,AccZ); % Plot signal over timeframe
title('acc signal not filtered - First Minute')
xlabel('Time (s)');
ylabel('acceleration (g)');
legend('X - ML','Y - Vertical','Z - AP')

Calculate parameters;

calculate only for the first participant;
inputData = AccData;
WindowLength = FS*10; % why FS*10?
ApplyRealignment = true; % reorder data to 1 = V; 2= ML, 3 = AP
ApplyRemoveSteps = false; % if true - removes first 30 and last 30 steps
[ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLength,ApplyRealignment,ApplyRemoveSteps)
ResultStruct = struct with fields:
StrideRegularity_V: 0.3908 - StrideRegularity_ML: 0.4191 - StrideRegularity_AP: 0.1101 - StrideRegularity_All: 0.3529 - RelativeStrideVariability_V: 0.6092 - RelativeStrideVariability_ML: 0.5809 - RelativeStrideVariability_AP: 0.8899 - RelativeStrideVariability_All: 0.6471 - StrideTimeSamples: 132 - StrideTimeSeconds: 1.3200 - GaitSymm_V: 16.8830 - GaitSymm_AP: NaN - GaitSymmIndex: 2.8065 - StepLengthMean: 11.5894 - Distance: 1.8825e+03 - WalkingSpeedMean: 7.8279 - StrideTimeVariability: 0.1931 - StrideSpeedVariability: 1.1210 - StrideLengthVariability: 0.7532 - StrideTimeVariabilityOmitOutlier: 14.7357 - StrideSpeedVariabilityOmitOutlier: 0.8713 - StrideLengthVariabilityOmitOutlier: 0.4511 - IndexHarmonicity_V: 0.6094 - IndexHarmonicity_ML: 0.8041 - IndexHarmonicity_AP: 0.9122 - IndexHarmonicity_All: 0.6742 - HarmonicRatio_V: 2.4928 - HarmonicRatio_ML: 2.8449 - HarmonicRatio_AP: 1.6364 - HarmonicRatioP_V: 9.1114 - HarmonicRatioP_ML: 14.1449 - HarmonicRatioP_AP: 5.6995 - FrequencyVariability_V: 0.5234 - FrequencyVariability_ML: 0.6435 - FrequencyVariability_AP: 0.6281 - StrideFrequency: 0.7367 - LyapunovWolf_V: 1.3653 - LyapunovWolf_ML: 1.1477 - LyapunovWolf_AP: 1.4231 - LyapunovRosen_V: 1.0151 - LyapunovRosen_ML: 0.7871 - LyapunovRosen_AP: 0.9792 - SampleEntropy_V: 0.1999 - SampleEntropy_ML: 0.2537 - SampleEntropy_AP: 0.2710 - LyapunovPerStrideWolf_V: 1.8534 - LyapunovPerStrideWolf_ML: 1.5579 - LyapunovPerStrideWolf_AP: 1.9318 - LyapunovPerStrideRosen_V: 1.3780 - LyapunovPerStrideRosen_ML: 1.0685 - LyapunovPerStrideRosen_AP: 1.3292 -
output:
- NaN GaitSymm_V:
  • Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4
- SampEn has two advantages over ApEn: data length independence and a relatively trouble-free implementation.
- Some Settings ResulStruct
IgnoreMinMaxStrides = 0.10; % Number or percentage of highest&lowest values ignored for improved variability estimation
N_Harm = 12; % Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation
Lyap_m = 7; % Embedding dimension (used in Lyapunov estimations)
Lyap_FitWinLen = round(60/100*FS); % Fitting window length (used in Lyapunov estimations Rosenstein's method)
Sen_m = 5; % Dimension, the length of the subseries to be matched (used in sample entropy estimation)
Sen_r = 0.3; % Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation)

Index of harmonicity (Lamoth et al. 2002)

by means of a discrete Fourier transform (DFT). The peak power at the first six harmonics was estimated and, subsequently, the index of harmonicity was defined as ; FORMULA
where P0 is the power spectral density of the fundamental frequency (first harmonic) and $ Pi the cumulative sum of power spectral density of the fundamental frequency and the first five superharmonics. A power ratio of 1 indicates that the rotation of the pelvis or the thorax is perfectly harmonic. In view of possible drift, which could lead to missing or widening peaks, the power spectral density of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of the peak frequency value. All power spectral densities were normalized by dividing the power by the sum of the total power spectrum, which equals the variance.

Lyapunov exponents (Wolfs vs. Rosenstein)

The W-algorithm is advocated for use when examining local dynamic stability with small gait data sets.

Visualize step detection;

function [ResultStruct] = GaitVariabilityAnalysisIH_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps);
script for analysing straight parts
(1) Realign Data
%% Realign data
data = inputData(:, [3,2,4]); % reorder data to 1 = V; 2= ML, 3 = AP
%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).
% 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
(2) Filter Data
%% Filter data strongly & Determine location of steps
% 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);
(3) Determine Location of steps
% Determine steps;
%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%%
% 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 = std(magNoGfilt);
[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2); % for step detection
numStepsOption2_filt = numel(pks); % counts number of steps;
(4) Determine location of turns
%% Determine locations of turns;
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(2);
findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',5); % 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',5);
locsTurns = [locs(locs_diffLocs), locs(locs_diffLocs+1)];
(5) Visualize turns
%% Visualizing turns
% Duplying signal + visualing
% to make second signal with the locations of the turns filled with NaN, so
% that both signals can be plotted above each other in a different colour
magNoGfilt_copy = magNoGfilt;
for k = 1: size(locsTurns,1);
magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
end
% visualising signal;
figure;
subplot(2,1,1)
hold on;
plot(magNoGfilt,'b')
plot(magNoGfilt_copy, 'r');
title('Inside Straight: Filtered data with turns highlighted in blue')
hold off;
(6) CALCULATIONS
%% Calculation
% VRAAG LAURENS zie blauwe blaadje
startPos = 1;
for i = 1: size(locsTurns,1);
endPos = locsTurns(i,1)-1;
inputData = dataAcc(startPos:endPos,:);
WindowLen = size(inputData,1);
ApplyRealignment = false;
[ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); % Naam van deze moet nog aangepast.
if i ==1 % only the firs time
Parameters = fieldnames(ResultStruct);
NrParameters = length(Parameters);
end
for j = 1:NrParameters % only works if for every bin we get the same outcomes (which is the case in this script)
DataStraight.([char(Parameters(j))])(i) = ResultStruct.([char(Parameters(j))]);
end
startPos = locsTurns(i,2)+1;
end
clear ResultStruct;
% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside
% straight;
for j = 1:NrParameters;
ResultStruct.([char(Parameters(j))]) = nanmean(DataStraight.([char(Parameters(j))]))
end
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - LyapunovPerStrideRosen_V: 1.5914 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - LyapunovPerStrideRosen_V: 1.5914 - LyapunovPerStrideRosen_ML: 1.2964 -
ResultStruct = struct with fields:
StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - LyapunovPerStrideRosen_V: 1.5914 - LyapunovPerStrideRosen_ML: 1.2964 - LyapunovPerStrideRosen_AP: 1.7481 -

AggregateFunction;

-
- \ No newline at end of file diff --git a/GaitVariabilityAnalysisLD.tex b/GaitVariabilityAnalysisLD.tex deleted file mode 100644 index 9b5130d..0000000 --- a/GaitVariabilityAnalysisLD.tex +++ /dev/null @@ -1,1944 +0,0 @@ -% This LaTeX was auto-generated from MATLAB code. -% To make changes, update the MATLAB code and export to LaTeX again. - -\documentclass{article} - -\usepackage[utf8]{inputenc} -\usepackage[T1]{fontenc} -\usepackage{lmodern} -\usepackage{graphicx} -\usepackage{color} -\usepackage{hyperref} -\usepackage{amsmath} -\usepackage{amsfonts} -\usepackage{epstopdf} -\usepackage[table]{xcolor} -\usepackage{matlab} - -\sloppy -\epstopdfsetup{outdir=./} -\graphicspath{ {./GaitVariabilityAnalysisLD_images/} } - -\matlabhastoc - -\begin{document} - -\label{T_D37184EE} -\matlabtitle{Gait Variability Analysis CLBP} - -\matlabtableofcontents{Table of Contents} -\begin{matlabcode} -% Gait Variability Analysis -% Script created for MAP 2020-2021 -% adapted from Claudine Lamoth and Iris Hagoort -% version1 October 2020 - -% Input: needs mat file which contains all raw accelerometer data -% Input: needs excel file containing the participant information including -% leg length. - - -\end{matlabcode} - - -\label{H_7D86BAF1} -\matlabheading{Clear and close;} - -\begin{matlabcode} -clear; -close all; -\end{matlabcode} - - -\label{H_82614DF8} -\matlabheading{Load data;} - -\begin{par} -\begin{flushleft} -Select 1 trial. \textbf{For loop to import all data will be used at a later stage} -\end{flushleft} -\end{par} - -\begin{matlabcode} -[FNaam,FilePad] = uigetfile('*.xls','Load phyphox data...'); -filename =[FilePad FNaam]; -PhyphoxData = xlsread(filename) -\end{matlabcode} -\begin{matlaboutput} -PhyphoxData = 24092x4 - 0 0.4018 -8.5041 4.8779 - 0.0100 0.3962 -8.4703 4.7944 - 0.0199 0.4335 -8.4378 4.7159 - 0.0299 0.5209 -8.3889 4.6266 - 0.0399 0.6495 -8.3796 4.5437 - 0.0498 0.7528 -8.3817 4.4288 - 0.0598 0.8820 -8.3622 4.3134 - 0.0697 0.9841 -8.4321 4.2221 - 0.0797 1.1041 -8.5237 4.1916 - 0.0897 1.1959 -8.5418 4.1310 - -\end{matlaboutput} -\begin{matlabcode} - -%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData -%load('ExcelInfo.mat'); -%Participants = fields(AccData); -\end{matlabcode} - - -\label{H_04FF0795} -\matlabheading{Settings;} - -\begin{matlabcode} -LegLength = 98 % LegLength info not available! -\end{matlabcode} -\begin{matlaboutput} -LegLength = 98 -\end{matlaboutput} -\begin{matlabcode} -%LegLengths = excel.data.GeneralInformation(:,5); % leglength info is in 5th column -LegLengthsM = LegLength/100; % convert to m -t1 = length(PhyphoxData(:,1)); % Number of Samples - - -FS = 100; % sample frequency -Time_ms = PhyphoxData(:,1); -accX = PhyphoxData(:,2); -accY = PhyphoxData(:,3); -accZ = PhyphoxData(:,4); -AccData = (PhyphoxData(:,[1 2 3 4])); % matrix with accelerometer data - -Start = 1; % Start time (s) for plot -End = 60; % End time (s) for plot -T1 = Start*FS; % Start time calculated from Hz -T2 = End*FS; % End time calculated from Hz -c = (Start:(1/FS):End)'; % Time STEPSIZE = 1/100 -\end{matlabcode} - - -\label{H_A9CB60AB} -\matlabheading{Plot the data;} - -\begin{par} -\begin{flushleft} -\textbf{(1) first step in notebook} -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -1st column is time data (ms) -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -2nd column is X, medio-lateral: + left, - right -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -3rd column is Y, vertical: + downwards, - upwards -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -4th column is Z, anterior- posterior : + forwards, - backwards -\end{flushleft} -\end{par} - -\begin{matlabcode} -AccX = accX(T1:T2); % Signal over timeframe -AccY = accY(T1:T2); % Signal over timeframe -AccZ = accZ(T1:T2); % Signal over timeframe - -figure(1); -plot(c,AccX,c,AccY,c,AccZ); % Plot signal over timeframe -title('acc signal not filtered - First Minute') -xlabel('Time (s)'); -ylabel('acceleration (g)'); -legend('X - ML','Y - Vertical','Z - AP') -\end{matlabcode} -\begin{center} -\includegraphics[width=\maxwidth{56.196688409433015em}]{figure_0.eps} -\end{center} - -\label{H_77261343} -\matlabheading{\includegraphics[width=\maxwidth{37.431008529854495em}]{image_0}} - -\label{H_4A6AF4FC} -\vspace{1em} - -\label{H_CA962223} -\matlabheading{Calculate parameters;} - -\begin{par} -\begin{flushleft} -calculate only for the first participant; -\end{flushleft} -\end{par} - -\begin{matlabcode} -inputData = AccData; -WindowLength = FS*10; % why FS*10? -ApplyRealignment = true; % reorder data to 1 = V; 2= ML, 3 = AP -ApplyRemoveSteps = false; % if true - removes first 30 and last 30 steps -[ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLength,ApplyRealignment,ApplyRemoveSteps) -\end{matlabcode} -\begin{matlaboutput} -ResultStruct = - StrideRegularity_V: 0.3908 - StrideRegularity_ML: 0.4191 - StrideRegularity_AP: 0.1101 - StrideRegularity_All: 0.3529 - RelativeStrideVariability_V: 0.6092 - RelativeStrideVariability_ML: 0.5809 - RelativeStrideVariability_AP: 0.8899 - RelativeStrideVariability_All: 0.6471 - StrideTimeSamples: 132 - StrideTimeSeconds: 1.3200 - GaitSymm_V: 16.8830 - GaitSymm_AP: NaN - GaitSymmIndex: 2.8065 - StepLengthMean: 11.5894 - Distance: 1.8825e+03 - WalkingSpeedMean: 7.8279 - StrideTimeVariability: 0.1931 - StrideSpeedVariability: 1.1210 - StrideLengthVariability: 0.7532 - StrideTimeVariabilityOmitOutlier: 14.7357 - StrideSpeedVariabilityOmitOutlier: 0.8713 - StrideLengthVariabilityOmitOutlier: 0.4511 - IndexHarmonicity_V: 0.6094 - IndexHarmonicity_ML: 0.8041 - IndexHarmonicity_AP: 0.9122 - IndexHarmonicity_All: 0.6742 - HarmonicRatio_V: 2.4928 - HarmonicRatio_ML: 2.8449 - HarmonicRatio_AP: 1.6364 - HarmonicRatioP_V: 9.1114 - HarmonicRatioP_ML: 14.1449 - HarmonicRatioP_AP: 5.6995 - FrequencyVariability_V: 0.5234 - FrequencyVariability_ML: 0.6435 - FrequencyVariability_AP: 0.6281 - StrideFrequency: 0.7367 - LyapunovWolf_V: 1.3653 - LyapunovWolf_ML: 1.1477 - LyapunovWolf_AP: 1.4231 - LyapunovRosen_V: 1.0151 - LyapunovRosen_ML: 0.7871 - LyapunovRosen_AP: 0.9792 - SampleEntropy_V: 0.1999 - SampleEntropy_ML: 0.2537 - SampleEntropy_AP: 0.2710 - LyapunovPerStrideWolf_V: 1.8534 - LyapunovPerStrideWolf_ML: 1.5579 - LyapunovPerStrideWolf_AP: 1.9318 - LyapunovPerStrideRosen_V: 1.3780 - LyapunovPerStrideRosen_ML: 1.0685 - LyapunovPerStrideRosen_AP: 1.3292 - -\end{matlaboutput} - -\begin{par} -\begin{flushleft} -\textbf{output:} -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -- \underline{NaN GaitSymm\_V: } -\end{flushleft} -\end{par} - -\begin{itemize} -\setlength{\itemsep}{-1ex} - \item{\begin{flushleft} Gait Synmmetry is only informative in AP/V direction: See Tura A, Raggi M, Rocchi L, Cutti AG, Chiari L: Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil 2010, 7:4 \end{flushleft}} -\end{itemize} - -\begin{par} -\begin{flushleft} -- SampEn has two advantages over ApEn: data length independence and a relatively trouble-free implementation. -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -- Some Settings ResulStruct -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{IgnoreMinMaxStrides = 0.10; } \% Number or percentage of highest\&lowest values ignored for improved variability estimation -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{N\_Harm = 12; } \% Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{Lyap\_m = 7; } \% Embedding dimension (used in Lyapunov estimations) -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{Lyap\_FitWinLen} = round(60/100*FS); \% Fitting window length (used in Lyapunov estimations Rosenstein's method) -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{Sen\_m = 5;} \% Dimension, the length of the subseries to be matched (used in sample entropy estimation) -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{Sen\_r = 0.3;} \% Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation) -\end{flushleft} -\end{par} - -\label{H_2EA43FAC} -\matlabheadingtwo{Index of harmonicity (Lamoth et al. 2002)} - -\begin{par} -\begin{flushleft} -by means of a discrete Fourier transform (DFT). The peak power at the first six harmonics was estimated and, subsequently, the index of harmonicity was defined as ; \textbf{FORMULA} -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -where P0 is the power spectral density of the fundamental frequency (first harmonic) and \$ Pi the cumulative sum of power spectral density of the fundamental frequency and the first five superharmonics. A power ratio of 1 indicates that the rotation of the pelvis or the thorax is perfectly harmonic. In view of possible drift, which could lead to missing or widening peaks, the power spectral density of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of the peak frequency value. All power spectral densities were normalized by dividing the power by the sum of the total power spectrum, which equals the variance. -\end{flushleft} -\end{par} - -\label{H_3E396779} -\matlabheadingtwo{Lyapunov exponents (Wolfs vs. Rosenstein)} - -\label{H_B1B85E8D} -\begin{par} -\begin{flushleft} -The W-algorithm is advocated for use when examining local dynamic stability with small gait data sets. -\end{flushleft} -\end{par} - -\label{H_9FD2A70A} -\matlabheading{Visualize step detection;} - -\begin{par} -\begin{flushleft} -function [ResultStruct] = GaitVariabilityAnalysisIH\_WithoutTurns(inputData,FS,LegLength,ApplyRealignment,ApplyRemoveSteps); -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -\textbf{script for analysing straight parts} -\end{flushleft} -\end{par} - -\begin{par} -\begin{flushleft} -(1) Realign Data -\end{flushleft} -\end{par} - -\begin{matlabcode} -%% Realign data -data = inputData(:, [3,2,4]); % reorder data to 1 = V; 2= ML, 3 = AP - -%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). - % 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 - -\end{matlabcode} - -\begin{par} -\begin{flushleft} -(2) Filter Data -\end{flushleft} -\end{par} - -\begin{matlabcode} - -%% Filter data strongly & Determine location of steps - -% 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); - -\end{matlabcode} - -\begin{par} -\begin{flushleft} -(3) Determine Location of steps -\end{flushleft} -\end{par} - -\begin{matlabcode} -% Determine steps; - -%%%%%%% HIER MISSCHIEN ALTERNATIEF VOOR VAN RISPENS %%%%%%%%%%%%% - -% 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 = std(magNoGfilt); -[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2); % for step detection -numStepsOption2_filt = numel(pks); % counts number of steps; -\end{matlabcode} - -\begin{par} -\begin{flushleft} -(4) Determine location of turns -\end{flushleft} -\end{par} - -\begin{matlabcode} -%% Determine locations of turns; - -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(2); -findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',5); % these values have been chosen based on visual inspection of the signal -line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs]) -\end{matlabcode} -\begin{center} -\includegraphics[width=\maxwidth{56.196688409433015em}]{figure_1.eps} -\end{center} -\begin{matlabcode} -[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',5); -locsTurns = [locs(locs_diffLocs), locs(locs_diffLocs+1)]; - -\end{matlabcode} - -\begin{par} -\begin{flushleft} -(5) Visualize turns -\end{flushleft} -\end{par} - -\begin{matlabcode} -%% Visualizing turns - -% Duplying signal + visualing -% to make second signal with the locations of the turns filled with NaN, so -% that both signals can be plotted above each other in a different colour - -magNoGfilt_copy = magNoGfilt; -for k = 1: size(locsTurns,1); - magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN; -end - - -% visualising signal; -figure; -subplot(2,1,1) -hold on; -plot(magNoGfilt,'b') -plot(magNoGfilt_copy, 'r'); -title('Inside Straight: Filtered data with turns highlighted in blue') -hold off; -\end{matlabcode} -\begin{center} -\includegraphics[width=\maxwidth{56.196688409433015em}]{figure_2.eps} -\end{center} -\begin{matlabcode} - -\end{matlabcode} - -\begin{par} -\begin{flushleft} -(6) CALCULATIONS -\end{flushleft} -\end{par} - -\begin{matlabcode} -%% Calculation -% VRAAG LAURENS zie blauwe blaadje - -startPos = 1; -for i = 1: size(locsTurns,1); - endPos = locsTurns(i,1)-1; - - inputData = dataAcc(startPos:endPos,:); - WindowLen = size(inputData,1); - ApplyRealignment = false; - [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps); % Naam van deze moet nog aangepast. - - if i ==1 % only the firs time - Parameters = fieldnames(ResultStruct); - NrParameters = length(Parameters); - end - - for j = 1:NrParameters % only works if for every bin we get the same outcomes (which is the case in this script) - DataStraight.([char(Parameters(j))])(i) = ResultStruct.([char(Parameters(j))]); - end - startPos = locsTurns(i,2)+1; - -end - -clear ResultStruct; - -% Calculate mean over the bins without turns to get 1 outcome value per parameter for inside -% straight; - -for j = 1:NrParameters; - ResultStruct.([char(Parameters(j))]) = nanmean(DataStraight.([char(Parameters(j))])) -end -\end{matlabcode} -\begin{matlaboutput} -ResultStruct = - StrideRegularity_V: 0.9005 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - LyapunovPerStrideRosen_V: 1.5914 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - LyapunovPerStrideRosen_V: 1.5914 - LyapunovPerStrideRosen_ML: 1.2964 - -ResultStruct = - StrideRegularity_V: 0.9005 - StrideRegularity_ML: 0.7110 - StrideRegularity_AP: 0.6643 - StrideRegularity_All: 0.7765 - RelativeStrideVariability_V: 0.0995 - RelativeStrideVariability_ML: 0.2890 - RelativeStrideVariability_AP: 0.3357 - RelativeStrideVariability_All: 0.2235 - StrideTimeSamples: 217.5556 - StrideTimeSeconds: 2.1756 - GaitSymm_V: 5.7952 - GaitSymm_AP: 7.0128 - GaitSymmIndex: 2.3916 - StepLengthMean: 11.9893 - Distance: 83.0819 - WalkingSpeedMean: 6.1785 - StrideTimeVariability: 0.0200 - StrideSpeedVariability: 0.1990 - StrideLengthVariability: 0.5502 - StrideTimeVariabilityOmitOutlier: 6.0071 - StrideSpeedVariabilityOmitOutlier: 0.2542 - StrideLengthVariabilityOmitOutlier: 0.3722 - IndexHarmonicity_V: 0.4981 - IndexHarmonicity_ML: 0.7579 - IndexHarmonicity_AP: 0.8968 - IndexHarmonicity_All: 0.6349 - HarmonicRatio_V: 3.6118 - HarmonicRatio_ML: 3.4148 - HarmonicRatio_AP: 2.5141 - HarmonicRatioP_V: 21.1829 - HarmonicRatioP_ML: 19.5829 - HarmonicRatioP_AP: 12.5052 - FrequencyVariability_V: 0.2160 - FrequencyVariability_ML: 0.4381 - FrequencyVariability_AP: 0.1981 - StrideFrequency: 0.6364 - LyapunovWolf_V: 1.4401 - LyapunovWolf_ML: 1.2904 - LyapunovWolf_AP: 1.5238 - LyapunovRosen_V: 0.9931 - LyapunovRosen_ML: 0.8064 - LyapunovRosen_AP: 1.0984 - SampleEntropy_V: 0.2014 - SampleEntropy_ML: 0.2358 - SampleEntropy_AP: 0.2608 - LyapunovPerStrideWolf_V: 2.3378 - LyapunovPerStrideWolf_ML: 2.0830 - LyapunovPerStrideWolf_AP: 2.4095 - LyapunovPerStrideRosen_V: 1.5914 - LyapunovPerStrideRosen_ML: 1.2964 - LyapunovPerStrideRosen_AP: 1.7481 - -\end{matlaboutput} - -\label{H_4847D0D3} -\matlabheading{AggregateFunction;} - -\end{document} diff --git a/GaitVariabilityAnalysisLD_v1.m b/GaitVariabilityAnalysisLD_v1.m deleted file mode 100644 index 8a47423..0000000 --- a/GaitVariabilityAnalysisLD_v1.m +++ /dev/null @@ -1,103 +0,0 @@ -%% Gait Variability Analysis CLBP - -% Gait Variability Analysis -% Script created for MAP 2020-2021 -% adapted from Claudine Lamoth and Iris Hagoort -% version1 October 2020 - -% Input: needs mat file which contains all raw accelerometer data -% Input: needs excel file containing the participant information including -% leg length. -%% Clear and close; - -clear; -close all; -%% Load data; -% Select 1 trial. -% For loop to import all data will be used at a later stage - -[FNaam,FilePad] = uigetfile('*.xls','Load phyphox data...'); -filename =[FilePad FNaam]; -PhyphoxData = xlsread(filename) - -%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData -%load('ExcelInfo.mat'); -%Participants = fields(AccData); -%% Settings; -%adapted from GaitOutcomesTrunkAccFuncIH - -LegLength = 98; % LegLength info not available! -FS = 100; % Sample frequency - -Gr = 9.81; % Gravity acceleration, multiplication factor for accelerations -StrideFreqEstimate = 1.00; % Used to set search for stride frequency from 0.5*StrideFreqEstimate until 2*StrideFreqEstimate -StrideTimeRange = [0.2 4.0]; % Range to search for stride time (seconds) -IgnoreMinMaxStrides = 0.10; % Number or percentage of highest&lowest values ignored for improved variability estimation -N_Harm = 12; % Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation -LowFrequentPowerThresholds = ... - [0.7 1.4]; % Threshold frequencies for estimation of low-frequent power percentages -Lyap_m = 7; % Embedding dimension (used in Lyapunov estimations) -Lyap_FitWinLen = round(60/100*FS); % Fitting window length (used in Lyapunov estimations Rosenstein's method) -Sen_m = 5; % Dimension, the length of the subseries to be matched (used in sample entropy estimation) -Sen_r = 0.3; % Tolerance, the maximum distance between two samples to qualify as match, relative to std of DataIn (used in sample entropy estimation) -NStartEnd = [100]; -M = 5; % Maximum template length -ResultStruct = struct(); % Empty struct - - -inputData = (PhyphoxData(:,[1 2 3 4])); % matrix with accelerometer data -ApplyRealignment = true; -ApplyRemoveSteps = true; -WindowLen = FS*10; - - -%% Filter and Realign Accdata -% dataAcc depends on ApplyRealignment = true/false -% dataAcc_filt (low pass Butterworth Filter + zerophase filtering - -[dataAcc, dataAcc_filt] = FilterandRealignFunc(inputData,FS,ApplyRealignment); - -%% Step dectection -% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed -% StrideTimeSamples is needed for calculation stride parameters! - -[dataAccCut,dataAccCut_filt,StrideTimeSamples] = StepDetectionFunc(FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange); - -%% Calculate Stride Parameters -% Outcomeparameters: StrideRegularity,RelativeStrideVariability,StrideTimeSamples,StrideTime - -[ResultStruct] = CalculateStrideParametersFunc(dataAccCut_filt,FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange); - -%% Calculate spatiotemporal stride parameters -% Measures from height variation by double integration of VT accelerations and high-pass filtering -% StepLengthMean; Distance; WalkingSpeedMean; StrideTimeVariability; StrideSpeedVariability; -% StrideLengthVariability; StrideTimeVariabilityOmitOutlier; StrideSpeedVariabilityOmitOutlier; StrideLengthVariabilityOmitOutlier; - -[ResultStruct] = SpatioTemporalGaitParameters(dataAccCut_filt,StrideTimeSamples,ApplyRealignment,LegLength,FS,IgnoreMinMaxStrides,ResultStruct); - -%% Measures derived from spectral analysis -% IndexHarmonicity_V/ML/AP/ALL ; HarmonicRatio_V/ML/AP ; HarmonicRatioP_V/ML/AP ; FrequencyVariability_V/ML/AP; Stride Frequency - -AccVectorLen = sqrt(sum(dataAccCut_filt(:,1:3).^2,2)); -[ResultStruct] = SpectralAnalysisGaitfunc(dataAccCut_filt,WindowLen,FS,N_Harm,LowFrequentPowerThresholds,AccVectorLen,ResultStruct); - -%% calculate non-linear parameters -% Outcomeparameters: Sample Entropy, Lyapunov exponents - -[ResultStruct] = CalculateNonLinearParametersFunc(ResultStruct,dataAccCut,WindowLen,FS,Lyap_m,Lyap_FitWinLen,Sen_m,Sen_r); - -% Save struct as .mat file -% save('GaitVarOutcomesParticipantX.mat', 'OutcomesAcc'); - - - - - - - -%% AggregateFunction (seperate analysis per minute); -% see AggregateEpisodeValues.m -% -% - - diff --git a/HarmonicityFrequency.m b/HarmonicityFrequency.m index 1d99680..1a51728 100644 --- a/HarmonicityFrequency.m +++ b/HarmonicityFrequency.m @@ -1,6 +1,9 @@ function [ResultStruct] = HarmonicityFrequency(dataAccCut_filt,P,F, StrideFrequency,dF,LowFrequentPowerThresholds,N_Harm,FS,AccVectorLen,ResultStruct) +%LD 15-04-2021: Solve problem finding fundamental frequency in power +%spectral density STRIDE - VS STEP frequency. + % Add sum of power spectra (as a rotation-invariant spectrum) P = [P,sum(P,2)]; PS = sqrt(P); @@ -8,6 +11,7 @@ PS = sqrt(P); % Calculate the measures for the power per separate dimension for i=1:size(P,2); % Relative cumulative power and frequencies that correspond to these cumulative powers + PCumRel = cumsum(P(:,i))/sum(P(:,i)); PSCumRel = cumsum(PS(:,i))/sum(PS(:,i)); FCumRel = F+0.5*dF; @@ -21,12 +25,12 @@ for i=1:size(P,2); PHarm = zeros(N_Harm,1); PSHarm = zeros(N_Harm,1); for Harm = 1:N_Harm, - FHarmRange = (Harm+[-0.1 0.1])*StrideFrequency; + FHarmRange = (Harm+[-0.1 0.1])*(StrideFrequency); % In view of possible drift, which could lead to missing or widening peaks, the power spectral density of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of the peak frequency value. PHarm(Harm) = diff(interp1(FCumRel,PCumRel,FHarmRange)); PSHarm(Harm) = diff(interp1(FCumRel,PSCumRel,FHarmRange)); end - % Derive index of harmonicity + % Derive index of harmonicity; which is the spectral power of the basic harmonic divided by the sum of the power of first six harmonics if i == 2 % for ML we expect odd instead of even harmonics IndexHarmonicity(i) = PHarm(1)/sum(PHarm(1:2:12)); elseif i == 4 @@ -55,7 +59,8 @@ for i=1:size(P,2); FrequencyVariability(i) = nansum(StrideFreqFluctuation./(1:N_Harm)'.*PHarm)/nansum(PHarm); if i<4, - % Derive harmonic ratio (two variants) + % Derive harmonic ratio (two variants) --> Ratio of the even and + % odd harmonics! if i == 2 % for ML we expect odd instead of even harmonics HarmonicRatio(i) = sum(PSHarm(1:2:end-1))/sum(PSHarm(2:2:end)); % relative to summed 3d spectrum HarmonicRatioP(i) = sum(PHarm(1:2:end-1))/sum(PHarm(2:2:end)); % relative to own spectrum @@ -70,14 +75,6 @@ end ResultStruct.IndexHarmonicity_V = IndexHarmonicity(1); % higher smoother more regular patter ResultStruct.IndexHarmonicity_ML = IndexHarmonicity(2); % higher smoother more regular patter ResultStruct.IndexHarmonicity_AP = IndexHarmonicity(3); % higher smoother more regular patter - ResultStruct.IndexHarmonicity_All = IndexHarmonicity(4); ResultStruct.HarmonicRatio_V = HarmonicRatio(1); - ResultStruct.HarmonicRatio_ML = HarmonicRatio(2); ResultStruct.HarmonicRatio_AP = HarmonicRatio(3); - ResultStruct.HarmonicRatioP_V = HarmonicRatioP(1); - ResultStruct.HarmonicRatioP_ML = HarmonicRatioP(2); - ResultStruct.HarmonicRatioP_AP = HarmonicRatioP(3); - ResultStruct.FrequencyVariability_V = FrequencyVariability(1); - ResultStruct.FrequencyVariability_ML = FrequencyVariability(2); - ResultStruct.FrequencyVariability_AP = FrequencyVariability(3); ResultStruct.StrideFrequency = StrideFrequency; \ No newline at end of file diff --git a/Hratio.m b/Hratio.m new file mode 100644 index 0000000..ba6c435 --- /dev/null +++ b/Hratio.m @@ -0,0 +1,55 @@ +function [Hratio,wb]=Hratio(x,fmax,numfreq) +% function [HIndex]=IndexOfHarmonicity(x,numfreq); +% +% INPUT, signal, fmax is max frequency that can be considered as main +% frequency = around 1 for stride and around 2 for steps. +% numfreq = number of superharmonics in analysis +% OUTPUT: Harmoncity Index and mainfrequency +% PLOT: with no output a plot is generated +% CALLS: WndMainFreq.m +% CL 2016 + + +if nargin<3 || isempty(numfreq), numfreq = 10; end + +fs = 100;% sample frequency +le=length(x); +temp=[]; + n=10*length(x); + ws=fix(length(x)/2); + no=fix(0.99*ws); + +[Px,fx]=spectrum2(x,n,no,boxcar(le),100,'mean'); +Px = Px/sum(Px); % All power spectral densities were normalized by dividing the power by the sum of the total power spectrum, which equals the variance. + +% select frequency +wb = WindMainFreq(x,100,fmax) ; % mainfrequency frequency +wbr= wb + 0.055; % % basis mainfrequency + tail +wbl = wb - 0.055; + +wind=find(fx >=wbl & fx<= wbr); % index van basisfrequentie + +PI_0=sum(Px(wind)); + +temp=PI_0; +for k=1:numfreq; + w=wb*(k+1); + wr=w+0.055; + wl=w-0.055; + wInd=find(fx >=wl & fx<=wr); + if nargout ==0; % controle + plot(fx,Px,'r'); hold on; + set(gca','XLim', [0 6],'FontSize', 8) + xlabel('Frequency (Hz)'); ylabel('Power'); + plot(wb,max(Px),'r.','MarkerSize', 10), + plot(wbr,0,'c.'); plot(wbl,0,'c.'); + plot(w,0,'r.','MarkerSize', 10) + plot(wl,0,'c.'); plot(wr,0,'c.'); + end + + PI=sum(Px(wInd)); + temp=[temp; PI]; + +end + + Hratio = sum(temp(1:2:end-1))/sum(temp(2:2:end)); diff --git a/IndexOfHarmonicity.m b/IndexOfHarmonicity.m new file mode 100644 index 0000000..b42933c --- /dev/null +++ b/IndexOfHarmonicity.m @@ -0,0 +1,54 @@ +function [HIndex,wb]=IndexOfHarmonicity(x,fmax,numfreq) +% function [HIndex]=IndexOfHarmonicity(x,numfreq); +% +% INPUT, signal, fmax is max frequency that can be considered as main +% frequency = around 1 for stride and around 2 for steps. +% numfreq = number of superharmonics in analysis +% OUTPUT: Harmoncity Index and mainfrequency +% PLOT: with no output a plot is generated +% CALLS: WndMainFreq.m +% CL 2016 + + +if nargin<3 || isempty(numfreq), numfreq = 10; end + +fs = 100;% sample frequency +le=length(x); +temp=[]; + n=10*length(x); + ws=fix(length(x)/2); + no=fix(0.99*ws); + +[Px,fx]=spectrum2(x,n,no,boxcar(le),100,'mean'); +Px = Px/sum(Px); % All power spectral densities were normalized by dividing the power by the sum of the total power spectrum, which equals the variance. + +% select frequency +wb = WindMainFreq(x,100,fmax) ; % mainfrequency frequency +wbr= wb + 0.055; % % basis mainfrequency + tail +wbl = wb - 0.055; + +wind=find(fx >=wbl & fx<= wbr); % index van basisfrequentie + +PI_0=sum(Px(wind)); + +temp=PI_0; +for k=1:numfreq; + w=wb*(k+1); + wr=w+0.055; + wl=w-0.055; + wInd=find(fx >=wl & fx<=wr); + if nargout ==0; % controle + plot(fx,Px,'r'); hold on; + set(gca','XLim', [0 6],'FontSize', 8) + xlabel('Frequency (Hz)'); ylabel('Power'); + plot(wb,max(Px),'r.','MarkerSize', 10), + plot(wbr,0,'c.'); plot(wbl,0,'c.'); + plot(w,0,'r.','MarkerSize', 10) + plot(wl,0,'c.'); plot(wr,0,'c.'); + end + + PI=sum(Px(wInd)); + temp=[temp; PI]; + +end + HIndex=PI_0/sum(temp); diff --git a/Main_GaitVariabilityAnalysis_LD.mlx b/Main_GaitVariabilityAnalysis_LD.mlx new file mode 100644 index 0000000..740dba7 Binary files /dev/null and b/Main_GaitVariabilityAnalysis_LD.mlx differ diff --git a/MutualInformationHisPro.m b/MutualInformationHisPro.m index 18c0830..6ceea4d 100644 --- a/MutualInformationHisPro.m +++ b/MutualInformationHisPro.m @@ -22,7 +22,7 @@ function [mutM,cummutM,minmuttauV] = MutualInformationHisPro(xV,tauV,bV,flag) % delays. % - cummutM : the vector of the cumulative mutual information values for % the given delays -% - minmuttauV : the time of the first minimum of the mutual information. +% - : the time of the first minimum of the mutual information. %======================================================================== % , v 1.0 2010/02/11 22:09:14 Kugiumtzis & Tsimpiris % This is part of the MATS-Toolkit http://eeganalysis.web.auth.gr/ diff --git a/Phyphoxdata.mat b/Phyphoxdata.mat deleted file mode 100644 index 871ebbb..0000000 Binary files a/Phyphoxdata.mat and /dev/null differ diff --git a/SpatioTemporalGaitParameters.m b/SpatioTemporalGaitParameters.m index 2bfde40..0204232 100644 --- a/SpatioTemporalGaitParameters.m +++ b/SpatioTemporalGaitParameters.m @@ -1,17 +1,23 @@ function [ResultStruct] = SpatioTemporalGaitParameters(dataAccCut_filt,StrideTimeSamples,ApplyRealignment,LegLength,FS,IgnoreMinMaxStrides,ResultStruct); +%% Method Zijlstra & Hof +% Mean step length and mean walking speed are estimated using the upward +% and downward momvements of the trunk. Assuming a compass gait type, CoM +% movements in the sagittal plane follow a circular trajectory during each +% single support phase. In this inverted pendlum model, changes in height +% of CoM depend on step length. Thus, when changes in height are known, +% step length can be predicted from geometrical characteristics as in Cutoff = 0.1; MinDist = floor(0.7*0.5*StrideTimeSamples); % Use StrideTimeSamples estimated above -DatalFilt = dataAccCut_filt; +DatalFilt = dataAccCut_filt; % From acceleration to velocity Vel = cumsum(detrend(DatalFilt,'constant'))/FS; -[B,A] = butter(2,Cutoff/(FS/2),'high'); +[B,A] = butter(2,Cutoff/(FS/2),'high'); % To avoid integration drift, position data were high-pass filtered Pos = cumsum(filtfilt(B,A,Vel))/FS; PosFilt = filtfilt(B,A,Pos); -PosFiltVT = PosFilt(:,1); +PosFiltVT = PosFilt(:,1); % Changes in vertical position were calculated by a double integration of Y. -% Find minima and maxima in vertical position % if ~ApplyRealignment % Signals were not realigned, so it has to be done here % MeanAcc = mean(AccLoco); @@ -28,7 +34,8 @@ if isempty(PosPks) && isempty(NegPks) else PksAndLocs = sortrows([PosPks,PosLocs,ones(size(PosPks)) ; NegPks,NegLocs,-ones(size(NegPks))], 2); end -% Correct events for two consecutive maxima or two consecutive minima +%% Correct events for two consecutive maxima or two consecutive minima + Events = PksAndLocs(:,2); NewEvents = PksAndLocs(:,2); Signs = PksAndLocs(:,3); @@ -77,9 +84,9 @@ DH(DH>MaxDH) = MaxDH; % (Use delta h and delta t to calculate walking speed: use formula from % Z&H, but divide by 2 (skip factor 2)since we get the difference twice % each step, and multiply by 1.25 which is the factor suggested by Z&H) -HalfStepLen = 1.25*sqrt(2*LegLength*DH-DH.^2); +HalfStepLen = 1.25*sqrt(2*LegLength*DH-DH.^2); % Formulae is correct! Distance = sum(HalfStepLen); -WalkingSpeedMean = Distance/(sum(DT)/FS); +WalkingSpeedMean = Distance/(sum(DT)/FS); % Gait Speed (m/s) average walking speed % Estimate variabilities between strides StrideLengths = HalfStepLen(1:end-3) + HalfStepLen(2:end-2) + HalfStepLen(3:end-1) + HalfStepLen(4:end); StrideTimes = PksAndLocsCorrected(5:end,2)-PksAndLocsCorrected(1:end-4,2); @@ -91,28 +98,25 @@ for i=1:4, WSS(i) = std(StrideSpeeds(i:4:end)); end -StepLengthMean=mean(StrideLengths); +StepLengthMean=mean(StrideLengths)/2; % 9-4-2021 LD, previous version with dividing by 2, THEN THIS SHOULD BE STRIDELENGTHMEAN!! +StrideLengthMean = mean(StrideLengths); +StrideTimeMean = mean(StrideTimes)/FS; % Samples to seconds +StrideSpeedMean = mean(StrideSpeeds); StrideTimeVariability = min(STS); StrideSpeedVariability = min(WSS); StrideLengthVariability = std(StrideLengths); -% Estimate Stride time variability and stride speed variability by removing highest and lowest part -if ~isinteger(IgnoreMinMaxStrides) - IgnoreMinMaxStrides = ceil(IgnoreMinMaxStrides*size(StrideTimes,1)); -end -StrideTimesSorted = sort(StrideTimes); -StrideTimeVariabilityOmitOutlier = std(StrideTimesSorted(1+IgnoreMinMaxStrides:end-IgnoreMinMaxStrides)); -StrideSpeedSorted = sort(StrideSpeeds); -StrideSpeedVariabilityOmitOutlier = std(StrideSpeedSorted(1+IgnoreMinMaxStrides:end-IgnoreMinMaxStrides)); -StrideLengthsSorted = sort(StrideLengths); -StrideLengthVariabilityOmitOutlier = std(StrideLengthsSorted(1+IgnoreMinMaxStrides:end-IgnoreMinMaxStrides)); -ResultStruct.StepLengthMean = StepLengthMean; -ResultStruct.Distance = Distance; -ResultStruct.WalkingSpeedMean = WalkingSpeedMean; -ResultStruct.StrideTimeVariability = StrideTimeVariability; -ResultStruct.StrideSpeedVariability = StrideSpeedVariability; -ResultStruct.StrideLengthVariability = StrideLengthVariability; -ResultStruct.StrideTimeVariabilityOmitOutlier = StrideTimeVariabilityOmitOutlier; -ResultStruct.StrideSpeedVariabilityOmitOutlier = StrideSpeedVariabilityOmitOutlier; -ResultStruct.StrideLengthVariabilityOmitOutlier = StrideLengthVariabilityOmitOutlier; \ No newline at end of file +% LD 16-03-2021 - Calculate CoV as ''coefficient of relative variability'' +CoVStrideTime = (nanstd(StrideTimes)/nanmean(StrideTimes))*100; +CoVStrideSpeed = (nanstd(StrideSpeeds)/nanmean(StrideSpeeds))*100; +CoVStrideLength = (nanstd(StrideLengths)/nanmean(StrideLengths))*100; + +% ResultStruct +ResultStruct.WalkingSpeedMean = WalkingSpeedMean; + +ResultStruct.StrideTimeMean = StrideTimeMean; +ResultStruct.CoVStrideTime = CoVStrideTime; + +ResultStruct.StrideLengthMean = StrideLengthMean; +ResultStruct.CoVStrideLength = CoVStrideLength; \ No newline at end of file diff --git a/SpectralAnalysisGaitfunc.m b/SpectralAnalysisGaitfunc.m index cfdf00e..d439a1c 100644 --- a/SpectralAnalysisGaitfunc.m +++ b/SpectralAnalysisGaitfunc.m @@ -2,14 +2,18 @@ function [ResultStruct] = SpectralAnalysisGaitfunc(dataAccCut_filt,WindowLen,FS, P=zeros(0,size(dataAccCut_filt,2)); -for i=1:size(dataAccCut_filt,2) +for i=1:size(dataAccCut_filt,2) % pwelch = Welch’s power spectral density estimate [P1,~] = pwelch(dataAccCut_filt(:,i),hamming(WindowLen),[],WindowLen,FS); - [P2,F] = pwelch(dataAccCut_filt(end:-1:1,i),hamming(WindowLen),[],WindowLen,FS); + [P2,F] = pwelch(dataAccCut_filt(end:-1:1,i),hamming(WindowLen),[],WindowLen,FS); % data P(1:numel(P1),i) = (P1+P2)/2; end -dF = F(2)-F(1); +dF = F(2)-F(1); % frequencies % Calculate stride frequency and peak widths [StrideFrequency, ~, PeakWidth, MeanNormalizedPeakWidth] = StrideFrequencyRispen(P,F); [ResultStruct] = HarmonicityFrequency(dataAccCut_filt, P,F, StrideFrequency,dF,LowFrequentPowerThresholds,N_Harm,FS,AccVectorLen,ResultStruct); + +ResultStruct.IH_VT = IndexOfHarmonicity(dataAccCut_filt(:,1),(5*StrideFrequency),10); % expect fmax at step frequency (2* stridefrequency), what happens if you increase fmax (~ PSD) +ResultStruct.IH_ML = IndexOfHarmonicity(dataAccCut_filt(:,2),(5*StrideFrequency),10); % expect fmax at stride frequency +ResultStruct.IH_AP = IndexOfHarmonicity(dataAccCut_filt(:,3),(5*StrideFrequency),10); % expect fmax at step frequency (2* stridefrequency) end \ No newline at end of file diff --git a/StepcountFunc.m b/StepcountFunc.m index 3f4d542..7025890 100644 --- a/StepcountFunc.m +++ b/StepcountFunc.m @@ -1,65 +1,83 @@ -function [PksAndLocsCorrected] = StepcountFunc(dataAcc_filt,StrideTimeSamples,FS); - -% Step count funciton extracted from other function called: +function [PksAndLocsCorrected] = StepcountFunc(dataAcc,StrideTimeSamples,FS); Cutoff = 0.1; MinDist = floor(0.7*0.5*StrideTimeSamples); % Use StrideTimeSamples estimated above -DataLFilt = dataAcc_filt; -% From acceleration to -Vel = cumsum(detrend(DataLFilt,'constant'))/FS; -[B,A] = butter(2,Cutoff/(FS/2),'high'); -Pos = cumsum(filtfilt(B,A,Vel))/FS; -PosFilt = filtfilt(B,A,Pos); -PosFiltVT = PosFilt(:,1); +%% USE THE AP ACCELERATION: WIEBREN ZIJLSTRA: ASSESSMENT OF SPATIO-TEMPORAL PARAMTERS DURING UNCONSTRAINED WALKING (2004) -% Find minima and maxima in vertical position -[PosPks,PosLocs] = findpeaks(PosFiltVT(:,1),'minpeakdistance',MinDist); -[NegPks,NegLocs] = findpeaks(-PosFiltVT(:,1),'minpeakdistance',MinDist); +[B,A] = butter(4,5/(FS/2),'low'); % According to article Lamoth: Multiple gait parameters derived from iPod accelerometry predict age-related gait changes +dataAcc = filtfilt(B,A,dataAcc); +APAcceleration = dataAcc(:,3); + +% Find minima and maxima in AP acceleration signal +[PosPks,PosLocs] = findpeaks(APAcceleration(:,1),'minpeakdistance',MinDist); +[NegPks,NegLocs] = findpeaks(-APAcceleration(:,1),'minpeakdistance',MinDist); NegPks = -NegPks; if isempty(PosPks) && isempty(NegPks) PksAndLocs = zeros(0,3); else PksAndLocs = sortrows([PosPks,PosLocs,ones(size(PosPks)) ; NegPks,NegLocs,-ones(size(NegPks))], 2); end -% Correct events for two consecutive maxima or two consecutive minima -Events = PksAndLocs(:,2); -NewEvents = PksAndLocs(:,2); -Signs = PksAndLocs(:,3); -FalseEventsIX = find(diff(Signs)==0); -PksAndLocsToAdd = zeros(0,3); -PksAndLocsToAddNr = 0; -for i=1:numel(FalseEventsIX), - FIX = FalseEventsIX(i); - if FIX <= 2 - % remove the event - NewEvents(FIX) = nan; - elseif FIX >= numel(Events)-2 - % remove the next event - NewEvents(FIX+1) = nan; - else - StrideTimesWhenAdding = [Events(FIX+1)-Events(FIX-2),Events(FIX+3)-Events(FIX)]; - StrideTimesWhenRemoving = Events(FIX+3)-Events(FIX-2); - if max(abs(StrideTimesWhenAdding-StrideTimeSamples)) < abs(StrideTimesWhenRemoving-StrideTimeSamples) - % add an event - [M,IX] = min(Signs(FIX)*PosFiltVT((Events(FIX)+1):(Events(FIX+1)-1))); - PksAndLocsToAddNr = PksAndLocsToAddNr+1; - PksAndLocsToAdd(PksAndLocsToAddNr,:) = [M,Events(FIX)+IX,-Signs(FIX)]; - else - % remove an event - if FIX >= 5 && FIX <= numel(Events)-5 - ExpectedEvent = (Events(FIX-4)+Events(FIX+5))/2; - else - ExpectedEvent = (Events(FIX-2)+Events(FIX+3))/2; - end - if abs(Events(FIX)-ExpectedEvent) > abs(Events(FIX+1)-ExpectedEvent) - NewEvents(FIX) = nan; - else - NewEvents(FIX+1) = nan; - end - end - end -end -PksAndLocsCorrected = sortrows([PksAndLocs(~isnan(NewEvents),:);PksAndLocsToAdd],2); -end \ No newline at end of file +PksAndLocsCorrected = PosLocs; + +end +% DataLFilt = dataAcc_filt; + + +% % From acceleration to +% Vel = cumsum(detrend(DataLFilt,'constant'))/FS; +% [B,A] = butter(2,Cutoff/(FS/2),'high'); +% Pos = cumsum(filtfilt(B,A,Vel))/FS; +% PosFilt = filtfilt(B,A,Pos); +% PosFiltVT = PosFilt(:,1); + +% % Find minima and maxima in vertical position +% [PosPks,PosLocs] = findpeaks(PosFiltVT(:,1),'minpeakdistance',MinDist); +% [NegPks,NegLocs] = findpeaks(-PosFiltVT(:,1),'minpeakdistance',MinDist); +% NegPks = -NegPks; +% if isempty(PosPks) && isempty(NegPks) +% PksAndLocs = zeros(0,3); +% else +% PksAndLocs = sortrows([PosPks,PosLocs,ones(size(PosPks)) ; NegPks,NegLocs,-ones(size(NegPks))], 2); +% end + +% % Correct events for two consecutive maxima or two consecutive minima +% Events = PksAndLocs(:,2); +% NewEvents = PksAndLocs(:,2); +% Signs = PksAndLocs(:,3); +% FalseEventsIX = find(diff(Signs)==0); +% PksAndLocsToAdd = zeros(0,3); +% PksAndLocsToAddNr = 0; +% for i=1:numel(FalseEventsIX), +% FIX = FalseEventsIX(i); +% if FIX <= 2 +% % remove the event +% NewEvents(FIX) = nan; +% elseif FIX >= numel(Events)-2 +% % remove the next event +% NewEvents(FIX+1) = nan; +% else +% StrideTimesWhenAdding = [Events(FIX+1)-Events(FIX-2),Events(FIX+3)-Events(FIX)]; +% StrideTimesWhenRemoving = Events(FIX+3)-Events(FIX-2); +% if max(abs(StrideTimesWhenAdding-StrideTimeSamples)) < abs(StrideTimesWhenRemoving-StrideTimeSamples) +% % add an event +% [M,IX] = min(Signs(FIX)*APAcceleration((Events(FIX)+1):(Events(FIX+1)-1))); % APAcceleration used to be PosFiltVT +% PksAndLocsToAddNr = PksAndLocsToAddNr+1; +% PksAndLocsToAdd(PksAndLocsToAddNr,:) = [M,Events(FIX)+IX,-Signs(FIX)]; +% else +% % remove an event +% if FIX >= 5 && FIX <= numel(Events)-5 +% ExpectedEvent = (Events(FIX-4)+Events(FIX+5))/2; +% else +% ExpectedEvent = (Events(FIX-2)+Events(FIX+3))/2; +% end +% if abs(Events(FIX)-ExpectedEvent) > abs(Events(FIX+1)-ExpectedEvent) +% NewEvents(FIX) = nan; +% else +% NewEvents(FIX+1) = nan; +% end +% end +% end +% end +% PksAndLocsCorrected = sortrows([PksAndLocs(~isnan(NewEvents),:);PksAndLocsToAdd],2); diff --git a/StrideFrequencyRispen.m b/StrideFrequencyRispen.m index e5ae4fa..5589548 100644 --- a/StrideFrequencyRispen.m +++ b/StrideFrequencyRispen.m @@ -5,7 +5,8 @@ function [StrideFrequency, QualityInd, PeakWidth, MeanNormalizedPeakWidth] = Str % pwelch spectral densities % % Input: -% AccXYZ: a three-dimensional time series with trunk accelerations +% AccXYZ: a three-dimensional time series with trunk accelerations (is +% for error) % FS: the sample frequency of the time series % StrideFreqGuess: a first guess of the stride frequency % @@ -35,6 +36,15 @@ function [StrideFrequency, QualityInd, PeakWidth, MeanNormalizedPeakWidth] = Str %% History % February 2013, version 1.1, adapted from StrideFrequencyFrom3dAcc +%% Additional information +% Stride frequency was estimated from the median of the modal +% frequencies (half the modal frequency for VT and AP) for all three +% directions. Whenever the median of the three frequencies fell +% outside the range 0.6–1.2 Hz, it was replaced by one of the other +% two frequencies, if available, within this range. Finally, if all modal +% frequencies were within 10% of an integer multiple of the resulting +% frequency, it was replaced by the mean of the three associated base +% frequencies. frequencies. %% Check input if size(AccXYZ,2) ~= 3 @@ -44,7 +54,7 @@ elseif size(AccXYZ,1) < 10*F end -%% Get PSD +%% Get PSD (Power Spectral Density) if numel(F) == 1, % Calculate the PSD from time series AccXYZ, F is the sample frequency AccFilt = detrend(AccXYZ,'constant'); % Detrend data to get rid of DC component in most of the specific windows LenPSD = 10*F; @@ -57,13 +67,13 @@ elseif numel(F)==size(AccXYZ,1), % F are the frequencies of the power spectrum A Fwf = F; Pwf = AccXYZ; end -Pwf(:,4) = sum(Pwf,2); +Pwf(:,4) = sum(Pwf,2); % if input = F %% Estimate stride frequency % set parameters -HarmNr = [2 1 2]; -CommonRange = [0.6 1.2]; +HarmNr = [2 1 2]; % AP / V --> Biphasic +CommonRange = [0.6 1.2]; % Whenever the median of the three frequencies fell outside the range 0.6–1.2 Hz, it was replaced by one of the other two frequencies, % Get modal frequencies and the 'mean freq. of the peak' for i=1:4, MF1I = find([zeros(5,1);Pwf(6:end,i)]==max([zeros(5,1);Pwf(6:end,i)]),1); diff --git a/WindMainFreq.m b/WindMainFreq.m new file mode 100644 index 0000000..0aa398d --- /dev/null +++ b/WindMainFreq.m @@ -0,0 +1,26 @@ +function Omega=WindMainFreq(x,fs,fmax); +% function Omega=WindMainFreq(x,fs,fmax); +%INPUT: x = signal; fs = sample frequency; fmax = max frequency for +%mainfrequency +% OUTPUT = mainfrequency Omega +% CALSS: spectrum2.m +% PLOT : with no outtput [] generates plot + +if nargin<3 || isempty(fmax), fmax = 4; end + n=10*length(x); + ws=fix(length(x)/2); + no=fix(0.99*ws); + +[p,f]=spectrum2(x,n,no,hamming(ws),fs,'mean'); + p=p/sum(p); +aa=find(f>0.5 & f0 + y=y-mean(y); + sy=sqrt(mean(y.^2)); + y=y/sy; + x=x-mean(x); + sx=sqrt(mean(x.^2)); + x=x/sx; +end + +lastrun=zeros(nx,1); +run=zeros(nx,1); +A=zeros(M,1); +B=zeros(M,1); +p=zeros(M,1); +e=zeros(M,1); + + +for i=1:ny + for j=1:nx + if abs(x(j)-y(i)). %% Author % Sietse Rispens %% History % 23 October 2012, initial version based on div_wolf [N,D]=size(states); N_opt = N-evolv; states_opt = states(1:N_opt,:); One2N_opt=(1:N_opt)'; is=1; % search closest point to states(1,:), but further than min_dist away DeltaFidPoint = zeros(N_opt,D); for dim = 1:D, DeltaFidPoint(:,dim) = states_opt(:,dim)-states(is,dim); end DeltaFidPointSqr = DeltaFidPoint.^2; DistSqr = sum(DeltaFidPointSqr,2); Distances = sqrt(DistSqr); CosTh = zeros(N_opt,1); in = find(DistSqr==min(DistSqr(DistSqr>=min_dist^2 & abs(One2N_opt-is)>period)) & abs(One2N_opt-is)>period,1,'first'); if isempty(in) warning('Cannot calculate Wolf''s Lyapunov exponent, no states further than min_dist from starting point'); Lambda = nan; return; end DistStart = Distances(in); LnDistReplacementsSum = 0; for is=(1+evolv):evolv:N, in = in+evolv; % fill distance and cos matrices with new values DeltaOld = (states(in,:)-states(is,:))'; DistOld = sqrt(sum(DeltaOld.^2)); for dim = 1:D, DeltaFidPoint(:,dim) = states_opt(:,dim)-states(is,dim); end DeltaFidPointSqr(:,:) = DeltaFidPoint.^2; DistSqr(:,:) = sum(DeltaFidPointSqr,2); Distances(:,:) = sqrt(DistSqr); CosTh(:,:) = abs(DeltaFidPoint*DeltaOld)./DistOld./Distances; Index = []; search_theta = max_theta; while isempty(Index) && search_theta < pi dist_mult = 1; while isempty(Index) && dist_mult <= max_dist_mult search_dist = dist_mult*max_dist; Candidates = find(Distances <= search_dist & CosTh >= cos(search_theta) ... & Distances>= min_dist & abs(One2N_opt-is)>period); if ~isempty(Candidates) % find the best of candidates if numel(Candidates) > 1 Candidates = Candidates(CosTh(Candidates)==max(CosTh(Candidates))); if numel(Candidates) > 1 Candidates = Candidates(Distances(Candidates)==min(Distances(Candidates))); if numel(Candidates) > 1 Candidates = Candidates(abs(One2N_opt(Candidates)-is)==max(abs(One2N_opt(Candidates)-is))); end end end Index = Candidates(1); end dist_mult = dist_mult+1; end search_theta = search_theta*2; end if isempty(Index) && in > N_opt % we can not evolve the old point and no new one was found Index = find(DistSqr==min(DistSqr(DistSqr>=min_dist^2 & abs(One2N_opt-is)>period))& abs(One2N_opt-is)>period,1,'first'); if isempty(Index) warning('Cannot calculate Wolf''s Lyapunov exponent, no states further than min_dist from fiducial point'); Lambda = nan; return; end end if ~isempty(Index) LnDistReplacementsSum = LnDistReplacementsSum + log(DistOld/Distances(Index)); in = Index; end end DistEnd = sqrt(sum((states(is,:)-states(in,:)).^2)); Lambda = (log(DistEnd/DistStart) + LnDistReplacementsSum)/((is-1)/fs); \ No newline at end of file +function [Lambda]=div_wolf_fixed_evolv(states, fs, min_dist, max_dist, max_dist_mult, max_theta, period, evolv) %% Description % Calculate maximum lyapunov exponent from state space according to Wolf's method: % Wolf, A., et al., Determining Lyapunov exponents from a time series. % Physica D: 8 Nonlinear Phenomena, 1985. 16(3): p. 285-317. % % Input: % states: states in state space % fs: sample frequency % min_dist: neighbours should be more than this distandce away to be % selected (assumed noise amplitude ) % max_dist: maximum distance to which the divergence may grow before % replacing the neighbour % max_theta: maximum angle between vectors from feducial point to old and % new neighbours, this is the absolute maximum, first search % nearer % period: indicates period of time-wise near samples to exclude in % nearest neighbour search % evolv: time step after which the neighbour is replaced % % Output: % Lambda: the estimated Lyapunov exponent % ExtraArgs: optional struct containing additional info for debugging or % analysis of the method % %% Copyright % COPYRIGHT (c) 2012 Sietse Rispens, VU University Amsterdam % % This program is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with this program. If not, see . %% Author % Sietse Rispens %% History % 23 October 2012, initial version based on div_wolf [N,D]=size(states); N_opt = N-evolv; states_opt = states(1:N_opt,:); One2N_opt=(1:N_opt)'; is=1; % search closest point to states(1,:), but further than min_dist away DeltaFidPoint = zeros(N_opt,D); for dim = 1:D, DeltaFidPoint(:,dim) = states_opt(:,dim)-states(is,dim); end DeltaFidPointSqr = DeltaFidPoint.^2; DistSqr = sum(DeltaFidPointSqr,2); Distances = sqrt(DistSqr); CosTh = zeros(N_opt,1); in = find(DistSqr==min(DistSqr(DistSqr>=min_dist^2 & abs(One2N_opt-is)>period)) & abs(One2N_opt-is)>period,1,'first'); % Find states min_dist from starting point. if isempty(in) warning('Cannot calculate Wolf''s Lyapunov exponent, no states further than min_dist from starting point'); Lambda = nan; return; end DistStart = Distances(in); LnDistReplacementsSum = 0; % If multiple points are found in this range, the one which points best in % the same direction as the old nearby point is selected. for is=(1+evolv):evolv:N, in = in+evolv; % fill distance and cos matrices with new values DeltaOld = (states(in,:)-states(is,:))'; DistOld = sqrt(sum(DeltaOld.^2)); for dim = 1:D, DeltaFidPoint(:,dim) = states_opt(:,dim)-states(is,dim); end DeltaFidPointSqr(:,:) = DeltaFidPoint.^2; DistSqr(:,:) = sum(DeltaFidPointSqr,2); Distances(:,:) = sqrt(DistSqr); CosTh(:,:) = abs(DeltaFidPoint*DeltaOld)./DistOld./Distances; Index = []; search_theta = max_theta; while isempty(Index) && search_theta < pi % if Necessary, the direction limit is repeatedly doubled to maximally pi radians dist_mult = 1; while isempty(Index) && dist_mult <= max_dist_mult % Whenever points cannot be found, the distance limit is increased stepwise to maximally five times the original limit. search_dist = dist_mult*max_dist; Candidates = find(Distances <= search_dist & CosTh >= cos(search_theta) ... & Distances>= min_dist & abs(One2N_opt-is)>period); if ~isempty(Candidates) % find the best of candidates if numel(Candidates) > 1 Candidates = Candidates(CosTh(Candidates)==max(CosTh(Candidates))); if numel(Candidates) > 1 Candidates = Candidates(Distances(Candidates)==min(Distances(Candidates))); if numel(Candidates) > 1 Candidates = Candidates(abs(One2N_opt(Candidates)-is)==max(abs(One2N_opt(Candidates)-is))); end end end Index = Candidates(1); end dist_mult = dist_mult+1; end search_theta = search_theta*2; end if isempty(Index) && in > N_opt % we can not evolve the old point and no new one was found Index = find(DistSqr==min(DistSqr(DistSqr>=min_dist^2 & abs(One2N_opt-is)>period))& abs(One2N_opt-is)>period,1,'first'); % The nearest neighbours are found by selecting data point closest to X1, from separate cycles with a temporal separation larger than twice the first minimum in the mutual information function. if isempty(Index) warning('Cannot calculate Wolf''s Lyapunov exponent, no states further than min_dist from fiducial point'); Lambda = nan; return; end end if ~isempty(Index) LnDistReplacementsSum = LnDistReplacementsSum + log(DistOld/Distances(Index)); in = Index; end end DistEnd = sqrt(sum((states(is,:)-states(in,:)).^2)); % The dt between neighbouring trajectories in state space were computed as a function of time, and averaged over many original pairs of initially nearest neighbours. % The maximum Lyapunov exponent is determined by averaging the rate of exponential divergence between the reference % and nearby point as the trajectory evolves between replacement steps. Lambda = (log(DistEnd/DistStart) + LnDistReplacementsSum)/((is-1)/fs); \ No newline at end of file diff --git a/funcSampleEntropy.m b/funcSampleEntropy.m index 36619d5..dd17e52 100644 --- a/funcSampleEntropy.m +++ b/funcSampleEntropy.m @@ -49,7 +49,7 @@ function [SE] = funcSampleEntropy(DataIn, m, r) if size(DataIn,1) ~= 1 && size(DataIn,2) ~= 1 error('DataIn must be a vector'); end -DataIn = DataIn(:)/std(DataIn(:)); +DataIn = DataIn(:)/std(DataIn(:)); % The data is first normalized to unit variance, rendering the outcome scale-independent. N = size(DataIn,1); if N-m <= 0 error('m must be smaller than the length of the time series DataIn'); @@ -58,22 +58,30 @@ end %% Create the vectors Xm to be compared Xm = zeros(N-m,m); for i = 1:m, - Xm(:,i) = DataIn(i:end-1-m+i,1); + Xm(:,i) = DataIn(i:end-1-m+i,1); % (1) length = DataIn - m, so in this case [X-5,m] double end %% Count the numbers of matches for Xm and Xmplusone +% 1) Divide up your data into vectors of length m +% 2) Count your like matches (Vectors are considered a match if all numbers in the vector fall within +- r (0.3) +% 3) Calculate your conditional probability +% 4) Divide up your data into vectors of length m+1 +% 5) Count your like matches +% 6) Calculate your conditional probability +% 7) Entropy is a ratio of conditional probabilities + CountXm = 0; CountXmplusone = 0; XmDist = nan(size(Xm)); for i = 1:N-m, - for j=1:m, + for j=1:m, XmDist(:,j)=abs(Xm(:,j)-Xm(i,j)); end - IdXmi = find(max(XmDist,[],2)<=r); - CountXm = CountXm + length(IdXmi) - 1; - CountXmplusone = CountXmplusone + sum(abs(DataIn(IdXmi+m)-DataIn(i+m))<=r) - 1; + IdXmi = find(max(XmDist,[],2)<=r); % (2) + CountXm = CountXm + length(IdXmi) - 1; % (3) + CountXmplusone = CountXmplusone + sum(abs(DataIn(IdXmi+m)-DataIn(i+m))<=r) - 1; % (6) end %% Return sample entropy -SE = -log(CountXmplusone/CountXm); +SE = -log(CountXmplusone/CountXm); % (7) A srictly periodic time-series is completely predictable and will have a SEn of zero. SEn is defined as the negative natural logarithm of an estimate of the condiotional probability of epochs of length m (m=5) that match point-wise within a tolerance r and repeates itself for m+1 points. diff --git a/matlab.sty b/matlab.sty deleted file mode 100644 index f3b18dd..0000000 --- a/matlab.sty +++ /dev/null @@ -1,104 +0,0 @@ -\NeedsTeXFormat{LaTeX2e} -\ProvidesPackage{matlab} - -\RequirePackage{verbatim} -\RequirePackage{fancyvrb} -\RequirePackage{alltt} -\RequirePackage{upquote} -\RequirePackage[framemethod=tikz]{mdframed} -\RequirePackage{hyperref} -\RequirePackage{color} - - -\newcommand{\maxwidth}[1]{\ifdim\linewidth>#1 #1\else\linewidth\fi} -\newcommand{\mlcell}[1]{{\color{output}\verbatim@font#1}} - -\definecolor{output}{gray}{0.4} - -% Unicode character conversions -\DeclareUnicodeCharacter{B0}{\ensuremath{^\circ}} -\DeclareUnicodeCharacter{21B5}{\ensuremath{\hookleftarrow}} - -% Paragraph indentation -\setlength{\parindent}{0pt} - -% Hyperlink style -\hypersetup{ -colorlinks=true, -linkcolor=blue, -urlcolor=blue -} - - -% environment styles for MATLAB code and output -\mdfdefinestyle{matlabcode}{% - outerlinewidth=.5pt, - linecolor=gray!20!white, - roundcorner=2pt, - innertopmargin=.5\baselineskip, - innerbottommargin=.5\baselineskip, - innerleftmargin=1em, - backgroundcolor=gray!10!white -} - -\newenvironment{matlabcode}{\verbatim}{\endverbatim} -\surroundwithmdframed[style=matlabcode]{matlabcode} - -\newenvironment{matlaboutput}{% - \Verbatim[xleftmargin=1.25em, formatcom=\color{output}]% -}{\endVerbatim} - -\newenvironment{matlabsymbolicoutput}{% - \list{}{\leftmargin=1.25em\relax}% - \item\relax% - \color{output}\verbatim@font% -}{\endlist} - -\newenvironment{matlabtableoutput}[1]{% - {\color{output}% - \hspace*{1.25em}#1}% -}{} - - -% Table of Contents style -\newcounter{multititle} -\newcommand{\matlabmultipletitles}{\setcounter{multititle}{1}} - -\newcounter{hastoc} -\newcommand{\matlabhastoc}{\setcounter{hastoc}{1}} - -\newcommand{\matlabtitle}[1]{ -\ifnum\value{multititle}>0 - \ifnum\value{hastoc}>0 - \addcontentsline{toc}{section}{#1} - \fi -\fi -\section*{#1} -} - -\newcommand{\matlabheading}[1]{ -\ifnum\value{hastoc}>0 - \addcontentsline{toc}{subsection}{#1} -\fi -\subsection*{#1} -} - -\newcommand{\matlabheadingtwo}[1]{ -\ifnum\value{hastoc}>0 - \addcontentsline{toc}{subsubsection}{#1} -\fi -\subsubsection*{#1} -} - -\newcommand{\matlabheadingthree}[1]{ -\ifnum\value{hastoc}>0 - \addcontentsline{toc}{paragraph}{#1} -\fi -\paragraph*{#1} -} - -\newcommand{\matlabtableofcontents}[1]{ -\renewcommand{\contentsname}{#1} -\tableofcontents -} - \ No newline at end of file diff --git a/msentropy.m b/msentropy.m new file mode 100644 index 0000000..a577ad1 --- /dev/null +++ b/msentropy.m @@ -0,0 +1,28 @@ +% Function for calculating multiscale entropy +% input: signal +% m: match point(s) +% r: matching tolerance +% factor: number of scale factor +% sampenc is available at http://people.ece.cornell.edu/land/PROJECTS/Complexity/sampenc.m +% +% Multi-scale sample Entropy (Mscale-En) is an indicator of gait predictability. Multi-scale entropy takes the +% complexity of a system into account by calculating the +% predictability of a signal over time scales with increasing +% length. A ‘coarse-graining’ process is applied to the acceleration signals; non-overlapping windows of data +% points with an increasing length τ are constructed, with +% τ representing the time scale with a tolerance of r (in the +% present study τ = 7 and r = 0.2). A complete predictable +% signal will adopt a Mscale-En value of 0 [29]. + +function e = msentropy(input,m,r,factor) + +y=input; +y=y-mean(y); +y=y/std(y); + +for i=1:factor + s=coarsegraining(y,i); % dont have this function yet. + sampe=sampenc(s,m+1,r); + e(i)=sampe(m+1); +end +e=e'; \ No newline at end of file diff --git a/resources/project/uuid-397832ba-e50d-439c-bd33-8c866c35eb59.xml b/resources/project/uuid-397832ba-e50d-439c-bd33-8c866c35eb59.xml new file mode 100644 index 0000000..1c0844e --- /dev/null +++ b/resources/project/uuid-397832ba-e50d-439c-bd33-8c866c35eb59.xml @@ -0,0 +1,2 @@ + + \ No newline at end of file diff --git a/spectrum2.m b/spectrum2.m new file mode 100644 index 0000000..297e976 --- /dev/null +++ b/spectrum2.m @@ -0,0 +1,427 @@ +function [Spec,f,SpecConf] = spectrum2(varargin) +%SPECTRUM Power spectrum estimate of one or two data sequences. +% P=SPECTRUM2(X,NFFT,NOVERLAP,WIND) estimates the Power Spectral Density of +% signal vector(s) X using Welch's averaged periodogram method. X must be a +% set of column vectors. They are divided into overlapping sections, each of +% which is detrended and windowed by the WINDOW parameter, then zero padded +% to length NFFT. The magnitude squared of the length NFFT DFTs of the sec- +% tions are averaged to form Pxx. P is a two times SIZE(X,2) column matrix +% P = [Pxx Pxxc]; the second half of columns Pxxc are the 95% confidence +% intervals. The number of rows of P is NFFT/2+1 for NFFT even, (NFFT+1)/2 +% for NFFT odd, or NFFT if the signal X is complex. If you specify a scalar +% for WINDOW, a Hanning window of that length is used. +% +% [P,F] = SPECTRUM2(X,NFFT,NOVERLAP,WINDOW,Fs) given a sampling frequency +% Fs returns a vector of frequencies the same length as Pxx at which the +% PSD is estimated. PLOT(F,P(:,1:end/2)) plots the power spectrum estimate +% versus true frequency. +% +% [P, F] = SPECTRUM2(X,NFFT,NOVERLAP,WINDOW,Fs,Pr) where Pr is a scalar +% between 0 and 1, overrides the default 95% confidence interval and +% returns the Pr*100% confidence interval for Pxx instead. +% +% SPECTRUM2(X) with no output arguments plots the PSD in the current +% figure window, with confidence intervals. +% +% The default values for the parameters are NFFT = 256 (or SIZE(X,1), +% whichever is smaller), NOVERLAP = 0, WINDOW = HANNING(NFFT), Fs = 2, +% and Pr = .95. You can obtain a default parameter by leaving it out +% or inserting an empty matrix [], e.g. SPECTRUM(X,[],128). +% +% P = SPECTRUM2(X,Y) performs spectral analysis of the two times SIZE(X,2) +% sequences X and Y using the Welch method. SPECTRUM returns the 8 times +% SIZE(X,2)column array +% P = [Pxx Pyy Pxy Txy Cxy Pxxc Pyyc Pxyc] +% where +% Pxx = X-vector power spectral density +% Pyy = Y-vector power spectral density +% Pxy = Cross spectral density +% Txy = Complex transfer function from X to Y = Pxy./Pxx +% Cxy = Coherence function between X and Y = (abs(Pxy).^2)./(Pxx.*Pyy) +% Pxxc,Pyyc,Pxyc = Confidence range. +% All input and output options are otherwise exactly the same as for the +% single input case. +% +% SPECTRUM2(X,Y) with no output arguments will plot Pxx, Pyy, abs(Txy), +% angle(Txy) and Cxy in sequence, pausing between plots. +% +% SPECTRUM2(X,...,DFLAG), where DFLAG can be 'linear', 'mean' or 'none', +% specifies a detrending mode for the prewindowed sections of X (and Y). +% DFLAG can take the place of any parameter in the parameter list +% (besides X) as long as it is last, e.g. SPECTRUM(X,'none'); +% +% See also SPECTRUM, PSD, CSD, TFE, COHERE, SPECGRAM, SPECPLOT, DETREND, +% PMTM, PMUSIC. +% ETFE, SPA, and ARX in the Identification Toolbox. + +% The units on the power spectra Pxx and Pyy are such that, using +% Parseval's theorem: +% +% SUM(Pxx)/LENGTH(Pxx) = SUM(X.^2)/size(x,1) = COV(X) +% +% The RMS value of the signal is the square root of this. +% If the input signal is in Volts as a function of time, then +% the units on Pxx are Volts^2*seconds = Volt^2/Hz. +% +% Here are the covariance, RMS, and spectral amplitude values of +% some common functions: +% Function Cov=SUM(Pxx)/LENGTH(Pxx) RMS Pxx +% a*sin(w*t) a^2/2 a/sqrt(2) a^2*LENGTH(Pxx)/4 +%Normal: a*rand(t) a^2 a a^2 +%Uniform: a*rand(t) a^2/12 a/sqrt(12) a^2/12 +% +% For example, a pure sine wave with amplitude A has an RMS value +% of A/sqrt(2), so A = SQRT(2*SUM(Pxx)/LENGTH(Pxx)). +% +% See Page 556, A.V. Oppenheim and R.W. Schafer, Digital Signal +% Processing, Prentice-Hall, 1975. + +error(nargchk(1,8,nargin)) +[msg,x,y,nfft,noverlap,window,Fs,p,dflag]=specchk2(varargin); +error(msg) + + +if isempty(p), + p = .95; % default confidence interval even if not asked for +end + +n = size(x,1); % Number of data points +ns = size(x,2); % Number of signals +nwind = length(window); +if n < nwind % zero-pad x (and y) if length less than the window length + x(nwind,:)=0; n=nwind; + if ~isempty(y), y(nwind)=0; end +end + +k = fix((n-noverlap)/(nwind-noverlap)); % Number of windows +index = 1:nwind; +KMU = k*norm(window)^2; % Normalizing scale factor ==> asymptotically unbiased +% KMU = k*sum(window)^2;% alt. Nrmlzng scale factor ==> peaks are about right + +if (isempty(y)) % Single sequence case. + Pxx = zeros(nfft,ns); Pxx2 = zeros(nfft,ns); + for i=1:k + for l=1:ns + if strcmp(dflag,'linear') + xw = window.*detrend(x(index,l)); + elseif strcmp(dflag,'none') + xw = window.*(x(index,l)); + else + xw = window.*detrend(x(index,l),0); + end + Xx = abs(fft(xw,nfft)).^2; + Pxx(:,l) = Pxx(:,l) + Xx; + Pxx2(:,l) = Pxx2(:,l) + abs(Xx).^2; + end + index = index + (nwind - noverlap); + end + % Select first half + if ~any(any(imag(x)~=0)), % if x and y are not complex + if rem(nfft,2), % nfft odd + select = [1:(nfft+1)/2]; + else + select = [1:nfft/2+1]; % include DC AND Nyquist + end + else + select = 1:nfft; + end + Pxx = Pxx(select,:); + Pxx2 = Pxx2(select,:); + cPxx = zeros(size(Pxx)); + if k > 1 + c = (k.*Pxx2-abs(Pxx).^2)./(k-1); + c = max(c,zeros(size(Pxx))); + cPxx = sqrt(c); + end + ff = sqrt(2)*erfinv(p); % Equal-tails. + Pxxc = ff.*cPxx/KMU; + P = Pxx/KMU; + Pc = Pxxc; +else + Pxx = zeros(nfft,ns); % Dual sequence case. + Pxy = Pxx; Pxx2 = Pxx; Pxy2 = Pxx; + Pyy = zeros(nfft,1); Pyy2 = Pyy; + for i=1:k + if strcmp(dflag,'linear') + yw = window.*detrend(y(index)); + elseif strcmp(dflag,'none') + yw = window.*(y(index)); + else + yw = window.*detrend(y(index),0); + end + Yy = fft(yw,nfft); + Yy2 = abs(Yy).^2; + Pyy = Pyy + Yy2; + Pyy2 = Pyy2 + abs(Yy2).^2; + for l=1:ns + if strcmp(dflag,'linear') + xw = window.*detrend(x(index,l)); + elseif strcmp(dflag,'none') + xw = window.*(x(index,l)); + else + xw = window.*detrend(x(index,l),0); + end + Xx = fft(xw,nfft); + Xx2 = abs(Xx).^2; + Pxx(:,l) = Pxx(:,l) + Xx2; + Pxx2(:,l) = Pxx2(:,l) + abs(Xx2).^2; + Xy = Yy .* conj(Xx); + Pxy(:,l) = Pxy(:,l) + Xy; + Pxy2(:,l) = Pxy2(:,l) + Xy .* conj(Xy); + end + index = index + (nwind - noverlap); + end + % Select first half + if ~any(any(imag([x y])~=0)), % if x and y are not complex + if rem(nfft,2), % nfft odd + select = [1:(nfft+1)/2]; + else + select = [1:nfft/2+1]; % include DC AND Nyquist + end + else + select = 1:nfft; + end + Pxx = Pxx(select,:); + Pxy = Pxy(select,:); + Pxx2 = Pxx2(select,:); + Pxy2 = Pxy2(select,:); + Pyy = Pyy(select); + Pyy2 = Pyy2(select); + cPxx = zeros(size(Pxx)); + cPyy = zeros(size(Pyy)); + cPxy = cPxx; + if k > 1 + c = max((k.*Pxx2-abs(Pxx).^2)./(k-1),zeros(size(Pxx))); + cPxx = sqrt(c); + c = max((k.*Pyy2-abs(Pyy).^2)./(k-1),zeros(size(Pyy))); + cPyy = sqrt(c); + c = max((k.*Pxy2-abs(Pxy).^2)./(k-1),zeros(size(Pxx))); + cPxy = sqrt(c); + end + Txy = Pxy./Pxx; + Cxy = (abs(Pxy).^2)./(Pxx.*repmat(Pyy,1,size(Pxx,2))); + ff = sqrt(2)*erfinv(p); % Equal-tails. + Pxx = Pxx/KMU; + Pyy = Pyy/KMU; + Pxy = Pxy/KMU; + Pxxc = ff.*cPxx/KMU; + Pxyc = ff.*cPxy/KMU; + Pyyc = ff.*cPyy/KMU; + P = [Pxx Pyy Pxy Txy Cxy]; + Pc = [Pxxc Pyyc Pxyc]; +end +freq_vector = (select - 1)'*Fs/nfft; + +if nargout == 0, % do plots + newplot; + if Fs==2, xl='Frequency'; else, xl = 'f [Hz]'; end + nplot=1+(1-isempty(y))*4; + + subplot(nplot,1,1); + c = [max(Pxx-Pxxc,0) Pxx+Pxxc]; + c = c.*(c>0); + + h=semilogy(freq_vector,Pxx,... + freq_vector,c(:,1:size(c,2)/2),'--',... + freq_vector,c(:,size(c,2)/2+1:end),'--'); + title('\bf X Power Spectral Density') + ylabel('P_x') + xlabel(xl) + if length(h)>3 + s={}; + for k=1:length(h)/3 + c=get(h(k),'Color'); + set(h(k+length(h)/3),'Color',c); + set(h(k+2*length(h)/3),'Color',c); + s{k}=['x_' num2str(k)]; + end + legend(h(1:length(h)/3),s); + end + if (isempty(y)), % single sequence case + return + end + + subplot(nplot,1,2); + c = [max(Pyy-Pyyc,0) Pyy+Pyyc]; + c = c.*(c>0); + h=semilogy(freq_vector,Pyy,... + freq_vector,c(:,1),'--',... + freq_vector,c(:,2),'--'); + if size(Pxx,2)>1 + for k=1:length(h)/3 + c=get(h(k),'Color'); + set(h(k+length(h)/3),'Color',c); + set(h(k+2*length(h)/3),'Color',c); + end + end + + title('\bf Y Power Spectral Density') + ylabel('P_y') + xlabel(xl) + + subplot(nplot,1,3); + semilogy(freq_vector,abs(Txy)); + title('\bf Transfer function magnitude') + ylabel('T_{xy}^{(m)}') + xlabel(xl) + + subplot(nplot,1,4); + plot(freq_vector,(angle(Txy))), ... + title('\bf Transfer function phase') + ylabel('T_{xy}^{(p)}') + xlabel(xl) + + subplot(nplot,1,5); + plot(freq_vector,Cxy); + title('\bf Coherence') + ylabel('C_{xy}') + xlabel(xl) + if exist ('niceaxes') ~= 0 + niceaxes(findall(gcf,'Type','axes')); + end +elseif nargout ==1, + Spec = P; +elseif nargout ==2, + Spec = P; + f = freq_vector; +elseif nargout ==3, + Spec = P; + f = freq_vector; + SpecConf = Pc; +end + +function [msg,x,y,nfft,noverlap,window,Fs,p,dflag] = specchk2(P) +%SPECCHK Helper function for SPECTRUM +% SPECCHK(P) takes the cell array P and uses each cell as +% an input argument. Assumes P has between 1 and 7 elements. + +msg = []; +if size(P{1},1)<=1 + if max(size(P{1}))==1 + msg = 'Input data must be a vector, not a scalar.'; + else + msg = 'Requires column vector input.'; + end + x=[]; + y=[]; +elseif (length(P)>1), + if (all(size(P{1},1)==size(P{2})) & (size(P{1},1)>1) ) | ... + size(P{2},1)>1, % 0ne signal or 2 present? + % two signals, x and y, present + x = P{1}; y = P{2}; + % shift parameters one left + P(1) = []; + else + % only one signal, x, present + x = P{1}; y = []; + end +else % length(P) == 1 + % only one signal, x, present + x = P{1}; y = []; +end + +% now x and y are defined; let's get the rest + +if length(P) == 1 + nfft = min(size(x,1),256); + window = hanning(nfft); + noverlap = 0; + Fs = 2; + p = []; + dflag = 'linear'; +elseif length(P) == 2 + if isempty(P{2}), dflag = 'linear'; nfft = min(size(x,1),256); + elseif isstr(P{2}), dflag = P{2}; nfft = min(size(x,1),256); + else dflag = 'linear'; nfft = P{2}; end + window = hanning(nfft); + noverlap = 0; + Fs = 2; + p = []; +elseif length(P) == 3 + if isempty(P{2}), nfft = min(size(x,1),256); else nfft=P{2}; end + if isempty(P{3}), dflag = 'linear'; noverlap = 0; + elseif isstr(P{3}), dflag = P{3}; noverlap = 0; + else dflag = 'linear'; noverlap = P{3}; end + window = hanning(nfft); + Fs = 2; + p = []; +elseif length(P) == 4 + if isempty(P{2}), nfft = min(size(x,1),256); else nfft=P{2}; end + if isstr(P{4}) + dflag = P{4}; + window = hanning(nfft); + else + dflag = 'linear'; + window = P{4}; window = window(:); % force window to be a column + if length(window) == 1, window = hanning(window); end + if isempty(window), window = hanning(nfft); end + end + if isempty(P{3}), noverlap = 0; else noverlap=P{3}; end + Fs = 2; + p = []; +elseif length(P) == 5 + if isempty(P{2}), nfft = min(size(x,1),256); else nfft=P{2}; end + window = P{4}; window = window(:); % force window to be a column + if length(window) == 1, window = hanning(window); end + if isempty(window), window = hanning(nfft); end + if isempty(P{3}), noverlap = 0; else noverlap=P{3}; end + if isstr(P{5}) + dflag = P{5}; + Fs = 2; + else + dflag = 'linear'; + if isempty(P{5}), Fs = 2; else Fs = P{5}; end + end + p = []; +elseif length(P) == 6 + if isempty(P{2}), nfft = min(size(x,1),256); else nfft=P{2}; end + window = P{4}; window = window(:); % force window to be a column + if length(window) == 1, window = hanning(window); end + if isempty(window), window = hanning(nfft); end + if isempty(P{3}), noverlap = 0; else noverlap=P{3}; end + if isempty(P{5}), Fs = 2; else Fs = P{5}; end + if isstr(P{6}) + dflag = P{6}; + p = []; + else + dflag = 'linear'; + if isempty(P{6}), p = .95; else p = P{6}; end + end +elseif length(P) == 7 + if isempty(P{2}), nfft = min(size(x,1),256); else nfft=P{2}; end + window = P{4}; window = window(:); % force window to be a column + if length(window) == 1, window = hanning(window); end + if isempty(window), window = hanning(nfft); end + if isempty(P{3}), noverlap = 0; else noverlap=P{3}; end + if isempty(P{5}), Fs = 2; else Fs = P{5}; end + if isempty(P{6}), p = .95; else p = P{6}; end + if isstr(P{7}) + dflag = P{7}; + else + msg = 'DFLAG parameter must be a string.'; return + end +end + +% NOW do error checking +if isempty(msg) + if (nfft= length(window)), + msg = 'Requires NOVERLAP to be strictly less than the window length.'; + end + if (nfft ~= abs(round(nfft)))|(noverlap ~= abs(round(noverlap))), + msg = 'Requires positive integer values for NFFT and NOVERLAP.'; + end + if ~isempty(p), + if (prod(size(p))>1)|(p(1,1)>1)|(p(1,1)<0), + msg = 'Requires confidence parameter to be a scalar between 0 and 1.'; + end + end + if (min(size(y))~=1)&(~isempty(y)), + msg = 'Requires column vector input as second signal.'; + end + if (size(x,1)~=length(y))&(~isempty(y)), + msg = 'Requires X and Y to have the same number of rows.'; + end +end \ No newline at end of file diff --git a/struct2csv - Snelkoppeling.lnk b/struct2csv - Snelkoppeling.lnk new file mode 100644 index 0000000..9227850 Binary files /dev/null and b/struct2csv - Snelkoppeling.lnk differ