104 lines
4.5 KiB
Mathematica
104 lines
4.5 KiB
Mathematica
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%% Gait Variability Analysis CLBP
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% Gait Variability Analysis
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% Script created for MAP 2020-2021
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% adapted from Claudine Lamoth and Iris Hagoort
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% version1 October 2020
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% Input: needs mat file which contains all raw accelerometer data
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% Input: needs excel file containing the participant information including
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% leg length.
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%% Clear and close;
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clear;
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close all;
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%% Load data;
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% Select 1 trial.
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% For loop to import all data will be used at a later stage
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[FNaam,FilePad] = uigetfile('*.xls','Load phyphox data...');
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filename =[FilePad FNaam];
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PhyphoxData = xlsread(filename)
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%load('Phyphoxdata.mat'); % loads accelerometer data, is stored in struct with name AccData
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%load('ExcelInfo.mat');
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%Participants = fields(AccData);
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%% Settings;
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%adapted from GaitOutcomesTrunkAccFuncIH
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LegLength = 98; % LegLength info not available!
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FS = 100; % Sample frequency
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Gr = 9.81; % Gravity acceleration, multiplication factor for accelerations
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StrideFreqEstimate = 1.00; % Used to set search for stride frequency from 0.5*StrideFreqEstimate until 2*StrideFreqEstimate
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StrideTimeRange = [0.2 4.0]; % Range to search for stride time (seconds)
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IgnoreMinMaxStrides = 0.10; % Number or percentage of highest&lowest values ignored for improved variability estimation
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N_Harm = 12; % Number of harmonics used for harmonic ratio, index of harmonicity and phase fluctuation
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LowFrequentPowerThresholds = ...
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[0.7 1.4]; % Threshold frequencies for estimation of low-frequent power percentages
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Lyap_m = 7; % Embedding dimension (used in Lyapunov estimations)
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Lyap_FitWinLen = round(60/100*FS); % Fitting window length (used in Lyapunov estimations Rosenstein's method)
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Sen_m = 5; % Dimension, the length of the subseries to be matched (used in sample entropy estimation)
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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)
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NStartEnd = [100];
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M = 5; % Maximum template length
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ResultStruct = struct(); % Empty struct
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inputData = (PhyphoxData(:,[1 2 3 4])); % matrix with accelerometer data
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ApplyRealignment = true;
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ApplyRemoveSteps = true;
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WindowLen = FS*10;
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%% Filter and Realign Accdata
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% dataAcc depends on ApplyRealignment = true/false
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% dataAcc_filt (low pass Butterworth Filter + zerophase filtering
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[dataAcc, dataAcc_filt] = FilterandRealignFunc(inputData,FS,ApplyRealignment);
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%% Step dectection
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% Determines the number of steps in the signal so that the first 30 and last 30 steps in the signal can be removed
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% StrideTimeSamples is needed for calculation stride parameters!
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[dataAccCut,dataAccCut_filt,StrideTimeSamples] = StepDetectionFunc(FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange);
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%% Calculate Stride Parameters
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% Outcomeparameters: StrideRegularity,RelativeStrideVariability,StrideTimeSamples,StrideTime
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[ResultStruct] = CalculateStrideParametersFunc(dataAccCut_filt,FS,ApplyRemoveSteps,dataAcc,dataAcc_filt,StrideTimeRange);
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%% Calculate spatiotemporal stride parameters
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% Measures from height variation by double integration of VT accelerations and high-pass filtering
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% StepLengthMean; Distance; WalkingSpeedMean; StrideTimeVariability; StrideSpeedVariability;
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% StrideLengthVariability; StrideTimeVariabilityOmitOutlier; StrideSpeedVariabilityOmitOutlier; StrideLengthVariabilityOmitOutlier;
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[ResultStruct] = SpatioTemporalGaitParameters(dataAccCut_filt,StrideTimeSamples,ApplyRealignment,LegLength,FS,IgnoreMinMaxStrides,ResultStruct);
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%% Measures derived from spectral analysis
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% IndexHarmonicity_V/ML/AP/ALL ; HarmonicRatio_V/ML/AP ; HarmonicRatioP_V/ML/AP ; FrequencyVariability_V/ML/AP; Stride Frequency
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AccVectorLen = sqrt(sum(dataAccCut_filt(:,1:3).^2,2));
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[ResultStruct] = SpectralAnalysisGaitfunc(dataAccCut_filt,WindowLen,FS,N_Harm,LowFrequentPowerThresholds,AccVectorLen,ResultStruct);
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%% calculate non-linear parameters
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% Outcomeparameters: Sample Entropy, Lyapunov exponents
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[ResultStruct] = CalculateNonLinearParametersFunc(ResultStruct,dataAccCut,WindowLen,FS,Lyap_m,Lyap_FitWinLen,Sen_m,Sen_r);
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% Save struct as .mat file
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% save('GaitVarOutcomesParticipantX.mat', 'OutcomesAcc');
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%% AggregateFunction (seperate analysis per minute);
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% see AggregateEpisodeValues.m
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%
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%
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