function [ResultStruct] = GaitOutcomesTrunkAccFuncIH(inputData,FS,LegLength,WindowLen,ApplyRealignment,ApplyRemoveSteps)

% DESCRIPTON: 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 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),:);
    
    % 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