function [locsTurns,FilteredData] = DetermineLocationTurnsFunc(inputData,FS,ApplyRealignment,plotit,Distance)
% Description: Determine the location of turns, plot for visual inspection

% Input: Acc Data (not yet realigned)

%Realign sensor data to VT-ML-AP frame

if ApplyRealignment % apply relignment as described in Rispens S, Pijnappels M, van Schooten K, Beek PJ, Daffertshofer A, van Die?n JH (2014).
    data = inputData(:,[3,2,4]); % reorder data to 1 = V; 2 = ML; 3 = AP
    % Consistency of gait characteristics as determined from acceleration data collected at different trunk locations. Gait Posture 2014;40(1):187-92.
    [RealignedAcc, ~] = RealignSensorSignalHRAmp(data, FS);
    dataAcc = RealignedAcc;
end
% Filter data
[B,A] = butter(2,3/(FS/2),'low'); % Filters data very strongly which is needed to determine turns correctly
dataStepDetection = filtfilt(B,A,dataAcc);
% Determine steps
% Explanation of method: https://nl.mathworks.com/help/supportpkg/beagleboneblue/ref/counting-steps-using-beagleboneblue-hardware-example.html
% From website: To convert the XYZ acceleration vectors at each point in time into scalar values,
% calculate the magnitude of each vector. This way, you can detect large changes in overall acceleration,
% such as steps taken while walking, regardless of device orientation.
magfilt = sqrt(sum((dataStepDetection(:,1).^2) + (dataStepDetection(:,2).^2) + (dataStepDetection(:,3).^2), 2));
magNoGfilt = magfilt - mean(magfilt);
minPeakHeight2 = 1.2*std(magNoGfilt);                                   % based on visual inspection, parameter tuning was performed on standard deviation from MInPeak (used to be 1 SD)
[pks, locs] = findpeaks(magNoGfilt, 'MINPEAKHEIGHT', minPeakHeight2);   % for step detection
numStepsOption2_filt = numel(pks);                                      % counts number of steps;

diffLocs = diff(locs); % calculates difference in step location
avg_diffLocs = mean(diffLocs); % average distance between steps
std_diffLocs = std(diffLocs); % standard deviation of distance between steps

figure;
findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs, 'MINPEAKDISTANCE',9);                                    % these values have been chosen based on visual inspection of the signal
line([1 length(diffLocs)],[avg_diffLocs avg_diffLocs])
[pks_diffLocs, locs_diffLocs] = findpeaks(diffLocs, 'MINPEAKHEIGHT', avg_diffLocs,'MINPEAKDISTANCE',10);    % values were initially 5
locsTurns =  [locs(locs_diffLocs),  locs(locs_diffLocs+1)];

magNoGfilt_copy = magNoGfilt;
for k = 1: size(locsTurns,1);
    magNoGfilt_copy(locsTurns(k,1):locsTurns(k,2)) = NaN;
end

% Visualising signal;
if plotit
    figure;
    subplot(2,1,1)
    hold on;
    plot(magNoGfilt,'b')
    plot(magNoGfilt_copy, 'r');
    title('Inside Straight: Filtered data with turns highlighted in blue')
    line([6000,12000,18000,24000,30000,36000;6000,12000,18000,24000,30000,36000],[-4,-4,-4,-4,-4,-4;4,4,4,4,4,4],'LineWidth',2,'Linestyle','--','color','k')
%     hold on;
%     for m = 1:size(locsTurns,1)
%         plot(locsTurns(m),DataStraight.([char(Participants(i))]).LyapunovPerStrideRosen_ML(:,m),'--gs',...
%         'LineWidth',2,...
%         'MarkerSize',10,...
%         'MarkerEdgeColor','g',...
%         'MarkerFaceColor',[0.5,0.5,0.5])
%     end
    hold off;
end
% Check if number of turns * 20 m are making sense based on total
% distance measured by researcher.
disp(['Number of turns detected = ' num2str(size(locsTurns,1))])
disp(['Total distance measured by researcher was = ' num2str(Distance)])

FilteredData = dataAcc;
end