gait_cycle.rar_gait cycle _gait period_human movement_matlab hum

clear close all clc input_dir = 'D:\PROJECT M.Tech\Databases\001\001\bg-01\090'; filenames = dir(fullfile(input_dir, '*.png')); tot_ims1 = numel(filenames); for n = 1:tot_ims1 filename = fullfile(input_dir, filenames(n).name); img1_p1 = imread(filename); img1_p1 = bwareaopen(img1_p1,50); se = strel('square',5); s_im= imdilate(img1_p1, se); s_im= imclose(s_im, se); % Label the 2 levels thresholded image [LabeledImage, Num]=bwlabel(s_im); % Calculate the area of different components in LabeledImage2 STATS = regionprops(s_im, 'Area'); % Get the label of the biggest area in LabeledImage. This will be the human [MaxArea,MaxAreaLabel]=max([STATS(:).Area]); % Binary detected breast region BinaryRegion=LabeledImage==MaxAreaLabel; labeledImage = bwconncomp(BinaryRegion,8); measurements = regionprops(labeledImage,'BoundingBox','Centroid'); bb = measurements.BoundingBox; bco = measurements.Centroid; AR(n)= bb(4)/bb(3); end fgs=5; subplot( fgs,1, 1) plot(AR, '-o') title('aspect ratio') %Normalization of aspect ration followed by mean filtering subplot( fgs,1, 2) mean_sub= AR- mean(AR); std_div= mean_sub/std(mean_sub); smooth_wave= smooth(std_div, 17, 'moving'); plot( smooth_wave ) title('smoothed') % * autocorrelation signals subplot(fgs,1, 3) autoc= xcorr(smooth_wave); plot( autoc ) title('autoc') % * first-order derivative signals of autocorrelations % * Peak positions indicating the periods. subplot(fgs,1, 4) df= diff(autoc); plot( autoc ) [PKS, LOCS]= findpeaks(df); hold on plot(LOCS, PKS, 'ro') title('deriv') Distance_Peaks = (max(diff(LOCS))+ mean(diff(LOCS)))/2; gait_period= round(Distance_Peaks)