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step2_JSE_feature_exctraction.m
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step2_JSE_feature_exctraction.m
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close all;
clear;
clc;
addpath 'function';
dataset_type = 'A'; % <-- Control parameter
if dataset_type == 'A'; dataset_name = '\Dataset A'; kinect_ver = 1;
elseif dataset_type == 'B'; dataset_name = '\Dataset B'; kinect_ver = 2;
elseif dataset_type == 'C'; dataset_name = '\Dataset C'; kinect_ver = 1;
elseif dataset_type == 'D'; dataset_name = '\Dataset D'; kinect_ver = 2;
end
[num_joints, limb_info] = Skeleton_Info(1);
dataset_path_in = strcat(pwd,dataset_name,'\kinect gait csv dataset\');
dataset_path_out = strcat(pwd,dataset_name,'\feature\');
if ~exist(dataset_path_out, 'dir')
mkdir(dataset_path_out);
end
if dataset_type == 'A'
gender_info_path_in = strcat(pwd, '\Dataset A\person-data.csv');
gender = readtable(gender_info_path_in, 'HeaderLines', 1);
person_lists = dir(dataset_path_in);
p_max = length(person_lists);
dataset = {};
idx = 1;
for p = 3:1:p_max
person_path_in = strcat(dataset_path_in, person_lists(p).name);
if contains(gender{p-2, 5}, 'M')
label = 0; % Male
elseif contains(gender{p-2, 5}, 'F')
label = 1; % Female
end
walk_list = dir(person_path_in);
w_max = length(walk_list);
for w = 3:1:w_max
file_path_in = strcat(person_path_in,'\',walk_list(w).name);
motion = importdata(file_path_in);
D_T = zeros(size(motion,1),17); % Transverse plane
D_F = zeros(size(motion,1),17); % Frontal plane
D_M = zeros(size(motion,1),17); % Median plane
for frm = 1:1:size(motion,1)
pose = reshape(motion(frm,:),3,num_joints)'; % current pose
pose = [pose(:,3) pose(:,1) pose(:,2)];
% Target Joint of this feature
% 1 Head
% 2 Shoulder-Center
% 3 Shoulder-Right
% 4 Shoulder-Left
% 5 Elbow-Right
% 6 Elbow-Left
% 7 Wrist-Right
% 8 Wrist-Left
% 9 Hand-Right
%10 Hand-Left
%11 Spine
%12 Hip-centro <--- Not target
%13 Hip-Right <--- Not target
%14 Hip-Left <--- Not target
%15 Knee-Right
%16 Knee-Left
%17 Ankle-Right
%18 Ankle-Left
%19 Foot-Right
%20 Foot-Left
J = [1:1:11, 15:1:20];
cnt = 1;
for j_idx = J
D_T(frm, cnt) = cal_dist_T(pose, j_idx);
D_F(frm, cnt) = cal_dist_F(pose, j_idx);
D_M(frm, cnt) = cal_dist_M(pose, j_idx);
cnt = cnt + 1;
end
end
% NaN check
for frm = size(motion,1):-1:1
if sum(isnan(D_T(frm,:))) ~= 0; D_T(frm,:) = []; end
if sum(isnan(D_F(frm,:))) ~= 0; D_F(frm,:) = []; end
if sum(isnan(D_M(frm,:))) ~= 0; D_M(frm,:) = []; end
end
R_T = mean(D_T);
R_F = mean(D_F);
R_M = mean(D_M);
JSE = [R_T R_F R_M];
data = [JSE, label];
dataset(idx,:) = {p-2, data};
idx = idx + 1;
end
end
save(strcat(dataset_path_out,'JSE_feature.mat'), 'dataset');
elseif dataset_type == 'B'
gender_info_path_in = strcat(pwd,dataset_name,'\1_Participants information.csv');
raw = importdata(gender_info_path_in);
gender = raw.textdata(:,4);
person_lists = dir(dataset_path_in);
p_max = length(person_lists);
dataset = {};
idx = 1;
for p = 3:1:p_max
file_path_in = strcat(dataset_path_in, person_lists(p).name);
if contains(gender{p-1}, 'MASCULINO')
label = 0; % Male
elseif contains(gender{p-1}, 'FEMENIN')
label = 1; % Female
end
raw = importdata(file_path_in);
motion_v2 = raw.data(:,2:1:76); % kinect v2
re_arr = [3*21-2:3*21, ... % 01. Head
3*23-2:3*23, ... % 02. Spine Shoulder (Shoulder-Center)
3*1-2:3*1, ... % 03. R-Shoulder
3*11-2:3*11, ... % 04. L-Shoulder
3*2-2:3*2, ... % 05. R-Elbow
3*12-2:3*12, ... % 06. L-Elbow
3*3-2:3*3, ... % 07. R-Wrist
3*13-2:3*13, ... % 08. L-Wrist
3*4-2:3*4, ... % 09. R-Hand
3*14-2:3*14, ... % 10. L-Hand
3*24-2:3*24, ... % 11. Spine mid (Spine)
3*25-2:3*25, ... % 12. Spine Base (Hip-centro)
3*7-2:3*7, ... % 13. R-Hip
3*17-2:3*17, ... % 14. L-Hip
3*8-2:3*8, ... % 15. R-Knee
3*18-2:3*18, ... % 16. L-Knee
3*9-2:3*9, ... % 17. R-Ankle
3*19-2:3*19, ... % 18. L-Ankle
3*10-2:3*10, ... % 19. R-Foot
3*20-2:3*20]; % 20. L-Foot
motion = motion_v2(:,re_arr);
frame_len = size(motion,1);
D_T = zeros(frame_len,17); % Transverse plane
D_F = zeros(frame_len,17); % Frontal plane
D_M = zeros(frame_len,17); % Median plane
for frm = 1:1:frame_len
pose = reshape(motion(frm,:),3,num_joints)'; % current pose
pose = [pose(:,3) pose(:,1) pose(:,2)];
J = [1:1:11, 15:1:20];
cnt = 1;
for j_idx = J
D_T(frm, cnt) = cal_dist_T(pose, j_idx);
D_F(frm, cnt) = cal_dist_F(pose, j_idx);
D_M(frm, cnt) = cal_dist_M(pose, j_idx);
cnt = cnt + 1;
end
end
% NaN check
for frm = frame_len:-1:1
if sum(isnan(D_T(frm,:))) ~= 0; D_T(frm,:) = []; end
if sum(isnan(D_F(frm,:))) ~= 0; D_F(frm,:) = []; end
if sum(isnan(D_M(frm,:))) ~= 0; D_M(frm,:) = []; end
end
R_T = mean(D_T);
R_F = mean(D_F);
R_M = mean(D_M);
JSE = [R_T, R_F, R_M];
data = [JSE, label];
dataset(idx,:) = {p-2, data};
idx = idx + 1;
end
save(strcat(dataset_path_out,'JSE_feature.mat'), 'dataset');
elseif dataset_type == 'C'
load(strcat(pwd,dataset_name,'\MatlabFormat\UPCVgait.mat'));
dataset = {};
idx = 1;
for p = 1:1:30
if p <= 15
label = 0; % first 15 persons are male
else
label = 1; % last 15 persons are female
end
for w = 1:1:5
seg = upcv{p,w};
whole_len = size(seg,1)/20;
motion_tmp = zeros(whole_len,60);
for len = 1:1:whole_len
motion_tmp(len,:) = reshape(seg(20*len-19:20*len,:)', [1,60]);
end
re_arr = [3*1-2:3*1, ... % 01. Head
3*2-2:3*2, ... % 02. C-Shoulder
3*3-2:3*3, ... % 03. R-Shoulder
3*4-2:3*4, ... % 04. L-Shoulder
3*8-2:3*8, ... % 05. R-Elbow
3*5-2:3*5, ... % 06. L-Elbow
3*9-2:3*9, ... % 07. R-Wrist
3*6-2:3*6, ... % 08. L-Wrist
3*10-2:3*10, ... % 09. R-Hand
3*7-2:3*7, ... % 10. L-Hand
3*11-2:3*11, ... % 11. Spine
3*12-2:3*12, ... % 12. C-Hip
3*13-2:3*13, ... % 13. R-Hip
3*14-2:3*14, ... % 14. L-Hip
3*18-2:3*18, ... % 15. R-Knee
3*15-2:3*15, ... % 16. L-Knee
3*19-2:3*19, ... % 17. R-Ankle
3*16-2:3*16, ... % 18. L-Ankle
3*20-2:3*20, ... % 19. R-Foot
3*17-2:3*17]; % 20. L-Foot
motion = motion_tmp(:,re_arr);
D_T = zeros(size(motion,1),17); % Transverse plane
D_F = zeros(size(motion,1),17); % Frontal plane
D_M = zeros(size(motion,1),17); % Median plane
for frm = 1:1:size(motion,1)
pose = reshape(motion(frm,:),3,num_joints)'; % current pose
pose = [pose(:,3) pose(:,1) pose(:,2)];
J = [1:1:11, 15:1:20];
cnt = 1;
for j_idx = J
D_T(frm, cnt) = cal_dist_T(pose, j_idx);
D_F(frm, cnt) = cal_dist_F(pose, j_idx);
D_M(frm, cnt) = cal_dist_M(pose, j_idx);
cnt = cnt + 1;
end
end
% NaN check
for frm = size(motion,1):-1:1
if sum(isnan(D_T(frm,:))) ~= 0; D_T(frm,:) = []; end
if sum(isnan(D_F(frm,:))) ~= 0; D_F(frm,:) = []; end
if sum(isnan(D_M(frm,:))) ~= 0; D_M(frm,:) = []; end
end
R_T = mean(D_T);
R_F = mean(D_F);
R_M = mean(D_M);
JSE = [R_T, R_F, R_M];
dataset(idx,:) = {p,[JSE, label]};
idx = idx + 1;
end
end
save(strcat(dataset_path_out,'JSE_feature.mat'), 'dataset');
elseif dataset_type == 'D'
load(strcat(pwd,dataset_name,'\MatlabFormat\KinectK2gait.mat'));
upcv2 = data; clear data;
dataset = {};
idx = 1;
for p = 1:1:30
if gender(p,1) == 0
label = 0; % male
else
label = 1; % female
end
for w = 1:1:10
td = upcv2{p,w};
whole_len = size(td,1)/25;
motion_v2 = zeros(whole_len,75);
for len = 1:1:whole_len
motion_v2(len,:) = reshape(td(25*len-24:25*len,:)', [1,75]);
end
re_arr = [3*4-2:3*4, ... % 01. Head
3*21-2:3*21, ... % 02. Spine Shoulder (Shoulder-Center)
3*5-2:3*5, ... % 03. R-Shoulder
3*9-2:3*9, ... % 04. L-Shoulder
3*6-2:3*6, ... % 05. R-Elbow
3*10-2:3*10, ... % 06. L-Elbow
3*7-2:3*7, ... % 07. R-Wrist
3*11-2:3*11, ... % 08. L-Wrist
3*8-2:3*8, ... % 09. R-Hand
3*12-2:3*12, ... % 10. L-Hand
3*2-2:3*2, ... % 11. Spine mid (Spine)
3*1-2:3*1, ... % 12. Spine Base (Hip-centro)
3*13-2:3*13, ... % 13. R-Hip
3*17-2:3*17, ... % 14. L-Hip
3*14-2:3*14, ... % 15. R-Knee
3*18-2:3*18, ... % 16. L-Knee
3*15-2:3*15, ... % 17. R-Ankle
3*19-2:3*19, ... % 18. L-Ankle
3*16-2:3*16, ... % 19. R-Foot
3*20-2:3*20]; % 20. L-Foot
motion = motion_v2(:,re_arr);
D_T = zeros(size(motion,1),17); % Transverse plane
D_F = zeros(size(motion,1),17); % Frontal plane
D_M = zeros(size(motion,1),17); % Median plane
for frm = 1:1:size(motion,1)
pose = reshape(motion(frm,:),3,num_joints)'; % current pose
pose = [pose(:,3) pose(:,1) pose(:,2)];
J = [1:1:11, 15:1:20];
cnt = 1;
for j_idx = J
D_T(frm, cnt) = cal_dist_T(pose, j_idx);
D_F(frm, cnt) = cal_dist_F(pose, j_idx);
D_M(frm, cnt) = cal_dist_M(pose, j_idx);
cnt = cnt + 1;
end
end
% NaN check
for frm = size(motion,1):-1:1
if sum(isnan(D_T(frm,:))) ~= 0; D_T(frm,:) = []; end
if sum(isnan(D_F(frm,:))) ~= 0; D_F(frm,:) = []; end
if sum(isnan(D_M(frm,:))) ~= 0; D_M(frm,:) = []; end
end
R_T = mean(D_T);
R_F = mean(D_F);
R_M = mean(D_M);
JSE = [R_T, R_F, R_M];
dataset(idx,:) = {p,[JSE, label]};
idx = idx + 1;
end
end
save(strcat(dataset_path_out,'JSE_feature.mat'), 'dataset');
end
rmpath 'function';
function dist = cal_dist_T(pose, idx)
dist = abs(pose(12, 3) - pose(idx, 3));
end
function dist = cal_dist_F(pose, idx)
numerator = abs((pose(14,2)-pose(13,2))*(pose(idx,1)-pose(12,1)) +...
(pose(13,1)-pose(14,1))*(pose(idx,2)-pose(12,2)));
denominator = sqrt((pose(14,2)-pose(13,2))^2 + (pose(13,1)-pose(14,1))^2);
dist = numerator / denominator;
end
function dist = cal_dist_M(pose, idx)
numerator = abs((pose(14,1)-pose(13,1))*(pose(idx,1)-pose(12,1)) + ...
(pose(14,2)-pose(13,2))*(pose(idx,2)-pose(12,2)) + ...
(pose(14,3)-pose(13,3))*(pose(idx,3)-pose(12,3)));
denominator = sqrt((pose(14,1)-pose(13,1))^2 + ...
(pose(14,2)-pose(13,2))^2 + ...
(pose(14,3)-pose(13,3))^2);
dist = numerator / denominator;
end