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msm/ShoulderModel/TOOLBOX_Kinematics/KINEMATICS_TOOL_Estimate_Landmarks.m
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KINEMATICS_TOOL_Estimate_Landmarks.m
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function [SSDATA, BLDATA] = KINEMATICS_TOOL_Estimate_Landmarks(KRHandle, KRPlotHandles, SSDATAin, BLDATAin)
%{
Function to load/check a measured kinematics to the model.
--------------------------------------------------------------------------
Syntax :
--------------------------------------------------------------------------
File Description :
--------------------------------------------------------------------------
%}
% initialize the SSDATA
SSDATA = SSDATAin;
BLDATA = BLDATAin;
% ------------------------------------------------------------------------
% 1) Estimate the AC landmark
% ------------------------------------------------------------------------
% The anathomical AC landmark is normally covered by the anterior deltoid.
% Therefore, a good practice is to pulpate a point on clavicle right before
% where the anterior deltoid wraps over the clavicle. Nevertheless, we now
% need to estimate the real AC landmark. To this end, the length of SC-AC
% is required. Provided this length we can define the AC (real) by the
% intersection of a sphere centered at SC with SC-AC radius and a line
% passing from SC to Ac (measured). SC-AC is clavicle length.
% display message
disp(['Estimating AC']);
% Given value for the clavicle length
clavicle_length = norm(BLDATA.Initial_Points.SC(:,1) - BLDATA.Initial_Points.AC(:,1));
% run throughout the measured motion
for TimeId = 1:length(SSDATA.Measured_Kinematics.IJ)
% define the line unit direction vector
direction_vector = SSDATA.Measured_Kinematics.Ac(:,TimeId) - SSDATA.Measured_Kinematics.SC(:,TimeId);
direction_vector = direction_vector/norm(direction_vector);
% deine the line parameter at the intersections point
lambda = ((clavicle_length^2)/(norm(direction_vector)^2))^0.5;
% define the real AC landmark
SSDATA.Measured_Kinematics.AC(:,TimeId) = SSDATA.Measured_Kinematics.SC(:,TimeId) + lambda*direction_vector;
end
% ------------------------------------------------------------------------
% 2) Estimate the GH landmark
% ------------------------------------------------------------------------
% one single initial configuration for (AC, AA, GH) and (EM, EL, GH). In
% fact we just need one single Radiology or MRI to see where GH is in the
% thorax frame. If we do not have this then we can define this single
% configuration though predictive equations of body segments and landmarks.
% AC, AA, GH
initial_configuration1 = [SSDATA.Measured_Kinematics.AC(:,1), ...
SSDATA.Measured_Kinematics.AA(:,1), ...
BLDATA.Initial_Points.GH(:,1)]; % for now we take GH from our scaled model, which is
% correct as it's the same subject that we had MRI for
% EM, EL, GH
initial_configuration2 = [SSDATA.Measured_Kinematics.EM(:,1), ...%EM
SSDATA.Measured_Kinematics.EL(:,1), ...
BLDATA.Initial_Points.GH(:,1)];
% radii1 = [norm(initial_configuration1(:,1) - initial_configuration1(:,3)), ...
% norm(initial_configuration1(:,2) - initial_configuration1(:,3))];
% radii2 = [norm(initial_configuration2(:,1) - initial_configuration2(:,3)), ...
% norm(initial_configuration2(:,2) - initial_configuration2(:,3))];
% run throughout the measured motion
for TimeId = 1:length(SSDATA.Measured_Kinematics.IJ)
% display message
disp(['Estimating GH, Time Stamp : ', num2str(TimeId), '/', num2str(length(SSDATA.Measured_Kinematics.IJ))]);
%{
% Approach 2: ---------------------------------------------------------
% estimating the GH as the intersection of four spheres
center1 = SSDATA.Measured_Kinematics.AC(:,TimeId);
center2 = SSDATA.Measured_Kinematics.AA(:,TimeId);
center3 = SSDATA.Measured_Kinematics.EM(:,TimeId);%EM
center4 = SSDATA.Measured_Kinematics.EL(:,TimeId);
if TimeId == 1
alpha0in = [0.3 0.3]; % initial guess for the parametrization angles of the two intersecting conic sections.
% A conic section is a function of alpha.
else
alpha0in = alpha_GH;
end
if TimeId == 1
initial_guess = 0*ones(1, 4);
else
initial_guess = centers_parameters;
end
[estimated_GH, alpha_GH, H_PS2T1, H_PS2T2, centers_parameters] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
center3, center4, initial_configuration1, initial_configuration2, alpha0in, initial_guess);
% if TimeId == 1
%
% [estimated_GH, alpha_GH, H_PS2T1, H_PS2T2] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
% center3, center4, radii1, radii2, alpha0in);
% d1 = norm(estimated_GH(:, 1) - H_PS2T1(1:3, 4));
% d2 = norm(estimated_GH(:, 2) - H_PS2T2(1:3, 4));
%
% p = norm(estimated_GH(:, 1) - estimated_GH(:, 2));
%
% Zh = (estimated_GH(:,1) + estimated_GH(:,2))/2 - (center3+center4)/2; Zh = Zh/norm(Zh);
% Yh = cross((estimated_GH(:,1) + estimated_GH(:,2))/2 - center4, center3 - center4); Yh = Yh/norm(Yh);
% Xh = cross(Yh, Zh); Xh = Xh/norm(Xh);
% Rh = [Xh, Yh, Zh];
%
% end
%
%
% [estimated_GH, alpha_GH, H_PS2T1, H_PS2T2] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
% center3-Rh*[0 0 p]', center4-Rh*[0 0 p]', radii1, radii2, alpha0in);
%
% Zh = (estimated_GH(:,1) + estimated_GH(:,2))/2 - (center3+center4)/2; Zh = Zh/norm(Zh);
% Yh = cross((estimated_GH(:,1) + estimated_GH(:,2))/2 - center4, center3 - center4); Yh = Yh/norm(Yh);
% Xh = cross(Yh, Zh); Xh = Xh/norm(Xh);
% Rh = [Xh, Yh, Zh];
%
%
%
%
%
%
%
% p = norm(estimated_GH(:, 1) - estimated_GH(:, 2))
%
% r_cor_AC = sqrt((norm(estimated_GH(:, 1) - H_PS2T1(1:3, 4)) - p/(1 + d2/d1))^2 + ...
% norm(SSDATA.Measured_Kinematics.AC(:,TimeId) - H_PS2T1(1:3, 4))^2);
% r_cor_AA = sqrt((norm(estimated_GH(:, 1) - H_PS2T1(1:3, 4)) - p/(1 + d2/d1))^2 + ...
% norm(SSDATA.Measured_Kinematics.AA(:,TimeId) - H_PS2T1(1:3, 4))^2);
% r_cor_EM = sqrt((norm(estimated_GH(:, 2) - H_PS2T2(1:3, 4)) - p/(1 + d1/d2))^2 + ...
% norm(SSDATA.Measured_Kinematics.EM(:,TimeId) - H_PS2T2(1:3, 4))^2);
% r_cor_EL = sqrt((norm(estimated_GH(:, 2) - H_PS2T2(1:3, 4)) - p/(1 + d1/d2))^2 + ...
% norm(SSDATA.Measured_Kinematics.EL(:,TimeId) - H_PS2T2(1:3, 4))^2);
%
% [estimated_GH, alpha_GH, H_PS2T1, H_PS2T2] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
% center3-Rh*[0 0 p]', center4-Rh*[0 0 p]', radii1, radii2, alpha0in);
%
SSDATA.Measured_Kinematics.GH(:,TimeId) = (estimated_GH(:,1) + estimated_GH(:,2))/2;
% if TimeId > 1
% if norm(estimated_GH(:, 2) - H_PS2T1(1:3, 4)) > 0.99*pen_criteria
% SSDATA.Measured_Kinematics.GH(:,TimeId) = estimated_GH(:,2);
% else
% r_compensation = pen_criteria - norm(estimated_GH(:, 2) - H_PS2T1(1:3, 4));
%
% r1_new = sqrt((norm(estimated_GH(:, 2) - H_PS2T2(1:3, 4)) - r_compensation)^2 + norm(H_PS2T2(1:3, 4) - center3)^2);
% r2_new = sqrt((norm(estimated_GH(:, 2) - H_PS2T2(1:3, 4)) - r_compensation)^2 + norm(H_PS2T2(1:3, 4) - center4)^2);
% radii2 = [r1_new r2_new];
% [estimated_GH, alpha_GH, H_PS2T1, H_PS2T2] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
% center3, center4, radii1, radii2, alpha0in);
% end
% end
% save the estimated GH evolutions. Note that above gives always to estimated point for each time step.
%SSDATA.Measured_Kinematics.GH(:,TimeId) = estimated_GH(:,2);
% pause(0.3)
% str1 = ['frame = ',num2str(TimeId)]; % show the abduction angle on the plot
% str1_prop = annotation('textbox',[0.01 0.01 0.5 0.05],'String',str1,'FitBoxToText','off','BackgroundColor','w');
% str1_prop.FontSize = 16;
% cla
% if TimeId == 1
% pen_criteria = norm(SSDATA.Measured_Kinematics.GH(:,1) - H_PS2T1(1:3, 4));
%
% end
%}
% approach 2: ---------------------------------------------------------
% estimating the GH based on a fixed displacement with respect to AC
% and AA
GH_cor1 = BLDATA.Initial_Points.GH - BLDATA.Initial_Points.AC;
GH_cor2 = BLDATA.Initial_Points.GH - BLDATA.Initial_Points.AA;
GH_est1 = SSDATA.Measured_Kinematics.AC(:,TimeId) + GH_cor1;
GH_est2 = SSDATA.Measured_Kinematics.AA(:,TimeId) + GH_cor2;
SSDATA.Measured_Kinematics.GH(:,TimeId) = (GH_est1 + GH_est2)/2;
%}
% approach 3: regression model from doi:10.1016/j.jbiomech.2009.03.039
% SSDATA.Measured_Kinematics.GH(:,TimeId) = [(66.468-(0.531*norm(0.5*(SSDATA.Measured_Kinematics.AA(:,TimeId)+SSDATA.Measured_Kinematics.AC(:,TimeId))-0.5*(SSDATA.Measured_Kinematics.IJ(:,TimeId)+SSDATA.Measured_Kinematics.C7(:,TimeId))))+(0.571*85.5));
% (96.2-(0.302*norm(SSDATA.Measured_Kinematics.IJ(:,TimeId)-SSDATA.Measured_Kinematics.C7(:,TimeId)))-(0.364*186)+(0.385*85.5));
% -66.32+(0.3*norm(SSDATA.Measured_Kinematics.IJ(:,TimeId)-SSDATA.Measured_Kinematics.C7(:,TimeId)))-(0.432*85.5)];
% SSDATA.Measured_Kinematics.GH(:,TimeId) = SSDATA.Measured_Kinematics.AA(:,TimeId) + [0 0 -70]';
% SSDATA.Measured_Kinematics.GH(:,TimeId) = 0.5*(SSDATA.Measured_Kinematics.AA(:,TimeId)+SSDATA.Measured_Kinematics.AC(:,TimeId)) + [6 12 -49]';
end
% ------------------------------------------------------------------------
% 3) Estimate the HU landmark
% ------------------------------------------------------------------------
% This landmark is very easy to estimate as it lies on the middle point of
% EM and EL throughout the motion.
% display message
disp(['Estimating HU']);
SSDATA.Measured_Kinematics.HU = 0.5*(SSDATA.Measured_Kinematics.EM + SSDATA.Measured_Kinematics.EL);
% ------------------------------------------------------------------------
% 4) Estimate the CP landmark
% ------------------------------------------------------------------------
% defining where CP lies between EM and EL, and use the same ratio
% throughout the motion. In fact, what I did for HU and what I am doing for
% CP can be categorized in using the predictive equations to find out where
% the missing points are.
% display message
disp(['Estimating CP']);
a_factor = norm(BLDATA.Initial_Points.EM(:,1)-BLDATA.Initial_Points.CP(:,1));
b_factor = norm(BLDATA.Initial_Points.EL(:,1)-BLDATA.Initial_Points.CP(:,1));
SSDATA.Measured_Kinematics.CP = SSDATA.Measured_Kinematics.EM + (a_factor/(a_factor + b_factor))*(SSDATA.Measured_Kinematics.EL - SSDATA.Measured_Kinematics.EM);
% ------------------------------------------------------------------------
% 5) Estimate the TS and AI landmark (scapula kinematics)
% ------------------------------------------------------------------------
% Given that we do not have any specific scapula measurement tools, the
% evolutions of lanmarks TS and AI should be also estimated. To this end,
% we build on the estimated evolutions of landmark GH estimate the landmarks
% AI and TS. TS for instance, lies at the intersections of 3 spheres
% centered at AA, AC, and GH with radii that can be defined from one known
% initial configuration of the scapula using for instance one single MRI or
% Radiology image. I use the intersection of 4 spheres considering one
% redundant sphere, for instance AC. In other words, intersection of
% spheres centered at AA, AC, AC, and GH.
% AC, AA, TS
initial_configuration1 = [SSDATA.Measured_Kinematics.AC(:,1), ...
SSDATA.Measured_Kinematics.AA(:,1), ...
BLDATA.Initial_Points.TS(:,1)];
% GH, AC, TS
initial_configuration2 = [SSDATA.Measured_Kinematics.GH(:,1), ...
SSDATA.Measured_Kinematics.AC(:,1), ...
BLDATA.Initial_Points.TS(:,1)];
% AC, AA, AI
initial_configuration3 = [SSDATA.Measured_Kinematics.AC(:,1), ...
SSDATA.Measured_Kinematics.AA(:,1), ...
BLDATA.Initial_Points.AI(:,1)];
% GH, AC, AI
initial_configuration4 = [SSDATA.Measured_Kinematics.GH(:,1), ...
SSDATA.Measured_Kinematics.AC(:,1), ...
BLDATA.Initial_Points.AI(:,1)];
% run throughout the measured motion
for TimeId = 1:length(SSDATA.Measured_Kinematics.IJ)
% display message
disp(['Estimating TS and AI, Time Stamp : ', num2str(TimeId), '/', num2str(length(SSDATA.Measured_Kinematics.IJ))]);
Zs = (SSDATA.Measured_Kinematics.AA(:,TimeId) + SSDATA.Measured_Kinematics.AC(:,TimeId))/2 - ...
SSDATA.Measured_Kinematics.GH(:,TimeId);
Xs = cross(SSDATA.Measured_Kinematics.AA(:,TimeId) - SSDATA.Measured_Kinematics.AC(:,TimeId) , ...
SSDATA.Measured_Kinematics.GH(:,TimeId) - SSDATA.Measured_Kinematics.AC(:,TimeId));
Ys = cross(Zs, Xs);
R_s2m = [Xs/norm(Xs) Ys/norm(Ys) Zs/norm(Zs)];
if TimeId == 1
TS_in_s = R_s2m'*BLDATA.Initial_Points.TS(:,1) - R_s2m'*SSDATA.Measured_Kinematics.AC(:,TimeId);
AI_in_s = R_s2m'*BLDATA.Initial_Points.AI(:,1) - R_s2m'*SSDATA.Measured_Kinematics.AC(:,TimeId);
end
SSDATA.Measured_Kinematics.TS(:,TimeId) = R_s2m*TS_in_s + SSDATA.Measured_Kinematics.AC(:,TimeId);
SSDATA.Measured_Kinematics.AI(:,TimeId) = R_s2m*AI_in_s + SSDATA.Measured_Kinematics.AC(:,TimeId);
%}
%{
% Estimate TS
center1 = SSDATA.Measured_Kinematics.AC(:,TimeId);
center2 = SSDATA.Measured_Kinematics.AA(:,TimeId);
center3 = SSDATA.Measured_Kinematics.GH(:,TimeId);
center4 = SSDATA.Measured_Kinematics.AC(:,TimeId);
if TimeId == 1
alpha0in = [0.1 0.1]; % initial guess for the parametrization angles of the two intersecting conic sections.
% A conic section is a function of alpha.
else
alpha0in = alpha_TS;
end
[estimated_TS, alpha_TS] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
center3, center4, initial_configuration1, initial_configuration2, alpha0in,0);
% save the estimated TS evolutions. Note that above gives always to estimated point for each time step.
SSDATA.Measured_Kinematics.TS(:,TimeId) = estimated_TS(:,1);
% Estimate AI
center1 = SSDATA.Measured_Kinematics.AC(:,TimeId);
center2 = SSDATA.Measured_Kinematics.AA(:,TimeId);
center3 = SSDATA.Measured_Kinematics.GH(:,TimeId);
center4 = SSDATA.Measured_Kinematics.AC(:,TimeId);
if TimeId == 1
alpha0in = [0.1 0.5]; % initial guess for the parametrization angles of the two intersecting conic sections.
% A conic section is a function of alpha.
% ******alpha0in is sometimes very important****
else
alpha0in = alpha_AI;
end
[estimated_AI, alpha_AI] = KINEMATICS_TOOL_four_sphere_intersect(center1, center2,...
center3, center4, initial_configuration3, initial_configuration4, alpha0in,0);
% save the estimated TS evolutions. Note that above gives always to estimated point for each time step.
SSDATA.Measured_Kinematics.AI(:,TimeId) = estimated_AI(:,2);
%}
end
% ------------------------------------------------------------------------
% 6) Show the estimated results on the BUILD MOTION (Option 2) GUI page
% ------------------------------------------------------------------------
% display message
disp(['Show the estimated/measured landmarks in GREEN for the first time Stamp']);
% set the current axis
set(KRHandle, 'currentaxes', KRPlotHandles.View_Motion_Axes); hold on;
% define the wireframe of the measured motion
Measured_WF_Thorax = [SSDATA.Measured_Kinematics.IJ(:,1),...
SSDATA.Measured_Kinematics.PX(:,1),...
SSDATA.Measured_Kinematics.T8(:,1),...
SSDATA.Measured_Kinematics.C7(:,1),...
SSDATA.Measured_Kinematics.IJ(:,1)];
Measured_WF_Clavicula = [SSDATA.Measured_Kinematics.SC(:,1),...
SSDATA.Measured_Kinematics.AC(:,1)];
Measured_WF_Scapula = [SSDATA.Measured_Kinematics.AC(:,1),...
SSDATA.Measured_Kinematics.AA(:,1),...
SSDATA.Measured_Kinematics.TS(:,1),...
SSDATA.Measured_Kinematics.AI(:,1),...
SSDATA.Measured_Kinematics.GH(:,1),...
SSDATA.Measured_Kinematics.AC(:,1)];
Measured_WF_Humerus = [SSDATA.Measured_Kinematics.GH(:,1),...
SSDATA.Measured_Kinematics.HU(:,1),...
SSDATA.Measured_Kinematics.EL(:,1),...
SSDATA.Measured_Kinematics.EM(:,1)];
Measured_WF_Ulna = [SSDATA.Measured_Kinematics.CP(:,1),...
SSDATA.Measured_Kinematics.HU(:,1),...
SSDATA.Measured_Kinematics.EM(:,1),...
SSDATA.Measured_Kinematics.US(:,1)];
Measured_WF_Radius = [SSDATA.Measured_Kinematics.CP(:,1),...
SSDATA.Measured_Kinematics.RS(:,1)];
% plot the wireframes of the measured motion associated with the first time
% stamp
plot3(Measured_WF_Thorax(1,:),...
Measured_WF_Thorax(2,:),...
Measured_WF_Thorax(3,:),...
'color', 'black',...
'linewidth', 2,...
'marker', '*',...
'markerfacecolor', 'green',...
'markeredgecolor', 'green');
plot3(Measured_WF_Clavicula(1,:),...
Measured_WF_Clavicula(2,:),...
Measured_WF_Clavicula(3,:),...
'color', 'black',...
'linewidth', 2,...
'marker', '*',...
'markerfacecolor', 'green',...
'markeredgecolor', 'green');
plot3(Measured_WF_Scapula(1,:),...
Measured_WF_Scapula(2,:),...
Measured_WF_Scapula(3,:),...
'color', 'black',...
'linewidth', 2,...
'marker', '*',...
'markerfacecolor', 'green',...
'markeredgecolor', 'green');
plot3(Measured_WF_Humerus(1,:),...
Measured_WF_Humerus(2,:),...
Measured_WF_Humerus(3,:),...
'color', 'black',...
'linewidth', 2,...
'marker', '*',...
'markerfacecolor', 'green',...
'markeredgecolor', 'green');
plot3(Measured_WF_Ulna(1,:),...
Measured_WF_Ulna(2,:),...
Measured_WF_Ulna(3,:),...
'color', 'black',...
'linewidth', 2,...
'marker', '*',...
'markerfacecolor', 'green',...
'markeredgecolor', 'green');
plot3(Measured_WF_Radius(1,:),...
Measured_WF_Radius(2,:),...
Measured_WF_Radius(3,:),...
'color', 'black',...
'linewidth', 2,...
'marker', '*',...
'markerfacecolor', 'green',...
'markeredgecolor', 'green');
return;
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