Hi,
To compute the first three principal components you need the numeric time-series of your three joint angles (thigh, shank, foot). Assuming you have 3 vectors such that:
thighAngle : N-by-1
shankAngle : N-by-1
footAngle : N-by-1
Once you have those three columns, PCA is straightforward:
X = [thighAngle(:) shankAngle(:) footAngle(:)];
Xm = X - mean(X,1);
% 2. Run PCA (Statistics & ML Toolbox)
[coeff, score, latent, ~, explained, mu] = pca(X); % ‘coeff’ are the eigenvectors
PC1_dir = coeff(:,1); % unit-length direction of the first PC
PC2_dir = coeff(:,2);
PC3_dir = coeff(:,3);
PC1_eig = latent(1); % eigenvalue (variance captured by PC1)
PC2_eig = latent(2);
PC3_eig = latent(3);
PC1_score = score(:,1); % time-series of the loop projected onto PC1
Here is the documentation of PCA:
Hope this helps!
