Can Principal Component Analysis be Applied in Real Time to Reduce the Dimension of Human Motion Signals?

Principal Component Analysis (PCA) is a usual method in multivariate analysis to reduce data dimensionality. PCA relies on the definition of a linear transformation of the data through an orthonormal matrix that is computed on the basis of the dataset itself. In this work we discuss the application of PCA on a set of human motion data and the cross validation of the result. The cross validation procedure simulates the application of the transformation on real time data. The PCA proved to be suitable to analyze data in real time and showed some interesting behavior on the data used as cross validation.