Section: New Results

Human motion decomposition

The aim of the work is to find ways of representing human movement in order to extract meaningful physical and cognitive information.

After the realization of a state of the art on human movement, several methods are compared: principal component analysis (PCA), Fourier series decomposition and inverse optimal control.

These methods are used on a signal comprising all the angles of a walking human being. PCA makes it possible to understand the correlations between the different angles during the trajectory. Fourier series decomposition methods are used for a harmonic analysis of the signal. Finally, inverse optimal control sets up a modeling of the engine control to highlight qualitative properties characteristic of the whole motion. These three methods are tested, combined and compared on data from the EWalk database (http://gamma.cs.unc.edu/GAIT/#EWalk) in order to test emotion recognition based on these decompositions and simple classifiers.