Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities

Abstract

In this paper we describe a system for automatic gait analysis. Different kinds of human gait are recognized using sequences of grey--level images. No markers are needed to get the trajectories of different body parts. The tracking of body parts and the classification are based on statistical models. We model several body parts and the background as mixture densities. The positions are determined iteratively, we begin with the most stable part to find. The anatomy of a human body restricts the area to search for the next one. From the trajectories, features for gait analysis are derived. These are used to train hidden Markov models (HMMs), one HMM for each kind of gait. 1 Introduction Application of gait analysis can be found in several fields, for example medical diagnosis, physical therapy, and sports. It is used to get information about gait disorders of patients with knee or hip pain, or tumors. It is helpful to control cycles of motion for example in rehabilitation or training. I...