Bayesian Hidden Markov Models for Segmentation of Gait Motion Capture Data

Abstract

The interpretation of 3DGA data often requires the segmentation of joint angle time­series (e.g. the identification of rockers in the ankle sagittal plane kinematics). We introduce a novel method for the automatic segmentation of joint angle time­series based on a Bayesian time­series model called Hierachical Dirichlet Process Hidden Markov Model (HDP­HMM) [1]. The goal of the method is to segment a joint angle time­series in a set of piecewise polynomial curves, while at the same time estimating the curve parameters and the time­correlation between the segments. The proposed method is suited for segmention of a set of time­series, (e.g. , a set of gait cycles exhibiting a specific pattern identified by a clinician) and to learn a probabilistic shape signature that can be used for classification of gait trials. Due to its Bayesian nature, the proposed method is able to incorporate clinical prior knowledge to produce a clinically meaningful segmentation.status: publishe