TY  - JOUR
T1  - Parallel Gaussian Processes for Gait and Phase Analysis
AU  - Sin, Bong-Kee 
JO  - Journal of KIISE, JOK
PY  - 2015
DA  - 2015/1/14
DO  - 
KW  - human gait analysis
KW  - Gaussian process
KW  - Markov chain
KW  - particle filter
KW  - von Mises distribution
AB  - This paper proposes a sequential state estimation model consisting of continuous and discrete variables, as a way of generalizing all discrete-state factorial HMM, and gives a design of gait motion model based on the idea. The discrete state variable implements a Markov chain that models the gait dynamics, and for each state of the Markov chain, we created a Gaussian process over the space of the continuous variable. The Markov chain controls the switching among Gaussian processes, each of which models the rotation or various views of a gait state. Then a particle filter-based algorithm is presented to give an approximate filtering solution. Given an input vector sequence presented over time, this finds a trajectory that follows a Gaussian process and occasionally switches to another dynamically. Experimental results show that the proposed model can provide a very intuitive interpretation of video-based gait into a sequence of poses and a sequence of posture states.