@article{M82C76EA9,
title = "Parallel Gaussian Processes for Gait and Phase Analysis",
journal = "Journal of KIISE, JOK",
year = "2015",
issn = "2383-630X",
doi = "",
author = "Bong-Kee Sin",
keywords = "human gait analysis,Gaussian process,Markov chain,particle filter,von Mises distribution",
abstract = "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."
}