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Dynamic Bayesian Network-Based Gait Analysis  

Kim, Chan-Young (부경대학교 컴퓨터공학과)
Sin, Bong-Kee (부경대학교 IT 융합 응용 공학과)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.5, 2010 , pp. 354-362 More about this Journal
Abstract
This paper proposes a new method for a hierarchical analysis of human gait by dividing the motion into gait direction and gait posture using the tool of dynamic Bayesian network. Based on Factorial HMM (FHMM), which is a type of DBN, we design the Gait Motion Decoder (GMD) in a circular architecture of state space, which fits nicely to human walking behavior. Most previous studies focused on human identification and were limited in certain viewing angles and forwent modeling of the walking action. But this work makes an explicit and separate modeling of pedestrian pose and posture to recognize gait direction and detect orientation change. Experimental results showed 96.5% in pose identification. The work is among the first efforts to analyze gait motions into gait pose and gait posture, and it could be applied to a broad class of human activities in a number of situations.
Keywords
Gait Analysis; Dynamic Bayesian Network; Factorial Hidden Markov Model;
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