Browse > Article
http://dx.doi.org/10.5626/JOK.2015.42.6.748

Parallel Gaussian Processes for Gait and Phase Analysis  

Sin, Bong-Kee (Pukyong National Univ.)
Publication Information
Journal of KIISE / v.42, no.6, 2015 , pp. 748-754 More about this Journal
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.
Keywords
human gait analysis; Gaussian process; Markov chain; particle filter; von Mises distribution;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. K. Aggarwal and M.S.Ryoo, "Human activity analysis: A review," J. ACM Computing Surveys, Vol. 43, Issues 3, Article No. 16, p. 43, Apr. 2011.
2 G. Fan, X. Zhang and M. Ding, "Gaussian Process for Human Motion Modeling: A Comparative Study," Proc. IEEE Int'l W. on ML for Signal Proc., Beijing, China, 2011.
3 "Dynamic Bayesian Network based Gait Analysis," Journal of KIISE: Software and Applicatoins, Vol. 37, No. 5, pp. 354-362, May 2010.
4 Z. Ghahramani and M. Jordan, "Factorial Hidden Markov Models," Machine Learning, Vol. 29, No. 2/3, pp. 245-273, 1997.   DOI
5 C. E. Rasmussen and C. K. Williams, Gaussian Process and Machine Learning, MIT Press, 2006.
6 J. Wang, D. Fleet and A. Hertzmann, "Gaussian Process Dynamical Models for Human Motion," IEEE. Trans. on PAMI, Vol. 30, pp. 283-298, 2008.   DOI
7 N. Lawrence, "Gaussian Process Latent Variable Models for Visualization of High Dimensional Data," Proc. NIPS, 2003.
8 A.Elgammal and C.-S.Lee, "Tracking People on torus," IEEE Trans. on PAMI, Vol. 31, pp. 520-538, 2009.   DOI
9 X. Zhang and G. Fan, "Joint Gait-Pose Manifold for Video-based Human Motion Estimation," Proc. CVPR the 3rd Workshop on Machine Learning for Vision-based Motion Analysis, 2011.
10 "Parallel GPs for Gait Pose/Phase Analysis," KIISE 2014 Winter Conference, Pyeongchang, Dec. 2014.
11 R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, Vol. 82, No. 1, pp. 35-45, 1960.   DOI
12 C. Bishop. Pattern Recognition and Machine Learning, Berlin: Springer, 2006.
13 T. F. Novachek, "The Biomechanics of Running," Gait and Posture, Vol. 7, pp. 77-95, 1998.   DOI
14 N. J. Gordon, D. J. Salmond, and A. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," IEE Proc. F on Radar and Signal Processing, Vol. 140, No. 2, pp. 107-113, 1993.   DOI