Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2012.18.7.644

Gait Phases Classification using Joint angle and Ground Reaction Force: Application of Backpropagation Neural Networks  

Chae, Min-Gi (University of Science & Technology)
Jung, Jun-Young (University of Science & Technology)
Park, Chul-Je (University of Science & Technology)
Jang, In-Hun (Korea Institute of Industrial Technology)
Park, Hyun-Sub (Korea Institute of Industrial Technology)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.18, no.7, 2012 , pp. 644-649 More about this Journal
Abstract
This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.
Keywords
gait phase classification; neural network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By SCOPUS : 0
연도 인용수 순위
1 N. Mijailovic, M. Gavrilovic, and S. Rafajlovic, "Gait phases recognition from accelerations and ground reaction forces: application of neural networks," Telfor Journal, vol. 1, no. 1, pp. 34-36, 2009.
2 S. B. Cho and J. H. Kim, "Acceleration techniques of backpropagation learning algorithm : classification and comparision," Journal of KIISE (in Korean), vol. 18, no. 6, pp. 649-660, 1991.
3 K. Neumann, Gehen Verstehen, 2nd Ed., Georg Thime Verlag KG, Stuttgart, 2006.
4 J. Wu, "Kernel-Based feature extraction for automated gait classification using kinetics data," Natural Computation, vol. 7, no. 1, pp. 28-33, Oct. 2008.
5 M. Y. Lee and S. Y. Lee, "Gait estimation system for leg diagnosis and rehabilitation using gyroscopes," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 16, no. 9, pp. 866-871, 2010.   과학기술학회마을   DOI   ScienceOn
6 S. K. Ng and H. J. Chizeck, "Fuzzy model identification for classification of gait events in paraplegics" IEEE Trans. on Fuzzy Systems, vol. 5, no. 4, pp. 536-544, Nov, 1997.   DOI   ScienceOn
7 D. A. Neumann, Kinesiology of the Musculoskeletal System, 2nd Ed., Mosby, New York, 2010.
8 S. R. Lee, G. S. Heo, O. H. Kang, and C. Y. Lee, "Recognition of stance phase for walking assistive devices by foot pressure patterns," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 3, pp. 223-228, 2011.   과학기술학회마을   DOI
9 K. Kaczmarczyk, A. Wit, M. Krawczyk, and J. Zaborski, "Gait classification in post-stroke patients using artificial neural networks," Gait Posture, vol. 30, no. 2, pp. 207-210, Aug. 2009.   DOI
10 Y. Shimada, S. Ando, T. Matsunaga, A. Misawa, T. Aizawa, T. Shirahata, and E. Itoi, "Clinical application of acceleration sensor to detect the swing phase of stroke gait in functional electrical stimulation," The Tohoku Journal of Experimental Medicine, vol. 207, no. 3, pp. 197-202, 2005.   DOI   ScienceOn