JRM Vol.19 No.6 pp. 705-714
doi: 10.20965/jrm.2007.p0705


Motion Analysis by Independent Component Analysis with Phase Difference Information Among Joints

Kiyoshi Hoshino and Tomoko Sato

Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

April 7, 2007
September 4, 2007
December 20, 2007
movement analysis, independent component analysis (ICA), phase difference information among joints, interpolation and extrapolation of movement
We configured a system that interpolates and extrapolates two different human movements at an arbitrary ratio for both periodic and discrete movement. This could, for example, extrapolate possible future movement from two data points from the past and present for a person with a certain disorder and enable quantitative assessment of the disorder by interpolating or extrapolating two typical movements of nondisabled and disabled persons at an arbitrary ratio and by comparing the result to the movement of subjects. We used independent component analysis involving phase difference information between joint movements. To demonstrate the system’s effectiveness, we generated three different gaits of periodic movement and conducted experiments with and without considering phase differences between joint movement. When results were implemented in a human model in computer graphics (CG) to picture movement, the system considering phase differences reconstructed the original movement naturally even with a number of independent variables as small as two. Movement analysis not considering phase differences, however, was unnatural, especially in ankle movement, due to the lack of appropriate phase differences between the knee and hip. We synthesized and evaluated discrete movement from two winning poses – one powerful and one weak – by interpolating movement I at 50% and movement II at 50%, and extrapolation of movement I at 150%. The former generated discrete movement with an in-between impression and the latter with a powerful impression. These results demonstrate that both periodic and discrete movement can be expressed by a small number of independent variables when phase difference information between joint movements is used appropriately.
Cite this article as:
K. Hoshino and T. Sato, “Motion Analysis by Independent Component Analysis with Phase Difference Information Among Joints,” J. Robot. Mechatron., Vol.19 No.6, pp. 705-714, 2007.
Data files:
  1. [1] H. Sato and P. D. Andrew, “Feasibility of estimating joint moments during gait with only kinematic data,” J. Mechanics in Medicine and Biology, 2, 2, pp. 131-145, 2002.
  2. [2] H. Kawakami, N. Sugano, K. Yonenobu, H. Yoshikawa, T. Ochi, A. Hattori, and N. Suzuki, “Gait analysis system for assessment of dynamic loading axis of the knee,” Gait Posture, 21, 1, pp. 125-130, 2005.
  3. [3] E. Genda, K. Oota, Y. Suzuki, K. Koyama, and T. Kasahara, “A new walking orthosis for paraplegics: hip and ankle linkage system,” Prosthet. Orthot. Int., 28, 1, pp. 69-74, 2004.
  4. [4] M. Shiba, “Extraction of Kansei information in dance movements,” Proc. of ATR Workshop on Virtual Communication Environments Bridges over Art/Kansei and VR Technologies, pp. 68-88, 1998.
  5. [5] S. Kamisato, S. Odo, Y. Ishikawa, and K. Hoshino, “Extraction of motion characteristics corresponding to sensitivity information in dance pattern,” J. Advanced Computational Intelligence and Intelligent Informatics, 8, 2, pp. 168-180, 2004.
  6. [6] K. Hoshino, “International and extrapolation of repeated motions obtained with magnetic motion capture,” IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences, E87-A, 9, pp. 2401-2407, 2004.
  7. [7] H. Mori and J. Hoshino, “Independent Component Analysis and Synthesis of Human Motion,” ICASSP2002, IMDSP-P04.08, pp. 265-266, 2002.
  8. [8] A. Hyvarinen, J. Karhunen, and E. Oja, “Independent component analysis (Adaptive and learning systems for signal processing, communications and control Series,” Wiley-Interscience, 2001.
  9. [9] K. Hoshino and A. Hoshino, “Quantitative estimation of walking in blind children with multivariate autoregressive model,” Proc.1999 International Symposium on Nonlinear Theory and its Applications, 7, 2, pp. 597-600, 1999.
  10. [10] K. Goetz-Neumann, “Gehen verstehen. Ganganalyse in der Physiotherapie,” Thieme Georg Verlag, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024