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JACIII Vol.15 No.8 pp. 1019-1029
doi: 10.20965/jaciii.2011.p1019
(2011)

Paper:

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Extraction of Coordinative Structures of Motions by Segmentation Using Singular Spectrum Transformation

Hiroaki Nakanishi, Sayaka Kanata, Hirofumi Hattori,
Tetsuo Sawaragi, and Yukio Horiguchi

Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto 606-8501, Japan

Received:
March 10, 2011
Accepted:
June 19, 2011
Published:
October 20, 2011
Keywords:
segmentation of human behaviors, singular spectrum transformation, coordinative structure, multiple alignment
Abstract

In this article, we focus on the coordinative structure of human behavior, which contributes to specifying dynamics from time-series kinematic data. We propose a method for the extraction of dynamical interaction from time-series data of human behavior using Singular Spectrum Transformation. Using the proposed method, human behavior can be described as a letter string whose letters indicate where the motion segmentation is detected. We also discuss a method of extracting coordinative structures by constructing multiple alignments from the timing structure of extracted motion change points. To confirm the effectivity of the proposed method, the results of motion analysis are shown.

Cite this article as:
Hiroaki Nakanishi, Sayaka Kanata, Hirofumi Hattori,
Tetsuo Sawaragi, and Yukio Horiguchi, “Extraction of Coordinative Structures of Motions by Segmentation Using Singular Spectrum Transformation,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1019-1029, 2011.
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References
  1. [1] N. Bernstein, “The co-ordination and regulation of movements,” Pergamon Press, 1967.
  2. [2] R. Osaki and K. Uehara, “Primitive Motion Extraction for Motion Recognition by using DTW,” IEICE Technical Report, Vol.99, No.61, pp. 279-284, 1999.
  3. [3] T. Shiratori, A. Nakazawa and K. Ikeuchi, “Motions Using Motion Capture and Musical Information,” The Transactions of the Institute of Electronics, Information and Communication Engineers DII, Vol.J88, No.8, pp. 1662-1671, 2005.
  4. [4] W. Takano and Y. Nakamura, “Segmentaion of Human Behavior Patterns Based on the Probablistic Correlation,” Proceedings of The 19th Annual Conference of the Japanese Society for Artificial Intelligence, 3F1-02, 2005.
  5. [5] T. Ide and K. Inoue, “Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations,” Proc. of 2005 SIAM Int. Conf. on Data Mining (SDM 05), pp.571-576, 2005.
  6. [6] H. Sakurai, H. Nakanishi, Y. Horiguchi, and T. Sawaragi, “Extraction of Dynamic Interaction using Singular Spectrum Transformation for Motion Recognition,” Proceedings of the 36th SICE Symposium on Intelligent Systems, pp. 205-210, 2009.
  7. [7] R. Durbin, S. R. Eddy, A. Krogh, and G. Mitchison, “Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids,” Cambridge Univ. Pr. (Txp), 2005.
  8. [8] T. Akutsu, “Mathematics and Algorithms for Bioinformatics,” Kyoritu Syuppan, 2007.
  9. [9] R. R. Sokal and C. D.Michener, “A statistical method for evaluating systematic relationships,” University of Kansas Scientific Bulletin 28, pp. 1409-1438, 1958.
  10. [10] M. Kawato, M. Sasaki, H. Mishima, J. Tanji, H. Sakata, T. Murata, and M. Fujita, “Cognitice Science <4> Motion,” Iwanami Shoten, 1994.
  11. [11] M. A. Larkin et al, “ClustalW and ClustalX version 2.0,” Bioinformatics, Nov 1, Vol.23, No.21, pp. 2947-8. Epub, Sep 10. 2007.

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