Acquisition of Embodied Knowledge on Gesture Motion by Singular Value Decomposition
Isao Hayashi*, Yinlai Jiang**, and Shuoyu Wang**
*Graduate School of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki, Osaka 569-1095, Japan
**School of Systems Engineering, Kochi University of Technology, 185 Miyanokuti, Tosayamada, Kami, Kochi 782-8502, Japan
Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human embodied knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring embodied knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.
-  D. Tsuda, S. Wang, N. Miura, and Y. Jiang, “Development and Safety Validation of Lead for a Guidance Robot,” J. of Biomedical Fuzzy Systems Association, Vol.11, No.1, pp. 43-48, 2009. (in Japanese)
-  J. K. Aggarwal and Q. Cai, “Human Motion Analysis: A Review,” Computer Vision and Image Understanding, Vol.73, No.3, pp. 428-440, 1999.
-  A. Kendon, “Gesture: Visible Action as Utterance,” Cambridge University Press, 2004.
-  R. Bowden, “Learning Statistical Models of Human Motion,” IEEE Workshop on Human Modelling, Analysis and Synthesis (CVPR2000), pp. 10-17, 2000.
-  K. Furukawa, S. Igarashi, K. Ueno, T. Ozaki, S. Morita, N. Tamagawa, T. Okuyama, and I. Kobayashi, “Modeling Human Skill in Bayesian Network,” Electric Transaction of Artificial Intelligence (ETAI), Linkoping University Electronic Press, 2002.
-  S. Igarashi, K. Ueno, T. Ozaki, S. Morita, and K. Furukawa, “Skill Modeling in Cello Performance by Bayesian Networks,” Technical Report of IEICE, Vol.102, No.709, pp. 1-6, 2003.
-  R. Balasubramaniam and M. T. Turvey, “Coordination Modes in the Multisegmental Dynamics of Hula Hooping,” Biological Cybernetics, Vol.90, pp. 176-190, 2004.
-  S. Furuya and H. Kinoshita, “Organization of the Upper Limb Movement for Piano Key-depression Differs between Expert Pianists and Novice Players,” Experimental Brain Research, Vol.185, No.4, pp. 581-593, 2008.
-  S. Furuya, R. Osu, and H. Kinoshita, “Effective Utilization of Gravity During Arm Downswing in Keystroke by Expert pianists,” Neuroscience, Vol.164, No.2, pp. 822-831, 2009.
-  Y. Jiang, I. Hayashi, M. Hara, and S. Wang, “Three-dimensional Motion Analysis for Gesture Recognition Using Singular Value Decomposition,” Proc of 2010 IEEE Int. Conf. on Information and Automation, pp. 805-810, 2010.
-  I. Hayashi, Y. Jiang, M. Hara, and S. Wang, “Knowledge Acquisition from Motion of Evacuation Instruction Using Singular Value Decomposition,” Proc. of the 26th Fuzzy System Symposium, pp. 824-829, 2010. (in Japanese)
-  S. Mitra and T. Acharya, “Gesture recognition: a survey,” IEEE Trans. on Systems, Man, and Cybernetics, Part C, Vol.37, No.3, pp. 311-324, 2007.
-  Y. Iwai, K. Watanabe, Y. Yagi, and M. Yachida, “Gesture Recognition Using Colored Gloves,” Proc. of IEEE Int. Conf. on Patter Recognition (ICPR96), Vol.A, pp. 662-666, 1996.
-  M. V. Lamar, M. S. Bhuiyan, and A. Iwata, “Hand Gesture Recognition Using T-CombNET: A New Neural Network Model,” IEICE trans. on information and systems, Vol.E83-D, No.11, pp. 1986-1995, 2000.
-  T. E. Jerde, J. F. Soechting, and M. Flanders, “Biological Constraints Simplify the Recognition of Hand Shapes,” IEEE Trans. on Biomedical Engineering, Vol.50, No.2, pp. 265-269, 2003.
-  H. I. Suk, B. K. Sin, and S. W. Lee, “Hand Gesture Recognition Based on Dynamic Bayesian Network Framework,” Pattern Recognition, Vol.43, pp. 3059-3072, 2010.
-  C. Uras and A. Verri, “Hand gesture recognition from edge maps,” Proc. of Int.Workshop on Automatic Face and Gesture Recognition, pp. 116-121, 1995.
-  Y. Fang, K.Wang, J. Cheng, and H. Lu, “A Real-Time Hand Gesture Recognition Method,” Proc. of Int. Conf. on Multimedia and Expo (ICME2007), pp. 995-998, 2007.
-  I. Hayashi, Y. Jiang, and S. Wang, “Embodied Knowledge of Gesture Motion Acquired by Singular Spectrum Analysis,” Proc. of the First Int. Conf. on Vulnerability and Risk Analysis andManagement (ICVRAM2010) and the Fifth Int. Symp. on Uncertainty Modeling and Analysis (ISUMA2010), pp. 45-52, 2011.
-  T. Ide and K. Inoue, “Knowledge Discovery from Heterogeneous Dynamic Systems Using Change-point Correlations,” Proc. 2005 SIAM Int. Conf. on Data Mining (SDM05), pp. 571-576, 2005.
-  K. Mishima, S. Kanata, H. Nakanishi, Y. Horiguchi, and T. Sawaragi, “Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition,” Proc of the 53rd annual conf. of the Institute of Systems, Control and Information Engineers (SCI09), pp. 409-410, 2009. (in Japanese)
-  M. E. Wall, A. Rechtsteiner, and L. M.Rocha, “Singular Value Decomposition and Principal Component Analysis.” in A Practical Approach to Microarray Data Analysis, D. P. Berrar, W. Dubitzky, and M. Granzow (Eds.) Kluwer pp. 91-109, 2003.
-  K. Doya, “Introduction to Computational Neuroscience: Toward Understanding of the Mechanisms of Learning in the Brain,” Science Sha, 2007. (in Japanese)
-  M. Kawato, “Internal Models for Motor Control and Trajectory Planning,” Current Opinion in Neurobiology, Vol.9, Vol.6, pp. 718-727, 1999.
-  R. C. Miall, D. J. Weir, D. M. Wolpert, and J. F. Stein, “Is the cerebellum a Smith predictor?” J. of Motor Behavior, Vol.25, pp. 203-216, 1993.
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