JRM Vol.19 No.4 pp. 444-447
doi: 10.20965/jrm.2007.p0444


Hidden Markov Modeling of Human Pivoting

Yusuke Maeda* and Tatsuya Ushioda**

*Division of Systems Research, Faculty of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan

**Department of Mechanical Engineering, Division of Systems Integration, Graduate School of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan

January 11, 2007
April 16, 2007
August 20, 2007
hidden Markov models (HMM), minimum description length (MDL), graspless manipulation
In modeling human movement using hidden Markov models (HMM), the “optimal” HMM with an appropriate number of states is determined based on the minimum description length (MDL) criterion. Human pivoting, typifying graspless manipulation, is modeled using Gaussian mixture HMMs. Analyzing the obtained HMMs using metric multidimensional scaling (MDS) showed the features of individual movement. Such dissimilarity analysis can be used to validate models of tacit skills in human manipulation.
Cite this article as:
Y. Maeda and T. Ushioda, “Hidden Markov Modeling of Human Pivoting,” J. Robot. Mechatron., Vol.19 No.4, pp. 444-447, 2007.
Data files:
  1. [1] L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. of IEEE, Vol.77, No.2, pp. 257-286, 1989.
  2. [2] B. Hannaford and P. Lee, “Hidden Markov model analysis of force/torque information in telemanipulation,” Int. J. of Robotics Research, Vol.10, No.5, pp. 528-539, 1991.
  3. [3] J. Yang, Y. Xu, and C. S. Chen, “Hidden Markov model approach to skill learning and its application,” IEEE Trans. on Robotics and Automation, Vol.10, No.5, pp. 621-631, 1994.
  4. [4] M. C. Nechyba and Y. Xu, “Stochastic similarity for validating human control strategy models,” IEEE Trans. on Robotics and Automation, Vol.14, No.3, pp. 437-451, 1998.
  5. [5] K. Itabashi, S. Yea, T. Suzuki, and S. Okuma, “Acquisition of the human skill with hidden Markov model,” T. of Soc. of Instrument and Control Engineers, Vol.34, No.8, pp. 890-897, 1998 (in Japanese).
  6. [6] T. Inamura, H. Tanie, and Y. Nakamura, “From stochastic motion generation and recognition to geometric symbol development and manipulation,” Proc. of IEEE-RAS Int. Conf. on Humanoid Robots, 1b-02, 2003.
  7. [7] K. Hirana, T. Nozaki, T. Suzuki, S. Okuma, K. Itabashi, and F. Fujiwara, “Quantitative evaluation for skill controller based on comparison with human demonstration,” IEEE Trans. on Control Systems Technology, Vol.12, No.4, pp. 609-619, 2004.
  8. [8] T. S. Han and K. Kobayashi, “Mathematics of Information and Coding,” American Mathematical Society, 2001.
  9. [9] Y. Aiyama, M. Inaba, and H. Inoue, “Pivoting: A new method of graspless manipulation of object by robot fingers,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 136-143, Yokohama, Japan, 1993.
  10. [10] HTK (Hidden Markov Model Toolkit).
  11. [11] R,

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