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:
Yusuke Maeda and Tatsuya Ushioda, “Hidden Markov Modeling of Human Pivoting,” J. Robot. Mechatron., Vol.19, No.4, pp. 444-447, 2007.
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