A classification method of motion database using hidden Markov model

Ayaka Matsui, Satoshi Nishimura, Seiichiro Katsura

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper proposes a classification method of a stored motion-data. Robotic technology has made progress, and robots are demanded to cooperate with human. To realize the human and robot exist together, a motion recognition system is needed. In the conventional method, the stored motion-data is classified in advance to search the motion quickly and accurately. However, the task of the classification will be very complex when the stored data is increased. Therefore, the classification system of stored data automatically is required. Since the human motion is time series information and unsteady signal, a hidden Markov Model is used as the probability models. Additionally, this paper shows that Kullback-Leiblaer divergence indicates the similarity index of the stored motion. At this time, the motion is classified according to the acceleration information, which includes the pure force and position information. The validity of the proposed method is confirmed by simulations.

本文言語English
ホスト出版物のタイトルProceedings - 2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2232-2237
ページ数6
ISBN(印刷版)9781479923991
DOI
出版ステータスPublished - 2014
イベント2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014 - Istanbul, Turkey
継続期間: 2014 6月 12014 6月 4

出版物シリーズ

名前IEEE International Symposium on Industrial Electronics

Other

Other2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014
国/地域Turkey
CityIstanbul
Period14/6/114/6/4

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学

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