Accident prediction based on motion data for perception-assist with a power-assist robot

Kazuo Kiguchi, Ryosuke Matsuo

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

8 被引用数 (Scopus)

抄録

Power-assist robots are expected to help facilitate the daily living motion of physically weak persons. Perception-assist has been studied to secure the safety of robot users whose sensory abilities are deteriorated or limited. The interaction between the human and environment must be carefully observed by the robot to determine the possibility of an accident in perception-assist. When the robot detects a potential accident during this interaction, it tries to avoid the accident by modifying the user's motion automatically using perception-assist. Therefore, it is important for the robot to predict potential accidents, such as falling, as soon as possible. In this paper, an accident prediction method for lower-limb perception-assist is proposed and evaluated for effectiveness. In the proposed method, the possibility of accident is predicted based on the lower-limb motion and zero-moment point of the robot user as well as information from the surrounding environment. A multilayer artificial neural network is applied in the proposed method.

本文言語英語
ホスト出版物のタイトル2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-5
ページ数5
ISBN(電子版)9781538627259
DOI
出版ステータス出版済み - 2 2 2018
イベント2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, 米国
継続期間: 11 27 201712 1 2017

出版物シリーズ

名前2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
2018-January

その他

その他2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
国/地域米国
CityHonolulu
Period11/27/1712/1/17

All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ サイエンスの応用
  • 制御と最適化

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