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

Kazuo Kiguchi, Ryosuke Matsuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538627259
DOIs
Publication statusPublished - Feb 2 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017Dec 1 2017

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period11/27/1712/1/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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  • Cite this

    Kiguchi, K., & Matsuo, R. (2018). Accident prediction based on motion data for perception-assist with a power-assist robot. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (pp. 1-5). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285408