Real-Time Accident Prediction Using Deep Learning for

Simi Asher, Kazuo Kiguchi

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

Abstract

Power-Assist exoskeleton robots are useful for assisting activities of daily living (ADL) for physically weak persons. Such persons are also likely to have deteriorated perception ability, hence, it is important for power-Assist robots to have perception-Ability to ensure the safety of the user. Perception-Assist can be accomplished by observing the interaction between the environment and the user, determining the possibility of accidents, such as falling, and preventing them by modifying the wearer's motion. Therefore, in order to accomplish perception-Assist, it is essential to predict the possibility of accidents in real-Time. In this paper, we propose a method that uses deep learning to predict the possibility of accidents based on the wearer's motion, wearer's motion intention from EMG signals, zero moment point (ZMP) and information from the surrounding environment. The effectiveness of the proposed method is evaluated by performing experiments to test accident-prediction in real-Time.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages333-338
Number of pages6
ISBN (Electronic)9781728172927
DOIs
Publication statusPublished - Sep 28 2020
Event2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020 - Virtual, Asahikawa, Hokkaido, Japan
Duration: Sep 28 2020Sep 29 2020

Publication series

Name2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020

Conference

Conference2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
CountryJapan
CityVirtual, Asahikawa, Hokkaido
Period9/28/209/29/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Control and Optimization
  • Instrumentation
  • Artificial Intelligence

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