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Motion Control of Training Robot with Enhanced Defecation Control Ability Based on Active and Passive

  • Jiayin Han
  • , Diansheng Chen*
  • , Yinghan Wang
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the significant increase of social aging trend, the defecation and control ability of the elderly has become an important issue in the field of elderly rehabilitation. This paper designs a training robot system based on active and passive defecation ability enhancement, which aims to deal with the problem of defecation ability obstacle of the elderly. The system includes upper computer, lower computer, sensor and robot execution unit. The patient's motion information is obtained through the perception system to achieve active and passive motion training control. The sports training module includes sit ups, hip joint movement. The position based control strategy is adopted to achieve stable passive motion through PID controller and predefined trajectory. In the aspect of assist training control, motion trend recognition and fuzzy PID algorithm are introduced to achieve safe and effective assist rehabilitation. Through experiments to verify the effectiveness of the system, it provides a comprehensive and flexible rehabilitation training solution for the elderly to enhance their defecation control ability.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages449-454
Number of pages6
ISBN (Electronic)9798350372601
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024 - Alesund, Norway
Duration: 24 Jun 202428 Jun 2024

Publication series

Name2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

Conference

Conference2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024
Country/TerritoryNorway
CityAlesund
Period24/06/2428/06/24

Keywords

  • active and passive training
  • motion control
  • rehabilitation robot
  • trend identification

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