Muscle Activation Analysis from Gait Kinematics and Reinforcement Learning

Prayook Jatesiktat, Dollaporn Anopas, Wai Hang Kwong, Ananda Sidarta, phyllis Liang, Wei Tech Ang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

We propose the use of reinforcement learning with imitation reward to estimate muscle activation from a purely kinematic motion capture sequence without the use of any force plate or electromyography (EMG) sensors. We also demonstrate the use of this method by comparing muscle activation between normal walking and U-Turning. Our simulation demonstrated a higher level of activation during U-Turning in the biceps femoris in the swing phase and the gluteus medius during the stance phase, which is consistent with the previous studies with EMG sensors on human subjects. Activation of ankle muscles generated from the simulation, however, did not match the conventional activation patterns. The source code and the data are made publicly available for research purposes.

Original languageEnglish
Title of host publication19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022
ISBN (Electronic)9781665485845
DOIs
Publication statusPublished - 16 Jun 2022
Event19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology -
Duration: 24 May 202227 May 2022

Publication series

Name19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

Conference

Conference19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology
Period24/05/2227/05/22

Keywords

  • gait kinematics
  • muscle activation
  • reinforcement learning
  • simulation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Signal Processing
  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

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