An OpenPose-Based Exercise and Performance Learning Assistant Design for Self-Practice Yoga

Cheng Hsien Lin, Shih Wei Shen, Irin Tri Anggraini, Nobuo Funabiki, Chih Peng Fan

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

Abstract

In this paper, a performance learning assistant design based on OpenPose is studied for self-practice Yoga. Firstly, the skeleton information of the human body is extracted through OpenPose. Secondly, to calculate the angle values of the selected keypoints, the vectors of the user and the instructor are obtained based on the center points. Next, by using the angle values, the angle differences between the user and the instructor are calculated. Finally, the scoring system is developed to calculate the total scores for evaluation of the user's Yoga posture. The experimental results show that the proposed design effectively detects the posture differences between the user and the instructor. Moreover, the proposed design performs up to 5.5 frames per second (FPS) on the GPU-based embedded platform.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages456-457
Number of pages2
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: Oct 12 2021Oct 15 2021

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period10/12/2110/15/21

Keywords

  • angle difference
  • OpenPose
  • scoring system
  • Yoga

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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