Developing a Body Posture Detection for Fitness

Kai Xuan Chong, Abdul Samad Bin Shibghatullah, Kasthuri Subaramaniam, Chit Su Mon

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

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Abstract

The Body Posture Detection System for Fitness is an innovative technology that aims to enhance exercise technique and movement patterns by providing real-time monitoring and feedback. It utilizes computer vision and machine learning algorithms to track and analyze body movements during fitness. The system's ability to provide immediate feedback and correction significantly improves exercise effectiveness and user safety. It also comes with the userfriendliness of the system, potentially incorporating a Graphical User Interface (GUI) for easy navigation and accessibility. To address time and budget constraints, the approach of this research will choose the Rapid Application Development (RAD) Model as the system development approach. This methodology consists of four phases which are: requirement planning, user design, rapid construction, and transition, and each is accompanied by deliverables. These efforts are aimed at enhancing the functionality and usability of the Body Posture Detection System for Fitness while addressing user needs and optimizing fitness training experiences.

Original languageEnglish
Title of host publicationProceedings of The 2025 International Conference on Artificial Life and Robotics (ICAROB 2025)
EditorsYingmin Jia, Takao Ito, Ju-Jang Lee
PublisherALife Robotics Corporation Ltd
Pages563-566
Number of pages4
ISBN (Print)9784991333729
DOIs
Publication statusPublished - 13 Feb 2025
Event30th International Conference on Artificial Life and Robotics 2025 - Oita, Japan
Duration: 13 Feb 202516 Feb 2025

Conference

Conference30th International Conference on Artificial Life and Robotics 2025
Abbreviated titleICAROB 2025
Country/TerritoryJapan
CityOita
Period13/02/2516/02/25

Keywords

  • Body Posture Detection
  • Graphical User Interface
  • OpenCV
  • Tkinter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation

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