OS20-4 Developing a Body Posture Detection for Fitness

Title:	OS20-4 Developing a Body Posture Detection for Fitness
Publication:	ICAROB2025
Volume:	30
Pages:	563-566
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2025.OS20-4
Author(s):	Kai Xuan Chong, Abdul Shibghatullah, Kasthuri Subaramaniam, Chit Su Mon
Publication Date:	February 13, 2025
Keywords:	OpenCV, Tkinter, Body Posture Detection, Graphical User Interface
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.
PDF File:	https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS20/OS20-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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