Intelligent Perfect Pose and Health Analysis in Sports and Fitness
With the growing emphasis on health and wellness, exercise and fitness have become essential components of maintaining overall well-being. The application of artificial intelligence (AI) algorithms in exercise and fitness training holds significant potential. This paper explores the integration of computer vision, image processing, and AI prediction algorithms for analyzing sports-related multimedia images. Through the acquisition and preprocessing of motion images, key features and movement patterns are extracted. Deep learning and pattern recognition techniques are then employed to evaluate the posture, motion, and physical condition of individuals during exercise. Based on these analyses, personalized health recommendations and guidance are provided. Experimental results demonstrate that this approach can accurately assess posture, movement, and physical state, offering tailored advice that enhances training outcomes, reduces the risk of injury, and improves both the effectiveness and quality of exercise routines.
Authors
- Jasper Liu
- D.C.C. Lam (ORCID: https://orcid.org/0000-0002-4688-1435)
- HengAn Wu (ORCID: https://orcid.org/0000-0003-0288-1617)
- Omar Cruz (ORCID: https://orcid.org/0000-0002-4417-8981)
- Minh Doan (ORCID: https://orcid.org/0000-0002-3235-0457)
- Mark Garcia
- Kenji Longid
Institutions
- Google (United States) (US)
- University of La Verne (US)
Publication Details
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1109/ichi64645.2025.00095
- Primary Topic
- Physical Education and Training Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of La Verne