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.

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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

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article

Intelligent Perfect Pose and Health Analysis in Sports and Fitness

Jasper Liu, D.C.C. Lam, HengAn Wu, Omar Cruz et al.
Physical Education and Training Studies
article

Intelligent Perfect Pose and Health Analysis in Sports and Fitness

Jasper Liu, D.C.C. Lam, HengAn Wu, Omar Cruz, Minh Doan, Mark Garcia, Kenji Longid
article en

Abstract

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.

Google (United States) (US), University of La Verne (US)
University of La Verne
Gender equality
Openalex Percentile: Top 71%
Physical Education and Training Studies
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