THE IMPACT OF PHYSICAL EXERCISE AND INTELLIGENT SPORTS EVALUATION USING DEEP LEARNING APPROACH UNDER THE MEDIATING ROLE OF SOCIAL SUPPORT

This study develops an intelligent sports evaluation system based on deep learning, designed to accurately recognize and count physical exercise movements. It also investigates the impact of physical activity on college students’ mental health, with a focus on the mediating role of social support. The system consists of three core modules: posture recognition, movement counting, and psychological prediction. The posture recognition module employs the Spatial Temporal Graph Convolutional Network (ST-GCN) and its improved variants to analyze skeletal motion. The movement counting module evaluates the accuracy of counting various sports actions using a self-constructed dataset. The psychological prediction module examines how social support mediates the relationship between physical exercise and mental health through an experimental design. Results indicate that the ST-GCN model achieves 93.7% accuracy and a 93.5% F1-score in posture recognition, demonstrating an effective balance between performance and computational efficiency. In movement counting, both missed detection and false detection rates remain low, with recognition rates reaching 95.7% for standard actions and 87.3% for non-standard actions. The psychological prediction module shows that social support significantly mediates the effect of physical exercise on mental health, accounting for 47.4% of the total effect. Overall, the intelligent sports evaluation system performs strongly in both posture recognition and movement counting, providing precise support for physical education and training. The study also confirms the crucial mediating role of social support in the relationship between physical activity and mental health among college students. These findings offer a theoretical foundation for improving physical education and student health management in higher education and hold practical value for application in university physical education programs, athletic training, and mental health interventions.

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Publication Details

Journal
Journal of Mechanics in Medicine and Biology
Published
2025-10-24
DOI
https://doi.org/10.1142/s0219519426400063
Primary Topic
Diverse Approaches in Healthcare and Education Studies
Type
article
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article

THE IMPACT OF PHYSICAL EXERCISE AND INTELLIGENT SPORTS EVALUATION USING DEEP LEARNING APPROACH UNDER THE MEDIATING ROLE OF SOCIAL SUPPORT

Ying Yan, Jamalsafri Saibon
Journal of Mechanics in Medicine and Biology
Diverse Approaches in Healthcare and Education Studies
article

THE IMPACT OF PHYSICAL EXERCISE AND INTELLIGENT SPORTS EVALUATION USING DEEP LEARNING APPROACH UNDER THE MEDIATING ROLE OF SOCIAL SUPPORT

Ying Yan, Jamalsafri Saibon
article en

Abstract

This study develops an intelligent sports evaluation system based on deep learning, designed to accurately recognize and count physical exercise movements. It also investigates the impact of physical activity on college students’ mental health, with a focus on the mediating role of social support. The system consists of three core modules: posture recognition, movement counting, and psychological prediction. The posture recognition module employs the Spatial Temporal Graph Convolutional Network (ST-GCN) and its improved variants to analyze skeletal motion. The movement counting module evaluates the accuracy of counting various sports actions using a self-constructed dataset. The psychological prediction module examines how social support mediates the relationship between physical exercise and mental health through an experimental design. Results indicate that the ST-GCN model achieves 93.7% accuracy and a 93.5% F1-score in posture recognition, demonstrating an effective balance between performance and computational efficiency. In movement counting, both missed detection and false detection rates remain low, with recognition rates reaching 95.7% for standard actions and 87.3% for non-standard actions. The psychological prediction module shows that social support significantly mediates the effect of physical exercise on mental health, accounting for 47.4% of the total effect. Overall, the intelligent sports evaluation system performs strongly in both posture recognition and movement counting, providing precise support for physical education and training. The study also confirms the crucial mediating role of social support in the relationship between physical activity and mental health among college students. These findings offer a theoretical foundation for improving physical education and student health management in higher education and hold practical value for application in university physical education programs, athletic training, and mental health interventions.

Journal of Mechanics in Medicine and Biology
Universiti Sains Malaysia (MY), Hunan City University (CN)
Openalex Percentile: Top 60%
Diverse Approaches in Healthcare and Education Studies
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