Research on injury prevention strategies of biomechanical analysis in physical education teaching
Abstract
Physical education instruction has several issues on injuries as a result of interruptions to the learning, participation, or physical activity of the students. Existing strategies concern the risk prevention and warm-up activities without addressing personal characteristics and anatomical and kinematic prerequisites that underlie the occurrence of injury. The biomechanical evaluation of human motion can determine factors such as joint stresses, muscle loads, and motion patterns. This proceeding strives at eradicating the aspects of biomechanics in expanding the protective measures of injury prevention in physical education teaching. Beside the motion capture system, the force plate measurement and the electromyography (EMG) data movement patterns and joint load are measured biomechanically as accurately as possible. The accumulative data have to pass through cleaning and standardization steps to provide a certain level of reliability. In this case, the use of Fast Fourier Transform (FFT) extracts features of the movements relating to the frequency domain to undergo further analysis. Subsequently, an efficient Earthworm Optimized Graph Neural network (EEO-GNN) is employed to identify injury risk elements through modeling complex biomechanical relationships and patterns. The EEO-GNN model efficiently predicted ability injury hotspots by analyzing joint stresses, muscle activation, and motion irregularities. It is surpassing previous approaches in terms of F1-score (96.2%), recall (95.2%), accuracy (96%), and precision (95%). It underscores the ability to integrate superior biomechanical analysis and deep learning procedures to enhance injury prevention, enhance motion mechanics, and foster safer and greater effective physical education environments.
References
1. Evans RR, Sims SK. Health and physical education for elementary classroom teachers: An integrated approach. Human Kinetics; 2022.
2. Zhao F. The Application of Sports Biomechanics in Sports Injury Prevention and Rehabilitation. Frontiers in Sport Research. 2024; 6(3). doi: 10.25236/fsr.2024.060320
3. Prieto-González P, Martínez-Castillo JL, Fernández-Galván LM, et al. Epidemiology of Sports-Related Injuries and Associated Risk Factors in Adolescent Athletes: An Injury Surveillance. International Journal of Environmental Research and Public Health. 2021; 18(9): 4857. doi: 10.3390/ijerph18094857
4. Bonilla DA, Cardozo LA, Vélez-Gutiérrez JM, et al. Exercise selection and common injuries in fitness centers: a systematic integrative review and practical recommendations. International journal of environmental research and public health. 2022; 19(19): 12710. doi: 10.3390/ijerph191912710
5. Prentice WE. Restoring Range of Motion and Improving Flexibility. Rehabilitation Techniques for Sports Medicine and Athletic Training; 2024. doi: 10.4324/9781003526308-10
6. Shan G. Exploring the intersection of equipment design and human physical ability: Leveraging biomechanics, ergonomics/anthropometry, and wearable technology for enhancing human physical performance. Advanced Design Research. 2023; 1(1): 7-11. doi: 10.1016/j.ijadr.2023.04.001
7. Rebelo A, Martinho DV, Valente-dos-Santos J, et al. From data to action: a scoping review of wearable technologies and biomechanical assessments informing injury prevention strategies in sport. BMC Sports Science, Medicine and Rehabilitation. 2023; 15(1). doi: 10.1186/s13102-023-00783-4
8. Garcia MB, Yousef AMF, de Almeida RPP, et al. Teaching physical fitness and exercise using computer-assisted instruction: A School-based public health intervention. In: Handbook of research on instructional technologies in health education and allied disciplines. IGI Global; 2023. doi: 10.4018/978-1-6684-7164-7.ch008
9. Leilei W, Rajendiran S, Gayathri K. An emergency response system created to combat injuries during physical education training in a university using deep learning. The Electronic Library. 2020; 39(4): 505-525. doi: 10.1108/el-07-2020-0175
10. Cui J, Du H, Wu X. Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices. Preventive Medicine. 2023; 173: 107589. doi: 10.1016/j.ypmed.2023.107589
11. Mishra N, De A, Nayak RK, et al. AI Applications in Sports Medicine. Global Innovations in Physical Education and Health; 2024. doi: 10.4018/979-8-3693-3952-7.ch013
12. Ouf MA, Amin RM, Sabry MM, et al. VISION-PF: A Computer Vision Approach to Injury Prevention in Physical Fitness. Proceedings of the 2024 International Telecommunications Conference (ITC-Egypt); 2024. doi: 10.1109/itc-egypt61547.2024.10620528
13. Xu T, Tang L. Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention. Frontiers in Neurorobotics. 2021; 14. doi: 10.3389/fnbot.2020.620378
14. Li L, Wei Y, Xiang S. Infrared thermal image monitoring based on artificial intelligence application in the prevention of sports injuries in aerobics: Computational thermal modeling. Thermal Science and Engineering Progress. 2025; 57: 103126. doi: 10.1016/j.tsep.2024.103126
15. Yang J, Meng C, Ling L. Prediction and simulation of wearable sensor devices for sports injury prevention based on BP neural network. Measurement: Sensors. 2024; 33: 101104. doi: 10.1016/j.measen.2024.101104
16. Xie J, Chen G, and Liu S. Intelligent badminton training robot in athlete injury prevention under machine learning. Frontiers in neurorobotics. 2021.
17. Wang M, Pei Z. Injury prevention and rehabilitation strategies in physical education: A machine learning-based approach using biomechanical characteristics. Molecular & Cellular Biomechanics. 2024; 21(2): 412. doi: 10.62617/mcb.v21i2.412
18. Cai W, Zhu J, Zhuang X. Combining Medical Images and Biomechanical Data in Sports Injury Prediction Models. Published online September 10, 2024. doi: 10.21203/rs.3.rs-4884511/v1
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