Optimizing physical education movements through biomechanical analysis: A new approach to reducing the risk of sports injuries
Abstract
Physical education is crucial for fostering student’s health, fitness, and lifetime active behaviors. However, using inappropriate movement skills during Physical activity may raise the risk of sports injuries. Biomechanical analysis is a scientific approach to studying the movement of students that focuses on the forces and mechanics of physical activity. The study’s goal is to develop physical education motions using biomechanical analysis and a deep learning (DL) method to reduce the risk of sports injury. This study proposed a novel turbulent flow of water-based adjustable long-short-term memory (TFW-ALSTM) to classify and predict the high risk of sports injuries. Using advanced motion capture data and biomechanical modeling techniques, the study identifies improper movement patterns that can lead to injury during common physical education activities. The data was preprocessed using normalization and Kalman filters to reduce noise from the data. Discrete wavelet transforms (DWT) to extract the features from preprocessed data. The system offers beneficial suggestions to enhance movement efficiency through biomechanical data analysis. Experimental results reveal that the suggested model achieves accuracy (98.2%), recall (97%), specificity (98.1%), and an F1-Score (98%), particularly in dynamic activities like running and leaping, reducing the risk of injury considerably to compare existing algorithms. The study emphasizes the significance of integrating biomechanical knowledge and prediction models to enhance injury prevention measures in physical education programs. This approach provides educators and coaches with a dependable and effective tool for ensuring safer and more efficient student engagement.
References
1. Towner, B.C., Broce, R.S., Battista, R.A. and Christiana, R.W., 2024. A forced shift: Effects and outcomes of online higher education physical activity courses. International Journal of Kinesiology in Higher Education, 8(1), pp.24-36.
2. Hao, Q., Choi, W.J. and Meng, J., 2023. A data mining-based analysis of cognitive intervention for college students’ sports health using Apriori algorithm. Soft Computing, 27(21), pp.16353-16371.
3. Cui, J., Du, H. and Wu, X., 2023. Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices. Preventive medicine, 173, p.107589.
4. dos Santos Duarte Junior, M.A., López-Gil, J.F., Caporal, G.C. and Mello, J.B., 2022. Benefits, risks, and possibilities of strength training in school Physical Education: a brief review. Sport Sciences for Health, pp.1-10.
5. Hughes, G. and Dai, B., 2023. The influence of decision making and divided attention on lower limb biomechanics associated with anterior cruciate ligament injury: a narrative review.Sports biomechanics,22(1), pp.30-45.
6. Hosseinimehr, S.H. and Salvati, F., 2024. Improving lower limb muscle strength according to a number of weeks of core stability exercises in female athletes with and without ACL injury. Sport Sciences for Health, pp.1-8.
7. Siedentop, D. and Van der Mars, H., 2022.Introduction to physical education, fitness, and sport. Human kinetics.
8. Mishra, N., Habal, B.G.M., Garcia, P.S. and Garcia, M.B., 2024, June. Harnessing an AI-Driven Analytics Model to Optimize Training and Treatment in Physical Education for Sports Injury Prevention. In Proceedings of the 2024 8th International Conference on Education and Multimedia Technology (pp. 309-315).
9. Pleša, J., Kozinc, Ž. and Šarabon, N., 2022. A brief review of selected biomechanical variables for sport performance monitoring and training optimization.Applied Mechanics,3(1), pp.144-159.
10. Roupa, I., da Silva, M.R., Marques, F., Gonçalves, S.B., Flores, P. and da Silva, M.T., 2022. On the modeling of biomechanical systems for human movement analysis: a narrative review.Archives of Computational Methods in Engineering,29(7), pp.4915-4958.
11. Li, S., Wang, C. and Wang, Y., 2024. Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence. Scientific Reports, 14(1), p.4788.
12. Yamasaki, T., 2023. Preventive strategies for cognitive decline and dementia: benefits of aerobic physical activity, especially open-skill exercise. Brain Sciences, 13(3), p.521.
13. Agbaje, A.O., 2023. Associations of accelerometer‐based sedentary time, light physical activity and moderate‐to‐vigorous physical activity with resting cardiac structure and function in adolescents according to sex, fat mass, lean mass, BMI, and hypertensive status. Scandinavian Journal of Medicine & Science in Sports, 33(8), pp.1399-1411.
14. Kaggle. Injury Prediction for Competitive Runners.Available online:https://www.kaggle.com/datasets/shashwatwork/injury-prediction-for-competitive-runners?select=day_approach_maskedID_timeseries.csv. (accessed on 2 June 2024).
15. She, H., 2023. Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training. Applied Mathematics and Nonlinear Sciences, 9(1).
16. Zhan, C., 2024. Application of artificial intelligence in the development of personalized sports injury rehabilitation plan. Molecular & Cellular Biomechanics, 21(1), pp.326-326.
Copyright (c) 2024 Xianqi Huo
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
