Research on the prevention strategies of sports injuries in physical education teaching through sports biomechanics analysis
Abstract
Sports activities induce significant changes in cell mechanics. Physical exercise prompts molecular adaptations in muscles, and analyzing the biomechanics of specific sports is crucial. Sports injuries, commonly occurring during exercise, often stem from overuse, crashes, or excessive forces. The physical and psychological rigors of sports and intense competitions heighten the risk of damage. For instance, hamstring strain injury is prevalent among football players. Understanding the biomechanics underlying such injuries is essential. This research focuses on gathering biomechanical data from physical education exercises, including joint angles, forces, velocities, and muscle activations. By preprocessing this data through cleaning and normalization, we aim to decipher the molecular and cellular level changes. Maximal hamstring flexibility and muscular tightness, identified as key factors, can provide insights into muscle cell mechanics and potential injury prevention. A novel Intelligent Flamingo Optimized Residual Network50 (IFO-ResNet50) is proposed. Through biomechanics analysis in physical education teaching, it targets the prevention of football muscle injuries. The method’s effectiveness is evaluated in terms of accuracy (98.1%), recall (98.4%), F1-Score (98.2%), AUC (98.5%), and precision (98.7%) in comparison to existing algorithms. This research not only aids in identifying the physiological and biomechanical changes at the cell or molecular level due to sports but also offers practical strategies for physical education teachers. By reducing injury risks, it can enhance student safety and performance in school sports programs, thereby contributing to a more comprehensive understanding of the relationship between sports and cell mechanics.
References
1. Ji, S., Ghajari, M., Mao, H., Kraft, R.H., Hajiaghamemar, M., Panzer, M.B., Willinger, R., Gilchrist, M.D., Kleiven, S. and Stitzel, J.D., 2022. Use of brain biomechanical models for monitoring impact exposure in contact sports. Annals of Biomedical Engineering, 50(11), pp.1389-1408.https://doi.org/10.1007/s10439-022-02999-w
2. Di Paolo, S., Zaffagnini, S., Pizza, N., Grassi, A. and Bragonzoni, L., 2021. Poor motor coordination elicits altered lower limb biomechanics in young football (soccer) players: implications for injury prevention through wearable sensors. Sensors, 21(13), p.4371.https://doi.org/10.3390/s21134371
3. Verheul, J., Nedergaard, N.J., Vanrenterghem, J. and Robinson, M.A., 2020. Measuring biomechanical loads in team sports–from lab to field. Science and Medicine in Football, 4(3), pp.246-252 https://doi.org/10.1080/24733938.2019.1709654
4. Yung, K.K., Ardern, C.L., Serpiello, F.R. and Robertson, S., 2022. Characteristics of complex systems in sports injury rehabilitation: examples and implications for practice. Sports medicine-open, 8(1), p.24.https://doi.org/10.1186/s40798-021-00405-8
5. Xu, D., Zhou, H., Quan, W., Jiang, X., Liang, M., Li, S., Ugbolue, U.C., Baker, J.S., Gusztav, F., Ma, X. and Chen, L., 2024. A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis. Gait & Posture, 107, pp.293-305.https://doi.org/10.1186/s40798-021-00405-8
6. Thomas ACQ, Stead CA, Burniston JG, Phillips SM. Exercise-specific adaptations in human skeletal muscle: Molecular mechanisms of making muscles fit and mighty. Free Radic Biol Med. 2024;223:341-356. doi:10.1016/j.freeradbiomed.2024.08.010
7. Furrer R, Handschin C. Molecular aspects of the exercise response and training adaptation in skeletal muscle. Free Radic Biol Med. 2024;223:53-68. doi:10.1016/j.freeradbiomed.2024.07.026
8. McGee SL, Hargreaves M. Exercise adaptations: molecular mechanisms and potential targets for therapeutic benefit. Nat Rev Endocrinol. 2020;16(9):495-505. doi:10.1038/s41574-020-0377-1
9. Barbosa, T.M., Barbosa, A.C., Simbaña Escobar, D., Mullen, G.J., Cossor, J.M., Hodierne, R., Arellano, R. and Mason, B.R., 2023. The role of the biomechanics analyst in swimming training and competition analysis. Sports Biomechanics, 22(12), pp.1734-1751.https://doi.org/10.1080/14763141.2021.1960417
10. Yu, J.E., 2022. Exploration of educational possibilities by four metaverse types in physical education. Technologies, 10(5), p.104.https://doi.org/10.1080/14763141.2021.1960417
11. Pranoto, B.E. and Suprayogi, S., 2020. A need analysis of ESP for physical education students in Indonesia. Premise: Journal of English Education, 9(1), pp.94-110.https://doi.org/10.3390/technologies10050104
12. Ilyosovich, M.S., 2023. Improving physical education of students with fatigued health. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE & INTERDISCIPLINARY RESEARCH ISSN: 2277-3630 Impact factor: 8.036, 12(10), pp.32-35.https://doi.org/10.1016/j.sbspro.2010.03.136
13. Varea, V., Gonzalez-Calvo, G. and García-Monge, A., 2022. Exploring the changes in physical education in the age of Covid-19. Physical Education and Sport Pedagogy, 27(1), pp.32-42.https://doi.org/10.1080/14763141.2021.1960417
14. Dexqonov, B., 2023. Preparation of future physical education teachers for innovative activities. Models and methods in modern science, 2(12), pp.82-86.https://doi.org/10.1080/13573322.2020.1821182
15. Jeong, H.C. and So, W.Y., 2020. Difficulties of online physical education classes in middle and high school and an efficient operation plan to address them. International Journal of Environmental Research and Public Health, 17(19), p.7279.https://doi.org/10.3390/ijerph17197279
16. Trasolini, N.A., Nicholson, K.F., Mylott, J., Bullock, G.S., Hulburt, T.C. and Waterman, B.R., 2022. Biomechanical analysis of the throwing athlete and its impact on return to sport. Arthroscopy, Sports Medicine, and Rehabilitation, 4(1), pp.e83-e91. https://doi.org/10.1016/j.asmr.2021.09.027
17. Zadeh, A., Taylor, D., Bertsos, M., Tillman, T., Nosoudi, N. and Bruce, S., 2021. Predicting sports injuries with wearable technology and data analysis. Information Systems Frontiers, 23, pp.1023-1037 https://doi.org/10.1007/s10796-020-10018-3
18. Lloyd, D., 2021. The future of in-field sports biomechanics: Wearables plus modeling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries. Sports Biomechanics, pp.1-29 https://doi.org/10.1080/14763141.2021.1959947
19. Alsaeed R, Hassn Y, Alaboudi W, Aldywan L. Biomechanical analytical research of some obstacles affecting the development of football players. International Journal of Physical Education, Sports and Health. 2023;10(3):342-6. https://doi.org/10.22271/kheljournal.2023.v10.i3e.2967
20. Huifeng, W., Shankar, A. and Vivekananda, G.N., 2021. Modeling and simulation of sprinters’ health promotion strategy based on sports biomechanics. Connection Science, 33(4), pp.1028-1046 https://doi.org/10.1080/09540091.2020.1807467
21. Ba, H., 2020. Medical sports rehabilitation deep learning system of sports injury based on MRI image analysis. Journal of Medical Imaging and Health Informatics, 10(5), pp.1091-1097.https://doi.org/10.1166/jmihi.2020.2892
22. Yeadon, M.R. and Pain, M.T.G., 2023. Fifty years of performance‐related sports biomechanics research. Journal of Biomechanics, 155, p.111666.https://doi.org/10.1016/j.jbiomech.2023.111666
23. Yan, S., Chen, J. and Huang, H., 2022. Biomechanical Analysis of Martial Arts Movements Based on Improved PSO Optimized Neural Network. Mobile Information Systems, 2022(1), p.8189426.https://doi.org/10.1155/2022/8189426
24. Taborri, J., Keogh, J., Kos, A., Santuz, A., Umek, A., Urbanczyk, C., van der Kruk, E. and Rossi, S., 2020. Sport biomechanics applications using inertial, force, and EMG sensors: A literature overview. Applied bionics and biomechanics, 2020(1), p.2041549.https://doi.org/10.1155/2020/2041549
25. Fonseca, S.T., Souza, T.R., Verhagen, E., Van Emmerik, R., Bittencourt, N.F., Mendonça, L.D., Andrade, A.G., Resende, R.A. and Ocarino, J.M., 2020. Sports injury forecasting and complexity: a synergetic approach. Sports medicine, 50, pp.1757-1770.https://doi.org/10.1007/s40279-020-01326-4
26. Li, C. and Li, Y., 2020. Feasibility analysis of VR technology in physical education and sports training. IEEE Access.https://doi.org/10.1109/ACCESS.2020.3020842
27. Kozin, S., Cretu, M., Kozina, Z., Chernozub, A., Ryepko, O., Shepelenko, T., Sobko, I. and Oleksiuk, M., 2021. Application of closed kinematic chain exercises with eccentric and strength exercises for the shoulder injuries prevention in student rock climbers: a randomized controlled trial. Acta of bioengineering and biomechanics, 23(2), pp.159-168.https://doi.org/10.37190/abb-01828-2021-01
28. Ramírez, C., 2024. BIOMECHANICAL ANALYSIS OF RUNNING TECHNIQUES: IMPLICATIONS FOR INJURY PREVENTION AND PERFORMANCE. Revistamultidisciplinar de las Ciencias del Deporte, 24(97).
29. Yang, J., Meng, C. and Ling, L., 2024. Prediction and simulation of wearable sensor devices for sports injury prevention based on BP neural network. Measurement: Sensors, 33, p.101104. https://doi.org/10.1016/j.measen.2024.101104
30. Zhao, J. and Li, G., 2023. A combined deep neural network and semi-supervised clustering method for sports injury risk prediction. Alexandria Engineering Journal, 80, pp.191-201.https://doi.org/10.1016/j.aej.2023.08.048
31. Zhang, H., Chai, J. and Li, C., 2024. On innovative strategies of youth sports teaching and training based on the Internet of things and artificial intelligence technology from the perspective of humanism. Learning and Motivation, 86, p.101969.https://doi.org/10.1016/j.lmot.2024.101969
32. Hughes DC, Ellefsen S, Baar K. Adaptations to Endurance and Strength Training. Cold Spring Harb Perspect Med. 2018;8(6):a029769. Published 2018 Jun 1. doi:10.1101/cshperspect.a029769
33. Egan B, Zierath JR. Exercise metabolism and the molecular regulation of skeletal muscle adaptation. Cell Metab. 2013;17(2):162-184. doi:10.1016/j.cmet.2012.12.012
34. Tidball JG. Mechanisms of muscle injury, repair, and regeneration. Compr Physiol. 2011;1(4):2029-2062. doi:10.1002/cphy.c100092
35. Michele DE. Mechanisms of skeletal muscle repair and regeneration in health and disease. FEBS J. 2022;289(21):6460-6462. doi:10.1111/febs.16577
36. Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T., 2022. Machine learning for understanding and predicting injuries in football. Sports Medicine-Open, 8(1), p.73.https://doi.org/10.1186/s40798-022-00465-4
37. Franssens, G., 2021. Injury prediction in professional football using a two-model approach.
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