Application of artificial intelligence in the development of personalized sports injury rehabilitation plan
Abstract
Sports injury rehabilitation is a kind of physical treatment used to address musculoskeletal system disorders, injuries, and discomfort in patients of all ages. Sports rehabilitation promotes health and fitness, aids in injury recovery, and lessens pain through movement, exercise, and physical therapy. During a sports injury, rehabilitation has developed into a specialized profession that has gradually brought together sports physicians, sports physiotherapists, and orthopedic surgeons. Finding the best ways to minimize recovery time, avoid injuries, and enhance performance is crucial for sports athletes. The aim of this research is to establish a personalized sports injury rehabilitation evaluation system enabled by artificial intelligence (AI). In this study, a novel advanced penguin search optimized efficient random forest (APSO-ERF) has been proposed for sports injury athletics exercise rehabilitation. This study used exercise movement image data to develop personalized sports injury rehabilitation. The data was preprocessed using a Wiener filter for noise reduction and image restoration. Convolutional neural networks (CNN) are used to extrapolate top-level characteristics from images. The proposed method is used to evaluate physical rehabilitation by assessing patient performance during the completion of prescribed sports injury rehabilitation exercises. The proposed method is compared to other traditional algorithms. With 97.80% accuracy, 96.01% sensitivity, 97.90% specificity, 98.88% precision, 96.11% recall, and 97.50% F1-score, the APSO-ERF approach beats conventional algorithms in tailored sports injury rehabilitation. The result illustrated that the proposed method achieved high performance in the accuracy of sports injury athletics exercise rehabilitation.
References
1. Qazi A, Iqbal A. ExerAIde: AI-assisted Multimodal Diagnosis for Enhanced Sports Performance and Personalised Rehabilitation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2024. pp. 3430-3438.
2. Li Y, Li X. The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning. Frontiers in Public Health. 2022; 10. doi: 10.3389/fpubh.2022.917053
3. Kakavas G, Malliaropoulos N, Pruna R, et al. Artificial intelligence: a tool for sports trauma prediction. Injury. 2020; 51: S63-S65. doi: 10.1016/ j.injury.2019.08.033
4. de Leeuw AW, van der Zwaard S, van Baar R, et al. Personalized machine learning approach to injury monitoring in elite volleyball players. European Journal of Sport Science. 2022; 22(4):511-520. doi: 10.1080/17461391.2021. 1887369
5. Yang R, Yuan Q, Zhang W, et al. Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace. SLAS Technology. 2024; 29(4): 100162. doi: 10.1016/j.slast.2024.100162
6. Tariq A, Gill AY, Hussain HK. Evaluating the potential of artificial intelligence in orthopedic surgery for value-based healthcare. International Journal of Multidisciplinary Sciences and Arts. 2023; 2(1): 27-35. doi: 10.47709/ijmdsa. v2i1.2394
7. Rossi A, Pappalardo L, Cintia P. A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer. Sports. 2021; 10(1): 5. doi: 10.3390/sports10010005
8. Blasiak A, Khong J, Kee T. CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence. SLAS Technology. 2020; 25(2): 95-105. doi: 10.1177/2472630319890316
9. Salloum G, Tekli J. Automated and Personalized Nutrition Health Assessment, Recommendation, and Progress Evaluation using Fuzzy Reasoning. International Journal of Human-Computer Studies. 2021; 151: 102610. doi: 10.1016/j.ijhcs.2021.102610
10. Nahavandi D, Alizadehsani R, Khosravi A, et al. Application of artificial intelligence in wearable devices: Opportunities and challenges. Computer Methods and Programs in Biomedicine. 2022; 213: 106541. doi: 10.1016/j.cmpb.2021.106541
11. Katović D, Bronzin T, Horvat M, et al. The use of AI in Human Pose Estimation Applications in Kinesiology: Taxonomy of Algorithms, Models, and Evaluation Methods. In: Proceeding of the 2024 47th MIPRO ICT and Electronics Convention (MIPRO); 2024. pp. 1163-1166. doi: 10.1109/MIPRO 60963.2024.10569385
12. Den Hartigh RJ, Meerhoff LRA, Van Yperen NW, et al. Resilience in sports: a multidisciplinary, dynamic, and personalized perspective. International Review of Sport and Exercise Psychology. 2024; 17(1): 564-586. doi: 10.1080/1750984x.2022.2039749
13. Montull L, Slapšinskaitė-Dackevičienė A, Kiely J, et al. Integrative Proposals of Sports Monitoring: Subjective Outperforms Objective Monitoring. Sports Medicine - Open. 2022; 8(1). doi: 10.1186/s40798-022-00432-z
14. Taimoor N, Rehman S. Reliable and resilient AI and IoT-based personalized healthcare services: A survey. IEEE Access. 2021; 10: 535-563. doi: 10.1109/ACCE SS.2021.3137364
15. Jauhiainen S, Kauppi JP, Leppänen M, et al. New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes. International Journal of Sports Medicine. 2020; 42(02): 175-182. doi: 10.1055/a-1231-5304
16. Zhao Z, Arya A, Orji R, et al. Effects of a Personalized Fitness Recommender System Using Gamification and Continuous Player Modeling: System Design and Long-Term Validation Study. JMIR Serious Games. 2020; 8(4): e19968. doi: 10.2196/19968
17. Thurzo A, Kurilová V, Varga I. Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System. Healthcare. 2021; 9(12): 1695. doi: 10.3390/healthcare9121695
18. Migliaccio GM, Padulo J, Russo L. The Impact of Wearable Technologies on Marginal Gains in Sports Performance: An Integrative Overview on Advances in Sports, Exercise, and Health. Applied Sciences. 2024; 14(15): 6649. doi: 10.3390/app 14156649
19. Burford KG, Itzkowitz NG, Ortega AG, et al. Use of Generative AI to Identify Helmet Status Among Patients with Micromobility-Related Injuries from Unstructured Clinical Notes. JAMA Network Open. 2024; 7(8): e2425981. doi: 10.1001/jamanetworkopen.2024.25981
20. Huihui X. Machine Vision Technology Based on Wireless Sensor Network Data Analysis for Monitoring Injury Prevention Data in Yoga Sports. Mobile Networks and Applications. 2024. doi: 10.1007/s11036-024-02374-0
21. Haick H, Tang N. Artificial Intelligence in Medical Sensors for Clinical Decisions. ACS Nano. 2021; 15(3): 3557-3567. doi: 10.1021/acsnano.1c00085
22. Navandar RK, Hasan SH, Jadhav N, et al. Modernizing sports is an intelligent strategy for entertainment through the Internet of Things in sports. Entertainment Computing. 2025; 52: 100804. doi: 10.1016/ j.entcom.2024.100804
23. Zhihong Y. Integrating Technology and Personalized Approaches in Sports Rehabilitation: Enhancing Performance and Preventing Sports Injuries. International Journal of Scientific and Management Research. 2023; 6(7):16-29. doi: 10.37502/ IJSMR.2023.6702
24. Dergaa I, Ben Saad H, El Omri A, et al. Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model. Biology of Sport. 2024; 41(2): 221-241. doi: 10.5114/biolsport.2024.133661
25. McBee JC, Han DY, Liu L, et al. Interdisciplinary inquiry via PanelGPT: application to explore chatbot application in sports rehabilitation. MedRxiv. 2023. doi: 10.1101%2f2023.07.23.23292452
26. Meng L, Qiao E. Analysis and design of dual-feature fusion neural network for sports injury estimation model. Neural Computing and Applications. 2021; 35(20): 14627-14639. doi: 10.1007/s00521-021-06151-y
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