Prediction and treatment of joint injuries in basketball training based on improved regression algorithm from the perspective of sports biomechanics
Abstract
With the increasing popularity of basketball, especially in collegiate competitions like the University Basketball Super League, the sport has become a significant part of student life. The intensity of basketball training and competition has risen, necessitating athletes to have enhanced physical capabilities to meet modern demands. This heightened physical confrontation often leads to various injuries, with joint injuries being particularly common and impactful. This study integrates sports biomechanics with machine learning to address the prediction and treatment of joint injuries in basketball training. By employing an improved regression algorithm and leveraging high-performance computing, we have experimentally analyzed the prediction of joint injuries and proposed effective solutions. Our results indicate that the difference between the highest and lowest predicted residual values for the Back Propagation (BP) model was 0.92, and for the Extreme Learning Machine (ELM) regression model was 0.87. Notably, the improved ELM regression model demonstrated a reduced residual difference of 0.43. This improvement suggests that the enhanced ELM regression model offers superior prediction accuracy for joint injuries in basketball training and provides more comprehensive monitoring of athletes’ physical health, thereby supporting the advancement of basketball training programs.
References
1. Li B, Xu X. Application of Artificial Intelligence in Basketball Sport. Journal of Education, Health and Sport. 2021; 11(7): 54-67. doi: 10.12775/jehs.2021.11.07.005
2. Shimozaki K, Nakase J, Takata Y, et al. Greater body mass index and hip abduction muscle strength predict noncontact anterior cruciate ligament injury in female Japanese high school basketball players. Knee Surgery, Sports Traumatology, Arthroscopy. 2018; 26(10): 3004-3011. doi: 10.1007/s00167-018-4888-4
3. Owoeye OBA, Palacios-Derflingher LM, Emery CA. Prevention of Ankle Sprain Injuries in Youth Soccer and Basketball: Effectiveness of a Neuromuscular Training Program and Examining Risk Factors. Clinical Journal of Sport Medicine. 2018; 28(4): 325-331. doi: 10.1097/jsm.0000000000000462
4. Wu H, Wang L. Analysis of lower limb high-risk injury factors of patellar tendon enthesis of basketball players based on deep learning and big data. The Journal of Supercomputing. 2021; 78(3): 4467-4486. doi: 10.1007/s11227-021-04029-3
5. Van Eetvelde H, Mendonça LD, Ley C, et al. Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of Experimental Orthopaedics. 2021; 8(1). doi: 10.1186/s40634-021-00346-x
6. Bond CW, Dorman JC, Odney TO, et al. Evaluation of the Functional Movement Screen and a Novel Basketball Mobility Test as an Injury Prediction Tool for Collegiate Basketball Players. Journal of Strength and Conditioning Research. 2019; 33(6): 1589-1600. doi: 10.1519/jsc.0000000000001944
7. Zadeh A, Taylor D, Bertsos M, et al. Predicting Sports Injuries with Wearable Technology and Data Analysis. Information Systems Frontiers. 2020; 23(4): 1023-1037. doi: 10.1007/s10796-020-10018-3
8. Glazier PS, Mehdizadeh S. Challenging Conventional Paradigms in Applied Sports Biomechanics Research. Sports Medicine. 2018; 49(2): 171-176. doi: 10.1007/s40279-018-1030-1
9. Huifeng W, Shankar A, Vivekananda GN. Modelling and simulation of sprinters’ health promotion strategy based on sports biomechanics. Connection Science. 2020; 33(4): 1028-1046. doi: 10.1080/09540091.2020.1807467
10. Warmenhoven J, Cobley S, Draper C, et al. Bivariate functional principal components analysis: considerations for use with multivariate movement signatures in sports biomechanics. Sports Biomechanics. 2017; 18(1): 10-27. doi: 10.1080/14763141.2017.1384050
11. Trowell D, Phillips E, Saunders P, et al. The relationship between performance and biomechanics in middle-distance runners. Sports Biomechanics. 2019; 20(8): 974-984. doi: 10.1080/14763141.2019.1630478
12. Hughes GTG, Camomilla V, Vanwanseele B, et al. Novel technology in sports biomechanics: some words of caution. Sports Biomechanics. 2021; 23(4): 393-401. doi: 10.1080/14763141.2020.1869453
13. van Oeveren BT, de Ruiter CJ, Beek PJ, et al. The biomechanics of running and running styles: a synthesis. Sports Biomechanics. 2021; 23(4): 516-554. doi: 10.1080/14763141.2021.1873411
14. Claudino JG, Capanema D de O, de Souza TV, et al. Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review. Sports Medicine - Open. 2019; 5(1). doi: 10.1186/s40798-019-0202-3
15. McIlwraith CW, Kawcak CE, Frisbie DD, et al. Biomarkers for equine joint injury and osteoarthritis. Journal of Orthopaedic Research. 2018; 36(3): 823-831. doi: 10.1002/jor.23738
16. Orozco GA, Tanska P, Florea C, et al. A novel mechanobiological model can predict how physiologically relevant dynamic loading causes proteoglycan loss in mechanically injured articular cartilage. Scientific Reports. 2018; 8(1). doi: 10.1038/s41598-018-33759-3
17. Visser E, Gosens T, Den Oudsten BL, et al. The course, prediction, and treatment of acute and posttraumatic stress in trauma patients. Journal of Trauma and Acute Care Surgery. 2017; 82(6): 1158-1183. doi: 10.1097/ta.0000000000001447
18. Neuman P, Dahlberg LE, Englund M, et al. Concentrations of synovial fluid biomarkers and the prediction of knee osteoarthritis 16 years after anterior cruciate ligament injury. Osteoarthritis and Cartilage. 2017; 25(4): 492-498. doi: 10.1016/j.joca.2016.09.008
19. Tomašev N, Glorot X, Rae JW, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019; 572(7767): 116-119. doi: 10.1038/s41586-019-1390-1
20. Ivarsson A, Johnson U, Andersen MB. Psychosocial factors and sport injuries: meta-analyses for prediction and prevention. Sports medicine. 2017; 47(2): 353-365.
21. Weatherford BM, Anderson JG, Bohay DR. Management of Tarsometatarsal Joint Injuries. Journal of the American Academy of Orthopaedic Surgeons. 2017; 25(7): 469-479. doi: 10.5435/jaaos-d-15-00556
22. Frank RM, Cotter EJ, Leroux TS, et al. Acromioclavicular Joint Injuries: Evidence-based Treatment. Journal of the American Academy of Orthopaedic Surgeons. 2019; 27(17): e775-e788. doi: 10.5435/jaaos-d-17-00105
23. Sato M, Yamato M, Mitani G, et al. Combined surgery and chondrocyte cell-sheet transplantation improves clinical and structural outcomes in knee osteoarthritis. npj Regenerative Medicine. 2019; 4(1). doi: 10.1038/s41536-019-0069-4
Copyright (c) 2024 Yan Bai, Xiao Yang
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
