A study on Modeling and Simulation of Sports Training Injury Optimization from a Biomechanical Perspective
Abstract
With the development of artificial intelligence technology today, sports training is one of the important ways to keep people healthy. Reasonable sports training can keep people happy, and can also improve the physical quality of athletes. However, because the intensity of sports training is greater than the physical endurance, it may lead to physical injury. The sports training injury model can collect the physical data of athletes, and simulate whether the actions that athletes want to practice are standard and correct through the collected data. If there are errors in the simulated exercise actions, the wrong exercise actions can be corrected in time, so as to reduce the incidence of sports injury accidents. The construction of the experimental model and the calculation and analysis process of the construction data are very complex, and the existing simulation models are difficult to achieve this, which has a significant impact on the subsequent development of the simulation model. In view of this phenomenon, this paper, on the basis of the sports training injury model, combined with the neural network method, conducted an effective research on the construction of the sports training injury model, and inspected the performance of the model by testing the accuracy of the evaluation of the injury risk level, the degree of sports training injury, and the accuracy of the evaluation of the prediction results of the sports training injury model. The experimental data shows that the maximum accuracy of the sports training injury model in the horizontal and vertical directions was 94.43% and 95.26% respectively, and the maximum accuracy of the single injury degree and the composite injury degree was 48.68% and 55.01% respectively, which had high accuracy and operational efficiency, and can effectively avoid the occurrence of training injury.
References
1. Davis WA, Yagnik GP. In-Season Management of Acute and Subacute Sports Foot Injuries. Foot and Ankle Clinics. 2021; 26(1): 187-203. doi: 10.1016/j.fcl.2020.12.001
2. Zhu L. Computer Vision‐Driven Evaluation System for Assisted Decision‐Making in Sports Training. Wireless Communications and Mobile Computing. 2021; 2021(1). doi: 10.1155/2021/1865538
3. Krstić O, Stamatovic M. Physical treatment of sports knee injuries. Facta Universitatis, Series: Physical Education and Sport. 2018; 16(1): 211. doi: 10.22190/fupes171204019k
4. Lauersen JB, Andersen LB. Multi-faceted exercise programs versus strength training to prevent sports injuries. Journal of Xiangya Medicine. 2017; 2: 34-34. doi: 10.21037/jxym.2017.03.10
5. Kerr ZY, Zuckerman SL, Register-Mihalik JK, et al. Estimating Concussion Incidence Using Sports Injury Surveillance Systems. Neurologic Clinics. 2017; 35(3): 409-434. doi: 10.1016/j.ncl.2017.03.001
6. Taddei UT, Matias AB, Duarte M, et al. Foot Core Training to Prevent Running-Related Injuries: A Survival Analysis of a Single-Blind, Randomized Controlled Trial. The American Journal of Sports Medicine. 2020; 48(14): 3610-3619. doi: 10.1177/0363546520969205
7. Kerr ZY, Wilkerson GB, Caswell SV, et al. The First Decade of Web-Based Sports Injury Surveillance: Descriptive Epidemiology of Injuries in United States High School Football (2005–2006 Through 2013–2014) and National Collegiate Athletic Association Football (2004–2005 Through 2013–2014). Journal of Athletic Training. 2018; 53(8): 738-751. doi: 10.4085/1062-6050-144-17
8. Baker B, Lapierre S, Tanaka H. Role of Cross-training in Orthopaedic Injuries and Healthcare Burden in Masters Swimmers. International Journal of Sports Medicine. 2018; 40(01): 52-56. doi: 10.1055/a-0759-2063
9. Yue T, Zou Y. Online Teaching System of Sports Training Based on Mobile Multimedia Communication Platform. International Journal of Mobile Computing and Multimedia Communications. 2019; 10(1): 32-48. doi: 10.4018/ijmcmc.2019010103
10. Xu Y. A Sports Training Video Classification Model Based on Deep Learning. Scientific Programming. 2021; 2021: 1-11. doi: 10.1155/2021/7252896
11. Ge S. Research on the Factors of Basketball Injury in Physical Teaching based on Artificial Neural Network. Revista De La Facultad De Ingenieria. 2017; 32(3): 415-422.
12. Xu W, Xiong W, Shao Z, et al. Analysis of Effectiveness and Performance Prediction of Sports Flipped Classroom Teaching Based on Neural Networks. Scientific Programming. 2021; 2021: 1-7. doi: 10.1155/2021/5284457
13. Liu Y, Ji Y. Target recognition of sport athletes based on deep learning and convolutional neural network. Journal of Intelligent & Fuzzy Systems. 2021; 40(2): 2253-2263. doi: 10.3233/jifs-189223
14. Mikalonytė R, Kemerytė - Riaubienė E. Causes of Sports Injuries and Preventive Measures of Physical Active Students / Studentų sportinių traumų priežastys ir naudojamos prevencinės priemonės. Sporto mokslas/Sport Science. 2017; 2(88): 68-73. doi: 10.15823/sm.2017.21
15. An KO, Lee KJ. Sports Injury Prevention and Functional Training: A Literature Review. The Asian Journal of Kinesiology. 2021; 23(1): 46-52. doi: 10.15758/ajk.2021.23.1.46
16. Song H, Han X, Montenegro-Marin CE, et al. Retracted article: Secure prediction and assessment of sports injuries using deep learning based convolutional neural network. Journal of Ambient Intelligence and Humanized Computing. 2021; 12(3): 3399-3410. doi: 10.1007/s12652-020-02560-4
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