Application of AI technology in muscle injury prevention in public sports basketball tests in colleges and universities
Abstract
AI public sports teaching technology is applied to daily indoor or outdoor fixed sports tests, ability assessments, and sports classroom teaching scenarios. It provides automated voice broadcast guidance, sports video AI intelligent engine automatic analysis, real-time detection of illegal actions, and intelligent real-time output of sports test results; Through AI intelligent guidance, the whole process of basketball test sports analysis and training suggestions are realized, and students are encouraged to conduct self-evaluation, promote training through testing, and practice more frequently. It also promotes the normalization of sports testing/teaching in public physical education in colleges and universities through teaching demonstration video explanations and a combination of various classroom forms, accumulates campus physical test data, and assists public physical education teaching and testing in colleges and universities. AI technology has intelligent joint detection and marking, multi-subject tracking and recognition. It does not require any markers to be installed on the captured object, and can capture accurate motion through video. During the basketball test, if an abnormal situation occurs, the student needs to stop the test. AI technology uses motion capture and analysis systems to help students and teachers better understand the sports skills and techniques tested in basketball, thereby developing more effective sports teaching and training plans. Motion capture and analysis systems can be used to evaluate athletes’ gait and posture to help them improve their techniques and reduce the risk of injury.
References
1. Chen Junchi, Hao Wenting. Research on improving the effectiveness of college students’ basketball elective courses. Contemporary Sports Science and Technology. 2015(20):113-4.
2. Kang Sanlong. Research on the application of functional movement screening (FMS) in basketball. Sports and Technology. 2020.
3. Yang, Z. S. Theoretical construction and empirical research on injury prevention physical training for basketball players (Doctoral dissertation, Beijing: Beijing Sport University).
4. Jiang Jie. Investigation and analysis of sports injuries and treatment methods among college basketball enthusiasts: A case study of students from Jiangsu Normal University. Sports World: Academic Edition. 2014(10):138-40.
5. Li Lu. On the correction methods of common mistakes in basketball teaching and training. New Curriculum: Educational Studies. 2011(7):104-.
6. He Runqiao. A brief discussion on the teaching of basketball in public physical education courses in colleges and universities. Jingwu. 2012(2):31-2.
7. Chen Zhixian. Exploration of teaching methods of basketball shooting techniques in public physical education in colleges and universities. Sports Science Research. 2007;11(1):97-9.
8. Li Shufang. Research on innovative teaching mode of public physical education courses in colleges and universities. Journal of Henan University: Social Science Edition. 2004;44(6):151-3.
9. Li B, Xu X. Application of artificial intelligence in basketball sport. Journal of Education, Health and Sport. 2021 Jul 8;11(7):54-67.
10. Liu Z. Application of artificial intelligence technology in basketball games. InIOP Conference Series: Materials Science and Engineering 2020 Feb 1 (Vol. 750, No. 1, p. 012093). IOP Publishing.
11. Cheng Y, Liang X, Xu Y, Kuang X. Artificial intelligence technology in basketball training action recognition. Frontiers in Neurorobotics. 2022 Jun 27;16:819784.
12. Yang Z. Research on basketball players’ training strategy based on artificial intelligence technology. InJournal of Physics: Conference Series 2020 Oct 1 (Vol. 1648, No. 4, p. 042057). IOP Publishing.
13. Yu S, Liu J. Automatic detection of image features in basketball shooting teaching based on artificial intelligence. Ine-Learning, e-Education, and Online Training: 6th EAI International Conference, eLEOT 2020, Changsha, China, June 20-21, 2020, Proceedings, Part II 6 2020 (pp. 165-175). Springer International Publishing.
14. Zhao Y, Xie J. Artificial intelligence, computer assisted instruction in basketball training. International Journal of Information. 2017;9(1):7.
15. Tsai WL, Su LW, Ko TY, Pan TY, Hu MC. Feasibility study on using AI and VR for decision-making training of basketball players. IEEE Transactions on Learning Technologies. 2021 Dec 1;14(6):754-62.
16. Agbozo E, PANDYA K, JOVANOVIC P, SUVOROVA EA. A comprehensive overview of artificial intelligence applications in basketball. Journal of Physical Education & Sport. 2024 Jan 1;24(1).
17. Musat CL, Mereuta C, Nechita A, Tutunaru D, Voipan AE, Voipan D, Mereuta E, Gurau TV, Gurău G, Nechita LC. Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods. Diagnostics. 2024 Nov 10;14(22):2516.
18. Ang Z. Application of IoT technology based on neural networks in basketball training motion capture and injury prevention. Preventive Medicine. 2023 Oct 1;175:107660.
19. Zhang H. Analysis of College Basketball Injuries: Implications for Healthcare and Patient Well-being. Journal of Commercial Biotechnology. 2023 Apr 1;28(2).
20. Han X. DECISION SUPPORT FOR BASKETBALL ATHLETE INJURY PROTECTION BASED ON PATTERN RECOGNITION. Revista multidisciplinar de las Ciencias del Deporte. 2024 Mar 2;24(95).
21. Sharma S, Raval MS, Kaya T, Divakaran S. Interpretable Machine Learning in Athletics for Injury Risk Prediction. InExplainable AI in Healthcare 2023 Jul 17 (pp. 255-278). Chapman and Hall/CRC.
22. Yin L, Alejandro MU, Li S. Application of multi-target positioning algorithm in basketball teaching system and injury prevention. Preventive Medicine. 2023 Aug 1;173:107612.
23. Muthaiah U, Sonai V, Kumar MR, Kumar S. Real-Time Basketball Scoring and Player Performance Tracking System Utilizing AI-Powered Court Vision Technology. In2023 Seventh International Conference on Image Information Processing (ICIIP) 2023 Nov 22 (pp. 564-569). IEEE.
24. Schulc A, Leite CB, Csákvári M, Lattermann L, Zgoda MF, Farina EM, Lattermann C, Tősér Z, Merkely G. Identifying anterior cruciate ligament injuries through automated video analysis of in-game motion patterns. Orthopaedic journal of sports medicine. 2024 Mar;12(3):23259671231221579.
25. Chen Z, Dai X. Utilizing AI and IoT Technologies for Identifying Risk Factors in Sports. Heliyon. 2024 Jun 4.
26. Sampedro AC. ARTIFICIAL INTELLIGENCE IN SPORTS: A REVOLUTIONARY TECHNOLOGY THAT MUST BE HANDLED WITH CARE. Movimento. 2024 Dec 20;30:e30054.
Copyright (c) 2025 Author(s)
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
