Application of AI in table tennis technique optimization based on biomechanics
Abstract
The application of AI in table tennis technology is an innovative way for table tennis players to train in daily life, aiming to improve the players’ technical level and competition results. Athletes only need to upload their training videos, and the AI system can conduct in-depth analysis of the videos, control strength, and evaluate various technical performances.AI technology can accurately identify and analyze key mechanical control actions such as the athlete’s serve power control, receiving and kicking skills, and movement strategies from a biomechanical perspective. Through precise data analysis, AI technical analysis will generate detailed reports to help athletes understand their strengths and areas for improvement, and provide specific optimization suggestions so that players can continue to improve their skills, especially in power control and center of gravity adjustment. In addition, AI applied to table tennis technology will also provide more targeted training plans based on the athlete’s personal strength and training goals. This targeted strength and ball skills training can help athletes focus on improving their special skills and achieve their goals more effectively. In the journey of AI table tennis technology, we will continue to challenge ourselves and achieve higher achievements.
References
1. Zhang Z. Research on physical training methods in table tennis training. Sports Fashion; 2021.
2. Kang K. Analysis of physical training methods in college table tennis training. In: Proceedings of the Industry and Technology Forum; 2021.
3. Li Y. Analysis of physical training methods in table tennis training. Athletics; 2021.
4. He Z, Yang Z, Dong L. Design and implementation of intelligent table tennis training system based on visual sensor fusion technology. In: Proceedings of the Abstracts of the 12th National Sports Science Conference—Poster Exchange (Sports Engineering Branch); 2022.
5. Cai G. Research on the application of information technology in table tennis training. Contemporary Sports Science and Technology; 2021.
6. Nie Z, Zhu B, Li Y, et al. Practical research on table tennis courses in ordinary universities based on hybrid teaching mode. Physical Science. 2024; 4(5): 24–27.
7. Yang L. Analysis of the importance of strength training in table tennis. Contemporary Sports Science and Technology; 2021.
8. Cao B, Zhou X. Comparative study on the penhold and horizontal racket playing styles of high-level male table tennis players in my country: Taking the 2020 National Table Tennis Championships as an example. Fujian Sports Science and Technology; 2022.
9. Wu CH, Wu TC, Lin WB. Exploration of applying pose estimation techniques in table tennis. Applied Sciences. 2023; 13(3): 1896.
10. Cao Y, Peng Y, Shen Z, et al. Application of Tactics in Technical and Tactical Analysis of Table Tennis Mixed Doubles Based on Artificial Intelligence Graph Theory Model. Journal of Environmental and Public Health. 2022; 2022(1): 6543953.
11. Liu Q, Ding H. Application of table tennis ball trajectory and rotation-oriented prediction algorithm using artificial intelligence. Frontiers in Neurorobotics. 2022; 16: 820028.
12. Kong X, Tanaka A. Ai table tennis: Methods and challenges. In: Proceedings of the 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE); 2021. pp. 837–838.
13. Wang X. Research and Practice on Intelligent Optimization Model of Table Tennis Tactics Based on Reinforcement Learning Algorithm. In: Proceedings of the International Conference on Artificial Intelligence for Society; 2024. pp. 447–456).
14. Zaferanieh A, Haghighi AH, Kakhak SA, et al. Effect of ballistic and power training on performance adaptations of élite table tennis players. Sport Sciences for Health. 2021; 17: 181–190.
15. Cui Y, Zhou C. Application of Internet of Things Artificial Intelligence and Knowledge Innovation System in Table Tennis Teaching and Training. Applied Bionics and Biomechanics. 2022; 2022(1): 7625626.
16. Ferrandez C, Marsan T, Poulet Y, et al. Physiology, biomechanics and injuries in table tennis: A systematic review. Science & Sports. 2021; 36(2): 95–104.
17. Lin HI, Yu Z, Huang YC. Ball tracking and trajectory prediction for table-tennis robots. Sensors. 2020; 20(2): 333. doi: 10.48550/arXiv.2408.03906
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