Evaluation of the effect of artificial intelligence training equipment in physical training of table tennis players from a biomechanical perspective

  • Jun Zhang School of Sport Communication and Information Technology, Shandong Sport University, Jinan 250102, China
Keywords: functional screening; table tennis players; athletic ability; physical training; evaluation diagnosis
Article ID: 319

Abstract

In table tennis competitions, the movement data of table tennis players plays an important role in daily training. In recent years, artificial intelligence technology has been widely used in sports competitions. With the development of artificial intelligence, how to use artificial intelligence to analyze sports video to obtain sports data of athletes has become more and more important. This paper is to use artificial intelligence to research and analyze the athletes in the table tennis competition, and then obtain the sports data of the athletes. This has important guiding significance for athletes and coaches in their daily training. Table tennis is a widely developed sport in China, and as China’s “national ball”, table tennis has always been in a leading position in the world table tennis competitions. It is widely loved by the public for its simple and easy access to sports facilities, low site requirements and sports methods. In this study, mathematical statistics and experimental methods were used. Combined with the application of the functional sports screen function test, 20 table tennis players from Shandong Sport University were selected as the test objects, and their sports performance was tested. The total score of 10 first-level athletes was 162 points, and the average score was 16.2 points. The total score of the 10 second-level athletes was 144 points, and the average score was 14.4 points. The performance of the first-level athletes was significantly higher than that of the second-level athletes. It showed that the first-level athletes were generally better than the second-level athletes in all aspects.

References

1. Wang J, Zhao K, Deng D, et al. Tac-Simur: Tactic-based Simulative Visual Analytics of Table Tennis. IEEE Transactions on Visualization and Computer Graphics. 2020; 26(1): 407-417. doi: 10.1109/tvcg.2019.2934630

2. Hayashi I, Fujii M, Maeda T, et al. Extraction of Knowledge from the Topographic Attentive Mapping Network and its Application in Skill Analysis of Table Tennis. Journal of Human Kinetics. 2017; 55(1): 39-54. doi: 10.1515/hukin-2017-0005

3. Iino Y, Yoshioka S, Fukashiro S. Effect of Mechanical Properties of the Lower Limb Muscles on Muscular Effort during Table Tennis Forehand. ISBS Proceedings Archive. 2018; 36(1): 183-183.

4. 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. doi: 10.1016/j.scispo.2020.04.007

5. Li YM, Li B, Wang XX, et al. Application of energy cost in evaluating energy expenditure in multi-ball practice with table tennis players. Chinese journal of applied physiology. 2019; 35(4): 331-335.

6. Nancy JL. Expectation: Philosophy, Literature. Trans. by Robert Bononno. French Studies. 2018; 72(4): 633-633. doi: 10.1093/fs/kny180

7. Zhang Y, Awrejcewicz J, Goethel M, et al. A Comparison of Lower Limb Kinematics between Superior and Intermediate Players in Table Tennis Forehand Loop. ISBS Proceedings Archive. 2017; 35(1): 40-40.

8. Siener M, Hohmann A. Talent orientation: the impact of motor abilities on future success in table tennis. German Journal of Exercise and Sport Research. 2019; 49(3): 232-243. doi: 10.1007/s12662-019-00594-1

9. Iino Y, Yoshioka S, Fukashiro S. Uncontrolled Manifold Analysis of Joint Angle Variability During Table Tennis Forehand. ISBS Proceedings Archive. 2017; 35(1): 148-148.

10. Xia R, Dai B, Fu W, et al. Kinematic Comparisons of the Shakehand and Penhold Grips in Table Tennis Forehand and Backhand Strokes when Returning Topspin and Backspin Balls. Journal of Sports Science & Medicine. 2020; 19(4): 637-644.

11. Zheng G. Analysis of acute and chronic sports injuries in table tennis players. Sports Excellence (Academic Edition). 2019; 038(007): 101-102.

12. Kondrič M. The fastest ball games from the viewpoint of science. Journal of Human Kinetics. 2017; 55(1): 5-5. doi: 10.1515/hukin-2017-0001

13. Buzzelli AA, Draper JA. Examining the Motivation and Perceived Benefits of Pickleball Participation in Older Adults. Journal of Aging and Physical Activity. 2020; 28(2): 180-186. doi: 10.1123/japa.2018-0413

14. Pilis K, Stec K, Pilis A, et al. Body composition and nutrition of female athletes. Roczniki Państwowego Zakładu Higieny. 2019; 243-251. doi: 10.32394/rpzh.2019.0074

15. Nonaka Y, Ando S, Yamada Y. A study on the strengthening process of world top-level womentable tennis choppers: Taiikugaku kenkyu (Japan Journal of Physical Education, Health and Sport Sciences). 2018; 63(2): 753-768. doi: 10.5432/jjpehss.17063

16. Zhao Q, Lu Y, Jaquess KJ, et al. Utilization of cues in action anticipation in table tennis players. Journal of Sports Sciences. 2018; 36(23): 2699-2705. doi: 10.1080/02640414.2018.1462545

17. Zhou X. Explanation and verification of the rules of attack in table tennis tactics. BMC Sports Science, Medicine and Rehabilitation. 2022; 14(1). doi: 10.1186/s13102-022-00396-3

18. Huai D, Yan Y. Research on the influence and countermeasures of new seamless plastic table tennis on youth training. Sports Excellence (Academic Edition). 2017; 036(008): 131-133.

19. Zhang S, Mao H. Optimization Analysis of Tennis Players’ Physical Fitness Index Based on Data Mining and Mobile Computing. Wu W, ed. Wireless Communications and Mobile Computing. 2021; 2021(1). doi: 10.1155/2021/9838477

20. Wang Q, Zong B, Lin Y, et al. The Application of Big Data and Artificial Intelligence Technology in Enterprise Information Security Management and Risk Assessment. Journal of Organizational and End User Computing. 2023; 35(1): 1-15. doi: 10.4018/joeuc.326934

21. Xing Y, Yu L, Zhang JZ, et al. Uncovering the Dark Side of Artificial Intelligence in Electronic Markets. Journal of Organizational and End User Computing. 2023; 35(1): 1-25. doi: 10.4018/joeuc.327278

Published
2024-11-04
How to Cite
Zhang, J. (2024). Evaluation of the effect of artificial intelligence training equipment in physical training of table tennis players from a biomechanical perspective. Molecular & Cellular Biomechanics, 21(1), 319. https://doi.org/10.62617/mcb.v21i1.319
Section
Article