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The Effect of the Self-Made Marking System on Table Tennis Players Serve and Reception Interpretation

Author(s)

Zeng Yumin1, Wang Pu1, Wang Ze2, Wang Zhengming3,*

Affiliation(s)

1Southern University of Science and Technology, Shenzhen, 518055, China 2Guangzhou Nanyang Polytechnic College , Guanzhou, 510900, China 3Shenzhen Technology University, Shenzhen, 518118, China *Corresponding Author.

Abstract

With the rise of the sports technology industry, it has become a global trend to combine science with technology. We can monitor the movement load and confirm the correctness of the key claims through wearable devices and action identification system, in order to improve the sports performance of the players, to search and reduce the controversy of the competition. Therefore, in the fast-paced table tennis match, how to replace the traditional way of sentiment search through technology intervention is a topic worth discussing. Objective: To improve the accuracy of table tennis lovers serve and receiving through the self-made marking system. Method: Use the single test study method to record an amateur table tennis player. Table tennis marker analysis and C statistics are used to understand the strength of the relationship between the variables. Results: Subject to accept the correct reading rate of "table tennis marking system after intervention". The C statistical analysis results found C=. 71, the cut-off test results z=2.85 (p <. 05), reaching significant differences. Conclusion: The visual learning of the table tennis marking system is effective. Conclusion: According to the C statistics and the analysis of the critical value results, the interpretation accuracy rate is not significantly different between the tracking period and the processing period, and the intervention effect is retained. In addition, the table tennis technology ball road is many difficultTo resolve with the naked eye. Therefore, it is believed that the improvement of interpretation accuracy is not only the improvement of their own skill level, but also the theoretical knowledge reserve of table tennis. The two are mutually related, which is also of great benefit to the skill level.

Keywords

Sports Technology; Sentiment Search; Accuracy

References

[1] Zhang Zhenxing. Analysis of the mark of receiving attack [J]. Volleyball Coach Science, 2010, (15) [2] Dai Congwen. Application of Data and Image Analysis in Taiwan Junior College Mens Basketball Competition [J]. Journal of Sports of National Taiwan University, 2018, (21) [3] Chen Jiayu, Liu Youde. Data can speak: Discussion on the methods of ball skills and tactics [J]. Taiwan Sports Psychology News, 2010 (17) [4] Li Pinxuan, Ou Jie, Lin Wenbin. Exploration on the intelligence of sports technology and tactical analysis [J]. Sports Management. 2018 (40) [5] Chen Guangzhi, Zeng Ruicheng. Comparison of PBL and direct teaching mode on college students billiards movement skills and learning attitude [J]. Journal of Sport, 2017, (1) [6] You Fengyun. Modern table ball serving and receiving technology training and application [J]. Journal of Sports Knowledge, 2007, (4) [7] Shen Zhuya. Research on the technical training of billiards serving and receiving service [J]. Science Public Science Education, 2012, (3) [8] Huang Zhenhua, Lin Lihua, Xie Xuan-rong. Trend analysis of the scoring effect of technical and tactical points in the table tennis competition stage [J]. Yadong Journal, 2010, (30) [9] Xu Minghua, Lin Jianhao, Wu Decheng. The Importance and Training methods of key ball in table ball games- -Take Zhuang Zhiyuan of the 2012 London Olympic Games as an example [J]. Sports Coaching Science, 2015, (38) [10] Wu Yufeng, Ye Shaowei. The Development strategy of the National Movement in the Era of Big Data [J]. Sports Management, 2016, (33) [11] Wang Weiyao, Zhang Kaixiang, Chen Tingfeng, etc. Badminton Coach AI: Badminton tournament information analysis platform based on deep learning [J]. Journal of Sport, 2020, (2) [12] CAI Junming, Li Qiulan. Application of deep learning YOLOv3 Research on the accuracy of improving the accuracy of detecting and identifying players in badminton events [J]. New Knowledge of the National Education Association, 2019, (1) [13] Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135. [14] Li, W., Wang, L., Li, W., Agustsson, E., & Van Gool, L. (2017). Webvision database: Visual learning and understanding from web data. arXiv preprint arXiv:1708.02862. [15] Jastorff, J., Kourtzi, Z., & Giese, M. A. (2009). Visual learning shapes the processing of complex movement stimuli in the human brain. Journal of Neuroscience, 29(44), 14026-14038. [16] Hughes, M., & Franks, I. M. (2004). Notational analysis—a review of the literature. Notational analysis of sport, 71-116. [17] Bischoff, M., Zentgraf, K., Lorey, B., Pilgramm, S., Balser, N., Baumgartner, E., ... & Munzert, J. (2012). Motor familiarity: Brain activation when watching kinematic displays of ones own　movements. Neuropsychologia, 50(8), 2085-2092. [18] Lam, W. K., Maxwell, J. P., & Masters, R. (2009). Analogy learning and the performance of motor skills under pressure. Journal of Sport and Exercise Psychology, 31(3), 337-357. [19]Liu, C., Huang, Q., Jiang, S., Xing, L., Ye, Q., & Gao, W. (2009). A framework for flexible summarization of racquet sports video using multiple modalities. Computer Vision and Image Understanding, 113(3), 415-424. [20] Sève, C., Saury, J., Leblanc, S., & Durand, M. (2005). Course-of-action theory in table tennis: A qualitative analysis of the knowledge used by three elite players during matches. European Review of Applied Psychology, 55(3), 145-155. [21] Williams, A. M., Davids, K., Burwitz, L., & Williams, J. G. (1994). Visual search strategies in experienced and inexperienced soccer players. Research Quarterly for Exercise and Sport, 65(2), 127-135. [22] Yang, B., Chang, Z., & Chen, Y. (2021). Falling-point recognition and scoring algorithm in table tennis using dual-channel target motion detection. Journal of Healthcare Engineering, 2021, 552998 1 . [23] Du, M., & Yuan, X. (2021). A survey of competitive sports data visualization and visual analysis. Journal of Visualization, 24(1), 47-67.