Detection of similarities and differences within the same shot movement using artificial intelligence-based performance analysis: An example of a tennis service

  • Takashi Jindo Division of Art, Music, and Physical Education, Osaka Kyoiku University, 4-698-1 Asahigaoka, Kashiwara, Osaka 582-8582
  • Yusuke Satonaka Information Services International-Dentsu, LTD, 2-17-1 Konan, Minato-ku, Tokyo 108-0075
  • Ryosuke Wakamoto Information Services International-Dentsu, LTD, 2-17-1 Konan, Minato-ku, Tokyo 108-0075
  • Michitaka Iida Information Services International-Dentsu, LTD, 2-17-1 Konan, Minato-ku, Tokyo 108-0075
  • Hikari Suzuki Master’s Program in Physical Education, Health and Sport Sciences University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8574
  • Hirotaka Shiraishi Master’s Program in Physical Education, Health and Sport Sciences University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8574
  • Daisuke Mitsuhashi Faculty of Health and Sport Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8574
Keywords: Performance analysis, motion analysis, artificial intelligence (AI), tennis, service

Abstract

Artificial intelligence (AI) -based performance analysis has the potential to support feedback in coaching; however, a useful method has not yet been proposed. This study aims to develop an AI-based performance analysis to support tennis coaching. Specifically, we investigate the accuracy of detecting similarities and differences within the same shot movement. The participants were two tennis players with more than ten years of tennis experience. This study targeted service in tennis and videos of the 1st and 2nd service from both sides were recorded using a smartphone located on the fence behind the participant. The analysis code was executed in Python, and the main part involved the use of BlazePose, which estimates the X-, Y-, and Z-coordinates of a human pose. Video clips of 2 s were cut, with a 1 s overlap between each clip, and one of the clips was manually chosen as the standard clip. The clips were compared with the comparison clips, and the difference scores for the total and each body part were automatically calculated. As a result, a certain accuracy (≥ 70%) was confirmed for detecting overlapping phases between clips. Moreover, manually evaluated body parts that showed different movements by a certified coach corresponded to the top three different parts in the AI-based analysis for 8 of the 12 conditions. Performance analysis provides feedback in tennis coaching.

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Published
2023-12-11
How to Cite
Jindo, T., Satonaka, Y., Wakamoto, R., Iida, M., Suzuki, H., Shiraishi, H., & Mitsuhashi, D. (2023). Detection of similarities and differences within the same shot movement using artificial intelligence-based performance analysis: An example of a tennis service. International Journal of Racket Sports Science, 5(1). Retrieved from https://journal.racketsportscience.org/index.php/ijrss/article/view/99