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

Takashi Jindo; Yusuke Satonaka; Ryosuke Wakamoto; Michitaka Iida; Hikari Suzuki; Hirotaka Shiraishi; Daisuke Mitsuhashi

doi:10.30827/Digibug.97955

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

DOI: https://doi.org/10.30827/Digibug.97955

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.

References

Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., & Grundmann, M. (2020). Blazepose: On-device real-time body pose tracking. arXiv preprint arXiv:2006.10204. https://doi.org/10.48550/arXiv.2006.10204

Brady, C., Tuyls, K., & Omidshafiei, S. (2021). AI for Sports. CRC Press.

Cai, J., Hu, J., Tang, X., Hung, T.-Y., & Tan, Y.-P. (2020). Deep historical long short-term memory network for action recognition. Neurocomputing, 407, 428-438. https://doi.org/10.1016/j.neucom.2020.03.111

Cust, E. E., Sweeting, A. J., Ball, K., & Robertson, S. (2019). Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance. Journal of Sports Sciences, 37(5), 568-600. https://doi.org/10.1080/02640414.2018.1521769

Devanandan, M., Rasaratnam, V., Anbalagan, M. K., Asokan, N., Panchendrarajan, R., & Tharmaseelan, J. (2021). Cricket Shot Image Classification Using Random Forest. In 2021 3rd International Conference on Advancements in Computing (ICAC) (pp. 425-430). Colombo, Sri Lanka. https://doi.org/10.1109/ICAC54203.2021.9671109.

Kovalchik, S. A., & Reid, M. (2017). Comparing matchplay characteristics and physical demands of junior and professional tennis athletes in the era of big data. Journal of sports science & medicine, 16(4), 489-497. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5721178/

Ó Conaire, C., Connaghan, D., Kelly, P., O'Connor, N. E., Gaffney, M., & Buckley, J. (2010). Combining inertial and visual sensing for human action recognition in tennis. Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams, 51-56. https://doi.org/10.1145/1877868.1877882

O’Donoghue, P. (2005). Normative Profiles of Sports Performance. International Journal of Performance Analysis in Sport, 5(1), 104-119. https://doi.org/10.1080/24748668.2005.11868319

Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. https://doi.org/10.1109/TASSP.1978.1163055

Shah, H., Chokalingam, P., Paluri, B., Pradeep, N., & Raman, B. (2007). Automated stroke classification in tennis. In Image Analysis and Recognition: 4th International Conference, ICIAR 2007, Montreal, Canada, August 22-24, 2007. Proceedings 4 (pp. 1128-1137). Springer Berlin Heidelberg.

Takahashi, H., Okamura, S., & Murakami, S. (2022). Performance analysis in tennis since 2000: A systematic review focused on the methods of data collection. International Journal of Racket Sports Science, 4(2), 40-55. https://doi.org/10.30827/Digibug.80900

Yoshida, S., Kitajima, E., & Miyata, R. (2021). Analyzing the motion of service on tennis using a pose-estimation model. In IEICE Conferences Archives [in Japanese].

Zhu, G., Xu, C., Huang, Q., & Gao, W. (2006). Action recognition in broadcast tennis video. 18th International Conference on Pattern Recognition (ICPR'06), 1, 251-254.