EXPLORING THE USE OF ARTIFICIAL INTELLIGENCE IN MOTOR LEARNING IN TENNIS
DOI:
https://doi.org/10.24193/subbeag.70.sp.iss.2.36Keywords:
motor learning, tennis, artificial intelligenceAbstract
Sports have always been an important component of human culture due to the combination of neuro-psychic skills, strategic planning, and physical excellence. With the discovery and rapid development of artificial intelligence, its applicability in the sports domain is becoming increasingly evident, and its deep integration is an inevitable trend at this moment. In sports, artificial intelligence combined with data analysis highlights unparalleled opportunities regarding motor learning, outcome prediction, decision-making capabilities, and performance optimization. The integration of methodologies offered by artificial intelligence represents an innovative approach to enhancing athletic performance, which will continue to evolve as a foundation for the technology of sports science. The technique and tactics of tennis have reached a high level due to the evolution of sports equipment, and in response to this phenomenon, coaches have increasingly leveraged the physical training of athletes as it has developed. Following a review of the literature from recent years, we concluded that the data obtained through artificial intelligence provide specific details that can assist coaches and tennis players both in planning training and in the motor learning process. Furthermore, the results obtained through AI in sports analysis, especially for a fast and strategic sport like tennis, have effects on the level of optimization of athletic performance, which implicitly reflects on the competitive results of players. Therefore, this study aims to provide information to tennis coaches and players on how artificial intelligence can be utilized to facilitate motor learning with the objective of optimizing athletic performance. Additionally, we believe that the application of artificial intelligence in the training of tennis players offers specialists insights that can facilitate the adaptation of training methodologies to optimize the motor learning process. By utilizing this information in training, coaches can adapt training methods to meet the demands of modern tennis, thereby improving player outcomes. With technological advancement, the continuous exploration of the motor learning process through artificial intelligence is justified in the attempt to achieve significant or even major progress in optimizing athletic performance in tennis.
References
Bačić, B., & Hume, P. A. (2018). Computational intelligence for qualitative coaching diagnostics: Automated assessment of tennis swings to improve performance and safety. Big Data, 6(4), 291–304.
Bodemer, O. (2023). Enhancing individual sports training through artificial intelligence: A comprehensive review. Authorea Preprints.
Bozděch, M., Puda, D., & Grasgruber, P. (2024). A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach. PLoS ONE 19(11), e0309085. https://doi.org/10.1371/journal. pone.0309085
Havlucu, H., Akgun, B., Eskenazi, T., Coskun, A., & Ozcan, O. (2022, June 17) Toward Detecting the Zone of Elite Tennis Players Through Wearable Technology. Front. Sports Act. Living 4, 939641. doi: 10.3389/fspor.2022.939641
Ju’An, W., & Shewang, (2024) J. Electrical Systems 20-3: 135-143 2, The Current Situation, Characteristics and Prospects of the Research on Tennis Theme Literature in China from the Perspective of Artificial Intelligence, J. Electrical Systems, 20(3), 135-143.
Yuan, K. (2025). Multi-Factors Analysis Using Visualizations and SHAP: Comprehensive Case Analysis of Tennis Results Forecasting. International Journal of Advanced Computer Science and Applications, 16(1).
Koopmann, T., Faber, I., Baker, J., & Schorer, J. (2020). Assessing technical skills in talented youth athletes: A sys tematic review. Sports Medicine, 50(9), 593–1611. https://doi.org/10.1007/s40279-020-01299 4PMID:32495253
Li, H., Guo, H., & Huang, H., (2022). Analytical Model of Action Fusion in Sports Tennis Teaching by Convolutional Neural Networks. Computational Intelligence and Neuroscience.. Article ID 7835241 https://doi.org/10.1155/2022/7835241
Liu, S., Wu, C., Xiao, S., Liu, Y., & Song, Y. (2024.) Optimizing young tennis players’ development: Exploring the impact of emerging technologies on training effectiveness and technical skills acquisition. PLoS ONE, 19(8), e0307882. https:// doi.org/10.1371/journal.pone.0307882
Morandín-Ahuerma, F. (2022). What is artificial intelligence? International Journal of Research Publication and Reviews, 3(12), 1947–1951. https://doi.org/10.55248/gengpi.2022.31261.
Perri, T., Reid, M., Murphy, A., Howle, K., & Duffield, R. (2022) Prototype machine learning algorithms from wearable technology to detect tennis stroke and movementactions. Sensors, 22(22), 8868. https://doi. org/10.3390/s22228868 PMID: 36433462
Powroznik,P., Skublewska-Paszkowska, M., Dziedzic, K., & Barszcz, M. (2025) Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition. Appl. Sci., 15, 5320. https://doi.org/10.3390/ app15105320
Prăjescu, P., & Abalașei, B., A. (2025). Artificial Intelligence in Tennis. A Social Perspective. Studia UBB Educatio Artis Gymn, 70(2), 101-115. https://doi.org/10.24193/subbeag.70(2).16
Ruan., H. & Zhang, X. (2022). Optimization of a Wireless Sensor-Based Tennis Motion Pattern Recognition System, Journal of Sensors, Article ID 6232267. https://doi.org/10.1155/2022/6232267
Rui, H. (2023). IoT-based analysis of tennis player’s serving behavior using image processing, Soft Computing, 27, 14413–14429. https://doi.org/10.1007/s00500-023-09031-w
Sampaio, T., Oliveira, J., Marinho, D., Neiva, H., & Morais, J. (2024). Applications of machine learning to optimize tennis performance: A systematic review. Applied Sciences, 14(13), 5517. https://doi.org/10.3390/app14135517
Siqi, M. (2025). Design and Implementation Strategy of Informative Training System for Tennis Physical Education. Applied Mathematics and Nonlinear Sciences, 10(1), 1-23. https://doi.org/10.2478/amns-2025-048
Stoicescu, M., & Stănescu, M. (2015). Using mobile phone technology to enhance physical education efficiency. The 11th International Scientific Conference eLearning and Software for Education Bucharest, Romania. https://dx.doi.org/10.12753/2066-026X-15-238
Souaifi, M., Dhahbi, W., Jebabli, N., Ceylan, H. I., Boujabli, M., Muntean, R.I., & Dergaa, I. (2025). Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention. Bioengineering, 12, 887. https://doi.org/10.3390/bioengineering12080887
Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049. DOI: 10.1016/j.caeai.2022.100049
Taghavi, S., Davari, F., Malazi, H., T., & Abin, A. A. (2019). Tennis stroke detection using inertial data of a smartwatch. In 2019th International Conference on Computer and Knowledge Engineering (ICCKE), 466 - 474.
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