The Role of Artificial Intelligence in Improving Athletic Performance: A Systematic Review

Hasan Sappar; Basem Ali; Fouad A.A. Al-Saady Asil

doi:10.61132/yudistira.v3i2.1632

Authors

Hasan Sappar University of Anbar
Basem Ali University of Anbar
Fouad A.A. Al-Saady Asil University of Anbar

DOI:

https://doi.org/10.61132/yudistira.v3i2.1632

Keywords:

AI, Performance, Sports, Research

Abstract

Abstract. The aim of this systematic review was to evaluate the impact of the use of artificial intelligence on improving athletic performance. Articles were selected through searches in various electronic databases, with those included following pre-established eligibility criteria. A narrative synthesis was performed. The electronic search resulted in 2,300 articles at first. After checking the titles, abstracts, and exclusion of duplicates, 52 were selected for full reading. After that, 33 articles were excluded. 19 articles were included in this systematic review. The included articles assess both the influence of artificial intelligence on improving team sports performance and individual sports performance. Forty percent of the articles found were observational, with the remaining 60% represented by quasi-experimental articles, indicating that few scientific methods were employed when testing interventions in the samples presented. The results indicate that the use of artificial intelligence has been more frequent in the study of team sports, with few studies using both commercial technologies. Studies demonstrate that despite the increase in research waves and the emergence of artificial intelligence products, many are still poorly understood. Moreover, the lack of proper control patterns also makes the comparison of results difficult. However, given the advances and the numerous possibilities for use, it is important to continue investing in research on artificial intelligence to improve the effectiveness and performance of athletes.

Downloads

Download data is not yet available.

References

Anderson, N., Belavy, D. L., Perle, S. M., Hendricks, S., Hespanhol, L., Verhagen, E., & Medicine, E. (2023). AI did not write this manuscript, or did it? Can we trick the AI text detector into generated texts? The potential future of ChatGPT and AI in Sports & Exercise Medicine manuscript generation. BMJ Specialist Journals, 9, e001568.

Asalomia, L. B., Nita, S. L., Mihailescu, M. I., Marascu, V., Samoilescu, G., & Racuciu, C. (2023). AI-enabled analysis of electric signals from gyrocompass for enhanced navigation management with cybersecurity considerations. Proceedings of the 2023 8th International Conference on Mathematics and Computers in Sciences and Industry (MCSI).

Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. J. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience, 2021(1), 8387680.

Bhuiyan, M. R., & Wree, P. (2023). Animal behavior for chicken identification and monitoring the health condition using computer vision: A systematic review. IEEE Access, 11, 126601-126610.

Boppiniti, S. T., & Engineering, C. (2022). Exploring the synergy of AI, ML, and data analytics in enhancing customer experience and personalization. International Machine Learning Journal, 5(5).

Bunker, R., & Susnjak, T. (2022). The application of machine learning techniques for predicting match results in team sport: A review. Journal of Artificial Intelligence Research, 73, 1285-1322.

Chmait, N., & Westerbeek, H. (2021). Artificial intelligence and machine learning in sport research: An introduction for non-data scientists. Frontiers in Sports and Active Living, 3, 682287.

Dhanya, V., Subeesh, A., Kushwaha, N., Vishwakarma, D. K., Kumar, T. N., Ritika, G., & Singh, A. (2022). Deep learning-based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6, 211-229.

Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2022). Conceptual structure and current trends in artificial intelligence, machine learning, and deep learning research in sports: A bibliometric review. International Journal of Environmental Research and Public Health, 20(1), 173.

Goriparthi, R. G. (2024). AI-driven predictive analytics for autonomous systems: A machine learning approach. Research in Intelligent Automation and Engineering, 15(1), 843-879.

Guelmami, N., Fekih-Romdhane, F., Mechraoui, O., & Bragazzi, N. L. (2023). Injury prevention, optimized training, and rehabilitation: How is AI reshaping the field of sports medicine? North African Journal of Medicine, 1(1), 30-34.

Iverson, G. L., Williams, M. W., Gardner, A. J., & Terry, D. P. (2020). Systematic review of preinjury mental health problems as a vulnerability factor for worse outcomes after sport-related concussion. Orthopaedic Journal of Sports Medicine, 8(10), 2325967120950682.

Jacob, S., Alagirisamy, M., Xi, C., Balasubramanian, V., Srinivasan, R., Parvathi, R., & Islam, S. M. (2021). AI and IoT-enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities. IEEE Access, 9, 80340-80350.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. European Management Journal, 31(3), 685-695.

Li, B., & Xu, X. (2021). Application of artificial intelligence in basketball sport. Journal of Exercise, Health & Sport, 11(7), 54-67.

Li, C., & Cui, J. (2021). [Retracted] Intelligent sports training system based on artificial intelligence and big data. Mathematical and Intelligent Systems, 2021(1), 9929650.

Luczak, T., Burch, R., Lewis, E., Chander, H., & Ball, J. (2020). State-of-the-art review of athletic wearable technology: What 113 strength and conditioning coaches and athletic trainers from the USA said about technology in sports. International Journal of Sports Science & Coaching, 15(1), 26-40.

Martowicz, M., Budgett, R., Pape, M., Mascagni, K., Engebretsen, L., Dienstbach-Wech, L., & Erdener, U. (2023). Position statement: IOC framework on fairness, inclusion, and non-discrimination on the basis of gender identity and sex variations. British Journal of Sports Medicine, 57(1), 26-32.

Mazurova, E., Standaert, W., Penttinen, E., & Tan, F. T. C. (2022). Paradoxical tensions related to AI-powered evaluation systems in competitive sports. Information Systems Frontiers, 24(3), 897-922.

Mishra, N., Habal, B. G. M., Garcia, P. S., & Garcia, M. B. (2024). Harnessing an AI-driven analytics model to optimize training and treatment in physical education for sports injury prevention. Proceedings of the 2024 8th International Conference on Education and Multimedia Technology.

Nahavandi, D., Alizadehsani, R., Khosravi, A., Acharya, U. R. (2022). Application of artificial intelligence in wearable devices: Opportunities and challenges. Computers in Medicine and Biomedicine, 213, 106541.

Olan, F., Arakpogun, E. O., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605-615.

Ramkumar, P. N., Luu, B. C., Haeberle, H. S., Karnuta, J. M., Nwachukwu, B. U., & Williams, R. J. (2022). Sports medicine and artificial intelligence: A primer. The American Journal of Sports Medicine, 50(4), 1166-1174.

Sampaio, T., Oliveira, J. P., Marinho, D. A., Neiva, H. P., & Morais, J. E. (2024). Applications of machine learning to optimize tennis performance: A systematic review. Applied Sciences, 14(13), 5517.

Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., & Jiang, D. (2023). WizardLM: Empowering large language models to follow complex instructions. arXiv preprint arXiv:2309.00486.