Bibliometric analysis of artificial intelligence in sport
DOI:
https://doi.org/10.47197/retos.v54.103531Keywords:
Artificial intelligence, sports training, modern sport, bibliometric analysis, ARA methodologyAbstract
The bibliometric analysis of artificial intelligence (AI) in sports reveals a growing trend in the research and application of this technology in this social phenomenon. In the last decade, there has been a significant increase in the number of scientific publications related to artificial intelligence and sports, indicating great interest in the topic. The objective of this research was to bibliometrically analyze the elements of artificial intelligence in sports. The methodology used was hermeneutics and the analysis of three fundamental components Authors, Journals and Contributions (ARA) proposed by the authors for the bibliometric review. 1002 scientific articles belonging to the Scopus (825), Science Direct (172) and Mendeley (5) databases were analyzed. Two criteria were taken as inclusion criteria in the research: all had to be scientific articles, in Spanish and English. The main results are based on the identification of the main authors, journals and contributions that enhance AI in sport, taking into account the new methodologies and trends of the above. In conclusion, AI in sports is defined as a tool that corrects errors, helps decision-making, enhances new sports training and competition strategies, helps prevent sports injuries, study opponents and enhance scenarios. high quality sports.
Keywords: Artificial intelligence, sports training, modern sport, bibliometric analysis, ARA methodology.
References
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics,11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007.
Bellod, H. C., Buendía Ramón, V., Carballeira Fernández, E., & Guzmán Luján, J. F. (2021). Análisis del estrés y el compromiso académico-deportivo mediante Redes Neuronales Artificiales Auto-organizativas. Retos, 42, 136–144. https://doi.org/10.47197/retos.v42i0.86983.
Bozděch, M., & Vychodilová, R. (2023). Evaluation of neural network feature and function settings on the model performance and accuracy. Journal of Physical Education and Sport, 23(4), pp. 983–989, 123. https://doi.org/10.7752/jpes.2023.04123.
Cheng, K., Guo, Q., He, Y., Li, C., & Wu, H. (2023). Artificial Intelligence in Sports Medicine: Could GPT-4 Make Human Doctors Obsolete? Annals of Biomedical Engineering, 51(8), pp. 1658–1662. https://doi.org/10.1007/s10439-023-03213-1.
Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Sage publications. https://doi.org/doi.org/10.1177/1558689812464242.
Dandrieux, P.-E., Navarro, L., Blanco, D., Hollander, K., & Edouard, P. (2023). Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): Protocol for a prospective cohort study over an athletics season. BMJ Open, 13(5), e069423. https://doi.org/10.1136/bmjopen-2022-069423.
Dorschky, E., Camomilla, V., Davis, J., Reenalda, J., & Koelewijn, A.D. (2023). Perspective on “in the wild” move-ment analysis using machine learning. Human Movement Science, 87, 103042. https://doi.org/10.1016/j.humov.2022.103042.
Huang, Y., & Bai, Y. (2023). Intelligent Sports Prediction Analysis System Based on Edge Computing of Particle Swarm Optimization Algorithm. IEEE Consumer Electronics Magazine, 12(2), pp. 73–82. https://doi.org/10.1109/MCE.2021.3139837.
Keiper, M.C. (2023). ChatGPT in practice: Increasing event planning efficiency through artificial intelligence. Journal of Hospitality, Leisure, Sport and Tourism Education, 33, 100454. https://doi.org/10.1016/j.jhlste.2023.100454.
Li, M., Gao, Y., & Zhao, J. (2023). Analysis of the current situation and development countermeasures of college sports training and management based on the background of artificial intelligence. Applied Mathematics and Non-linear Sciences. https://doi.org/10.2478/amns.2023.1.00161.
Li, W. (2023). Application of IoT-enabled computing technology for designing sports technical action characteristic model. Soft Computing, 27(17), pp. 12807–12824. https://doi.org/10.1007/s00500-023-08966-4.
Liu, A., Mahapatra, R.P., & Mayuri, A.V.R. (2023). Hybrid design for sports data visualization using AI and big data analytics. Complex and Intelligent Systems, 9(3), pp. 2969–2980. https://doi.org/10.1007/s40747-021-00557-w.
Liu, C., Hao, W., & Huo, B. (2023). Advances and challenges in sports biomechanics. Advances in Mechanics, 53(1), pp. 198–238. https://doi.org/10.6052/1000-0992-22-030.
Liu, Y., & Liu, L. (2023). Analysis of auxiliary modes for sports intelligence training system based on nonlinear model optimization and improved algorithms. Soft Computing. https://doi.org/10.1007/s00500-023-08546-6.
Matthew, J., Pagea, J.E., Mckenziea, P.M., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., ... & Moher, D. (2021). Declaración PRISMA 2020: Una guía ac-tualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. https://doi.org/doi.org/10.1016/j.recesp.2021.06.016.
Mei, Z. (2023). 3D Image Analysis of Sports Technical Features and Sports Training Methods Based on Artificial Intel-ligence. Journal of Testing and Evaluation, 51(1). https://doi.org/10.1520/JTE20210469.
Oronowicz, J., Ley, C., Pachowsky, M., Seil, R., & Tischer, T. (2023). Possibilities and perspectives for the use of artificial intelligence in orthopaedic sports medicine. Sports Orthopaedics and Traumatology, 39(1), pp. 4–10. https://doi.org/10.1016/j.orthtr.2022.12.002.
Rajsp, A., & Fister, I. (2023). Neo4j graph dataset of cycling paths in Slovenia. Data in Brief, 48, 109251. https://doi.org/10.1016/j.dib.2023.109251.
Sanabria-Navarro, J. R., Silveira Pérez, Y., & Cortina–Núñez, M. de J. (2023). Análisis bibliométrico del deporte 4.0: una realidad para el desarrollo de la cultura deportiva contemporánea. Retos, 48, 1086–1097. https://doi.org/10.47197/retos.v48.96948.
Sanabria-Navarro, J., Silveira-Pérez, Y., Pérez-Bravo, D., & de-Jesús-Cortina-Núñez, M. (2023). Incidences of artifi-cial intelligence in contemporary education. Comunicar, 77, 97-107. https://doi.org/10.3916/C77-2023-08.
Wang, X. (2023). Research on the evaluation of sports training effect based on artificial intelligence technology. Pro-ceedings of SPIE - The International Society for Optical Engineering, 12635, 1263513. https://doi.org/10.1117/12.2679212.
Wei, S., Wang, K., & Li, X. (2022). Design and implementation of college sports training system based on artificial intelligence. International Journal of System Assurance Engineering and Management, 13, pp. 971–977. https://doi.org/10.1007/s13198-021-01149-0.
Xie, M. (2023). Intelligent Analysis Method of Sports Training Posture Based on Artificial Intelligence. Lecture Notes in Electrical Engineering, 1031 LNEE, pp. 409–415. https://doi.org/10.1007/978-981-99-1428-9_50.
Xu, Z., & Zhang, S. (2023). Editorial: Special issue on artificial intelligence technologies in sports and art data applica-tions. Neural Computing and Applications, 35(6), pp. 4199–4200. https://doi.org/10.1007/s00521-022-08124-1.
Yang, C., & Chang, Y.-T. (2023). Data Collection And Performance Evaluation Of Running Training Sport Using Different Neural Network Techniques. Journal of Mechanics in Medicine and Biology, 23(4), 2340053. https://doi.org/10.1142/S0219519423400535.
Zhang, Y., Duan, W., Villanueva, L.E., & Chen, S. (2023). Transforming sports training through the integration of internet technology and artificial intelligence. Soft Computing. https://doi.org/10.1007/s00500-023-08960-w.
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