Digital technologies applied to the detection of learning problemsin sports and recreation contexts: research trends (2015–2025)
DOI:
https://doi.org/10.47197/retos.v72.117530Keywords:
Artificial intelligence, computer vision, digital technologies, learning problems, motor skillsAbstract
Introduction: The early detection of learning difficulties related to motor performance in sports, recreational, and school settings continues to be a key area of focus for researchers and education professionals.
Objective: To analyze research trends on the use of digital technologies applied to the detection of learning problems in sports and recreation contexts during the period 2015–2025, based on a bibliometric and systematic review of the scientific literature.
Methodology: Conducted under a quantitative-descriptive approach, oriented toward the bibliometric and systematic analysis of scientific production related to the use of digital technologies applied to the detection of learning problems in sports and recreation contexts during the period 2015–2025.
Results: The analysis of scientific literature published between 2015 and 2025 reveals a growing trend in the study of digital technologies applied to the detection of learning problems in sports and recreation contexts.
Discussion: The findings are consistent with the global trend observed in the literature, where multiple studies converge on the need to integrate technological innovation with pedagogical and health processes.
Conclusions: Research conducted between 2015 and 2025 showed a significant increase in the use of digital technologies to detect learning problems in sports and recreational settings, highlighting the application of artificial intelligence, computer vision, and wearable sensors in pose estimation, biomechanical analysis, and motor skills assessment.
References
Alzahrani, A., & Ullah, A. (2024). Advanced biomechanical analytics: Wearable technologies for preci-sion health monitoring in sports performance. Digital Health, 10. https://doi.org/10.1177/20552076241256745
Bastola, R., & Fenta, T. (2025). A survey on deep 3D human pose estimation. Artificial Intelligence Re-view, 58(24). https://doi.org/10.1007/s10462-024-11019-3
Bisquerra, R. (2009). Metodología de la investigación educativa (2.ª ed.). Madrid: La Muralla.
Bossavit, B., & Arnedillo, I. (2022). Using motion capture technology to assess locomotor development in children. Digital Health, 8. https://doi.org/10.1177/2055207622114420
Bossavit, B., & Arnedillo, I. (2023). Motion-based technology to support motor skills screening in de-veloping children: A scoping review. Computer Methods and Programs in Biomedicine, 240. https://doi.org/10.1016/j.cmpb.2023.107715
Bucheli, J. (2022). Tendencias digitales y la recreación física escolar. [Tesis de pregrado, Universidad Técnica de Ambato]. https://repositorio.uta.edu.ec/server/api/core/bitstreams/3df4978c-d7c1-4159-b304-08bb3ef5623f/content
Canali S, Schiaffonati V, Aliverti A. (2022). Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digital Health, 1(10). https://doi.org/10.1371/journal.pdig.0000104
Chen, L., Liu, T., Gong, Z., & Wang, D. (2024). Movement Function Assessment Based on Human Pose Estimation from Multi-View. Computer Systems Science and Engineering, 48(2), 321-339. https://doi.org/10.32604/csse.2023.037865
Chow, D., Cheng, W., & Tam, S. (2020). A Video-Based Classification System for Assessing Locomotor Skills in Children. Journal of Sports Science & Medicine, 19(3), 585-595. https://pmc.ncbi.nlm.nih.gov/articles/PMC7429431/
Fermin, P., Smith H, Zhang Y. (2020). Automated classification of movement quality using the Mi-crosoft Kinect V2 sensor. Computers in Biology and Medicine, 125. https://doi.org/10.1016/j.compbiomed.2020.104021
Hernández, R., Fernández, C., & Baptista, P. (2014). Metodología de la investigación (6.ª ed.). México: McGraw-Hill.
Huhn, S., Axt, M., Gunga, H., Maggioni, M., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., Bärnighausen, T., & Barteit, S. (2022). The Impact of Wearable Technologies in Health Re-search: Scoping Review. JMIR Mhealth Uhealth, 10(1). https://pmc.ncbi.nlm.nih.gov/articles/PMC8826148/
Kristoffersson, A., & Lindén, M. (2022). A Systematic Review of Wearable Sensors for Monitoring Phys-ical Activity. Sensors, 22(2), 573. https://doi.org/10.3390/s22020573
Marín, J., Rodríguez, Á., Martínez, D., & Rodríguez, J. (2025). Las competencias digitales del futuro pro-fesional de la Actividad Física y el Deporte en Ecuador. Retos, 67, 745–760. https://doi.org/10.47197/retos.v67.112741
Osmanoğlu, U., Erdaği, K., IŞIK, B., Örücü, S., & Özbay , E. (2025). Comparative analysis of Microsoft Kinect Azure and manual measurement methods in the sit and reach test among elite female weightlifters. Scientific Reports, 15(24636). https://doi.org/10.1038/s41598-025-09754-w
Ressman, J., Rasmussen-Barr, E., & Grooten, W. (2020). Reliability and validity of a novel Kinect-based software program for measuring a single leg squat. BMC Sports Science, Medicine & Rehabilita-tion, 12(31). https://doi.org/10.1186/s13102-020-00179-8
Tharatipyakul, A., Srikaewsiew, T., & Pongnumkul, S. (2024). Deep learning-based human body pose estimation in providing feedback for physical movement: A review. Heliyon, 10(17). https://doi.org/10.1016/j.heliyon.2024.e36589
Venek, V., Kranzinger, S., Schwameder, H., & Stöggl, T. (2022). Human Movement Quality Assessment Using Sensor Technologies in Recreational and Professional Sports: A Scoping Review. Sensors, 22(13). https://doi.org/10.3390/s22134786
Wang, Y., & Wang, X. (2024) Artificial intelligence in physical education: comprehensive review and future teacher training strategies. Frontiers in. Public Health, 12. https://doi.org/10.3389/fpubh.2024.1484848
Xu, H. (2025). AI-powered technologies in physical education: impact on physical and psycho-emotional well-being of students. The Journal of General Psychology, 1–22. https://doi.org/10.1080/00221309.2025.2541585
Zhang, Z., & Shin, S.-Y. (2025). Two-Dimensional Human Pose Estimation with Deep Learning: A Re-view. Applied Sciences, 15(13), 7344. https://doi.org/10.3390/app15137344
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