Digital coaching systems in sport: a systematic review of emerging technologies and research gaps for evidence-based coaching
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Published: July 23, 2026
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Page: 566-581
Abstract
Sports coaching has been getting a major overhaul from digital technologies. Things like continuous athlete monitoring, computer vision movement analysis, wearable sensors, and intelligent decision-support systems are all part of it. But even with all this rapid progress, the evidence is still scattered across different fields, sport science, engineering, AI, human-computer interaction. That makes it hard to get a clear picture of how well these tools actually work in real coaching. This systematic review pulled together the current evidence on digital coaching systems. We wanted to see what technological approaches are dominant, how solid the research methods are, and where things should go next. The review stuck to the PRISMA 2020 guidelines. We ran a structured Boolean search in Scopus, looking at TITLE-ABS-KEY fields for studies from 2021 to 2025. Only English-language peer-reviewed journal articles that met predefined inclusion and exclusion criteria were kept. Two independent reviewers screened titles, abstracts, and full texts; any disagreements got sorted out in discussion. Methodological quality got rated using the FICO framework, Focus, Information, Context, Outcome. Then we synthesized the findings through inductive thematic analysis. Out of 986 records initially identified, just 15 studies made it through all the eligibility and quality checks. Four major themes came up: wearable sensing and athlete monitoring, computer vision and markerless motion tracking, intelligent and immersive coaching systems, and analytics-driven decision-support. One thing was clear: technical accuracy is consistently high across these studies. But when it comes to long-term coaching effectiveness, usability, implementation, and ethical governance, the evidence is still thin. That points to a clear need for future research, longitudinal, multi-site, and centered on what coaches actually need.
- Digital coaching
- Athlete monitoring
- Computer vision
- Wearable sensors
- Ecision-support systems

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- Abahnini, N. M., Abahnini, K., & Mkaouer, B. (2024). Acute Effect of Video Feedback on Self-Regulation and Proprioceptive Control of Standing Back Tuck Somersault in the Absence of Vision. Motor Control, 28(3), 241–261. https://doi.org/10.1123/mc.2023-0046
- Achenbach, L., Bloch, H., Klein, C., Damm, T., Obinger, M., Rudert, M., Krutsch, W., & Szymski, D. (2024). Four distinct patterns of anterior cruciate ligament injury in women's professional football (soccer): A systematic video analysis of 37 match injuries. British Journal of Sports Medicine, 58(13), 709–716. https://doi.org/10.1136/bjsports-2023-107113
- Alzahrani, A., & Ullah, A. (2024). Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance. Digital Health, 10. https://doi.org/10.1177/20552076241256745
- Biró, A., Szilágyi, S. M., Szilágyi, L., Martín-Martín, J., & Cuesta-Vargas, A. I. (2023). Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer. Sensors, 23(7), Article 3595. https://doi.org/10.3390/s23073595
- Booth, A., Sutton, A., Clowes, M., & Martyn-St James, M. (2021). Systematic approaches to a successful literature review (3rd ed.). SAGE Publications.
- Coates, A. M., Cohen, J. N., & Burr, J. F. (2023). Investigating sensor location on the effectiveness of continuous glucose monitoring during exercise in a non-diabetic population. European Journal of Sport Science, 23(10), 2109–2117. https://doi.org/10.1080/17461391.2023.2174452
- Costa, J., Silva, C., Santos, M., Fernandes, T., & Faria, S. (2021). Framework for intelligent swimming analytics with wearable sensors for stroke classification. Sensors, 21(15), Article 5162. https://doi.org/10.3390/s21155162
- Diller, S. J. (2024). Ethics in digital and AI coaching. Human Resource Development International, 27(4), 584–596. https://doi.org/10.1080/13678868.2024.2315928
- Diller, S. J., & Passmore, J. (2023). Defining digital coaching: a qualitative inductive approach. Frontiers in Psychology, 14, Article 1148243. https://doi.org/10.3389/fpsyg.2023.1148243
- Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2023). 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), Article 173. https://doi.org/10.3390/ijerph20010173
- Esh, C. J., Pitsiladis, Y., Racinais, S., Taylor, L., Dablainville, V., Belfekih, T., Bendimerad, F., Pitsiladis, A., Verdoukas, P., Willems, M., Nader, N., Dalansi, F., Grandjean, P., Al-Mulla, M., Aldous, N., Dossou, J., Hassanein, Y. E., Khater, N., Miranda, H., & Cardinale, M. (2025). Real-Time Monitoring of Biometric Responses During a 200-km Ultra-Endurance Race Across the Desert. European Journal of Sport Science, 25(9), Article e70026. https://doi.org/10.1002/ejsc.70026
- Figueira, B., Mateus, N., Esteves, P., Dadelienė, R., & Paulauskas, R. (2022). Physiological Responses and Technical-Tactical Performance of Youth Basketball Players: A Brief Comparison between 3x3 and 5x5 Basketball. Journal of Sports Science and Medicine, 21(2), 332–340. https://doi.org/10.52082/jssm.2022.332
- Goes, F. R., Kempe, M., Van Norel, J., & Lemmink, K. A. P. M. (2021). Modelling team performance in soccer using tactical features derived from position tracking data. IMA Journal of Management Mathematics, 32(4), 519–533. https://doi.org/10.1093/imaman/dpab006
- Gomaz, L., Bouwmeester, C., van der Graaff, E., van Trigt, B., & Veeger, D. (2023). Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors. Sensors, 23(23), Article 9373. https://doi.org/10.3390/s23239373
- Goudsmit, J., Otter, R. T. A., Stoter, I., van Holland, B., van der Zwaard, S., de Jong, J., & Vos, S. (2022). Co-Operative Design of a Coach Dashboard for Training Monitoring and Feedback. Sensors, 22(23), Article 9073. https://doi.org/10.3390/s22239073
- Hou, Y., Li, Z., & Li, H. (2025). Sensor based interactive digital entertainment and gamified training to alleviate basketball player fatigue. Entertainment Computing, 52, Article 100838. https://doi.org/10.1016/j.entcom.2024.100838
- Hu, Y., Li, Y., Cui, B., Su, H., & Zhu, P. (2025). Internet of things enabled deep learning monitoring system for realtime performance metrics and athlete feedback in college sports. Scientific Reports, 15(1), Article 28405. https://doi.org/10.1038/s41598-025-13949-6
- Höschler, L., Halmich, C., Schranz, C., Koelewijn, A. D., & Schwameder, H. (2025). Evaluating the Influence of Sensor Configuration and Hyperparameter Optimization on Wearable-Based Knee Moment Estimation During Running. International Journal of Computer Science in Sport, 24(2), 80–106. https://doi.org/10.2478/ijcss-2025-0014
- Johnson, W. R., Mian, A., Robinson, M. A., Verheul, J., Lloyd, D. G., & Alderson, J. A. (2021). Multidimensional Ground Reaction Forces and Moments from Wearable Sensor Accelerations via Deep Learning. IEEE Transactions on Biomedical Engineering, 68(1), 289–297. https://doi.org/10.1109/TBME.2020.3006158
- Kos, A., Bűrmen Á., Hribernik, M., Tomažič, S., Umek, A., Fajfar, I., & Puhan, J. (2025). Lightweight Periodic Scheduler in Wearable Devices for Real-Time Biofeedback Systems in Sports and Physical Rehabilitation. Applied Sciences (Switzerland), 15(12), Article 6405. https://doi.org/10.3390/app15126405
- Kos, A., Hernandez Casillas, A., Tomazic, S., & Umek, A. (2025). Reliable Real-Time Communication for Coach-Assisted Feedback Systems in Swimming. IEEE Access, 13, 150091–150100. https://doi.org/10.1109/ACCESS.2025.3602904
- Krupitzer, C., Naber, J., Stauffert, J. P., Mayer, J., Spielmann, J., Ehmann, P., Boci, N., Bürkle, M., Ho, A., Komorek, C., Heinickel, F., Kounev, S., Becker, C., & Latoschik, M. E. (2022). CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning. Frontiers in Psychology, 13, Article 754732. https://doi.org/10.3389/fpsyg.2022.754732
- Li, Z., Wang, L., & Wu, X. (2025). Artificial intelligence based virtual gaming experience for sports training and simulation of human motion trajectory capture. Entertainment Computing, 52, Article 100828. https://doi.org/10.1016/j.entcom.2024.100828
- Lin, T., Aouididi, A., Chen, Z., Beyer, J., Pfister, H., & Wang, J. H. (2024). VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching. IEEE Transactions on Visualization and Computer Graphics, 30(1), 458–468. https://doi.org/10.1109/TVCG.2023.3327161
- Luo, P., & Song, J. (2025). Beyond raw data: AI-driven biosensor fusion for enhancing athletic performance. Array, 26, Article 100418. https://doi.org/10.1016/j.array.2025.100418
- Mekruksavanich, S., Phaphan, W., Hnoohom, N., & Jitpattanakul, A. (2024). Recognition of sports and daily activities through deep learning and convolutional block attention. PeerJ Computer Science, 10, Article e2100. https://doi.org/10.7717/PEERJ-CS.2100
- Mitrotasios, M., Plakias, S., Armatas, V., Kubayi, A., & Larkin, P. (2025). Strategic Insights Into One-Touch Finishing in Soccer: Analyzing Play During Copa America 2021. Perceptual and Motor Skills, 132(3), 567–584. https://doi.org/10.1177/00315125251320129
- Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), Article e1000097. https://doi.org/10.1371/journal.pmed.1000097
- Mundt, M., Born, Z., Goldacre, M., & Alderson, J. (2023). Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose. Sensors, 23(1), Article 78. https://doi.org/10.3390/s23010078
- Mănescu, D. C. (2025). Big Data Analytics Framework for Decision-Making in Sports Performance Optimization. Data, 10(7), Article 116. https://doi.org/10.3390/data10070116
- Ooi, J. H., & Gouwanda, D. (2023). Badminton stroke identification using wireless inertial sensor and neural network. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 237(4), 291–300. https://doi.org/10.1177/17543371211048328
- Ouyang, Y., Li, X., Zhou, W., Hong, W., Zheng, W., Qi, F., & Peng, L. (2024). Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology. PLoS ONE, 19(7 July), Article e0307478. https://doi.org/10.1371/journal.pone.0307478
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
- Panni, L., Cosoli, G., Arnesano, M., Citarelli, F., Antognoli, L., & Scalise, L. (2025). Measurement of Spatio-Temporal Gait Parameters Through a Wearable Device for the Evaluation of the Activity Level of Athletes. IEEE Open Journal of Instrumentation and Measurement, 4, Article 6500212. https://doi.org/10.1109/OJIM.2025.3636681
- Rana, M., & Mittal, V. (2021). Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review. IEEE Sensors Journal, 21(2), 1187–1207. https://doi.org/10.1109/JSEN.2020.3019016
- Ren, F., Ren, C., & Lyu, T. (2025). IoT-based 3D pose estimation and motion optimization for athletes: Application of C3D and OpenPose. Alexandria Engineering Journal, 115, 210–221. https://doi.org/10.1016/j.aej.2024.10.079
- Sanusi, K. A. M., Di Mitri, D., Limbu, B., & Klemke, R. (2021). Table tennis tutor: Forehand strokes classification based on multimodal data and neural networks. Sensors, 21(9), Article 3121. https://doi.org/10.3390/s21093121
- Segal, A. D., Adamczyk, P. G., Petruska, A. J., & Silverman, A. K. (2022). Balance Therapy With Hands-Free Mobile Robotic Feedback for At-Home Training Across the Lifespan. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2671–2681. https://doi.org/10.1109/TNSRE.2022.3205850
- Siddiqui, H. U. R., Younas, F., Rustam, F., Flores, E. S., Ballester, J. B., Diez, I. D. L. T., Dudley, S., & Ashraf, I. (2023). Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning. Sensors, 23(15), Article 6839. https://doi.org/10.3390/s23156839
- Soda, N., Takayama, S., & Shimokochi, Y. (2024). Immediate effects of different feedback methods on running jump height and motion improvement in male college basketball players. Human Movement Science, 98, Article 103293. https://doi.org/10.1016/j.humov.2024.103293
- Srivastava, P. K. R., Pandey, R. K., Srivastava, G. K., Anand, N., Krishna, K. R., Singhal, P., & Sharma, A. (2024). Intelligent Integration of Wearable Sensors and Artificial Intelligence for Real-time Athletic Performance Enhancement. Journal of Intelligent Systems and Internet of Things, 13(2), 60–77. https://doi.org/10.54216/JISIoT.130205
- Sun, Z., & Yin, L. (2025). Intelligent textile sensors coupled with machine learning for athlete physiological monitoring: a review of recent progress. Sensor Review, 1–21. https://doi.org/10.1108/SR-08-2025-0563
- Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, Article 45. https://doi.org/10.1186/1471-2288-8-45
- Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
- Wang, Y., Shan, G., Li, H., & Wang, L. (2023). A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw. Sensors, 23(1), Article 425. https://doi.org/10.3390/s23010425
- Watson, N., Hendricks, S., Stewart, T., & Durbach, I. (2021). Integrating machine learning and decision support in tactical decision-making in rugby union. Journal of the Operational Research Society, 72(10), 2274–2285. https://doi.org/10.1080/01605682.2020.1779624
- Wu, F., Wang, Q., Bian, J., Ding, N., Lu, F., Cheng, J., Dou, D., & Xiong, H. (2023). A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications. IEEE Transactions on Multimedia, 25, 7943–7966. https://doi.org/10.1109/TMM.2022.3232034
- Wu, J., & Wen, P. (2024). Track and field training information acquisition and feedback of the based wireless medical sensor network. Internet Technology Letters, 7(5), Article e442. https://doi.org/10.1002/itl2.442
- Yan, C. (2025). TinyML-Enhanced Cloud-Edge Collaborative Framework for Real-Time Sport Action Recognition. Internet Technology Letters, 8(5), Article e70100. https://doi.org/10.1002/itl2.70100
- Yang, F., & Wang, Z. (2025). An intelligent taekwondo coaching system based on augmented reality technology with real-time feedback mechanisms. Scientific Reports, 15(1), Article 40832. https://doi.org/10.1038/s41598-025-24608-1
- Zheng, W., Zhang, M., Dong, R., Qiu, M., & Wang, W. (2025). Feasibility and Accuracy of an RTMPose-Based Markerless Motion Capture System for Single-Player Tasks in 3x3 Basketball. Sensors, 25(13), Article 4003. https://doi.org/10.3390/s25134003
- Zhu, P., & Hu, Y. (2025). Carbon nanomaterials intelligent wearable devices for real-time athlete monitoring and performance tracking. Revista Materia, 30, Article e20250327. https://doi.org/10.1590/1517-7076-RMAT-2025-0327
- Zompanti, A., Basoli, F., Saggio, G., Mattioli, F., Sabatini, A., Grasso, S., Marino, M., Longo, U. G., Trombetta, M., & Santonico, M. (2024). Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine. Sensors, 24(19), Article 6182. https://doi.org/10.3390/s24196182