Machine Learning Applications for Wearable Sensor Data in Tennis and Padel: A Systematic Review of Movement Recognition, Performance Monitoring, and Emerging Injury-Related Applications.
9 affiliations
- 1Rehabilitation Unit, Ospedale degli Infermi, 13900 Biella, Italy.
- 2Research Center on Musculoskeletal Health, MusculoSkeletalHealth@UMG, Magna Graecia University, 88100 Catanzaro, Italy.
- 3Physical and Rehabilitative Medicine, Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", 88100 Catanzaro, Italy.
- 4Department of Life Sciences, Health, and Health Professions, Link Campus University, 00165 Rome, Italy.
- 5Unit of Audiology, Phoniatrics and Vestibology, Regional Centre for Cochlear Implants and ENT Diseases, Department of Experimental and Clinical Medicine, "Magna Graecia" University, 88100 Catanzaro, Italy.
- 6Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy.
- 7Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.
- 8Research Unit of Orthopedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
- 9Department of Orthopedic and Trauma Surgery, "Magna Graecia" University, "Renato Dulbecco" University Hospital, 88100 Catanzaro, Italy.
Abstract
Background: Wearable sensors combined with machine learning (ML) offer new opportunities for movement analysis and performance monitoring in tennis and padel. However, the current evidence and potential applications remain unclear. This systematic review aimed to summarize the applications of ML to wearable sensor data in tennis and padel, focusing on movement recognition, performance monitoring, workload and fatigue assessment, injury-related applications, and rehabilitation. Methods: PubMed and Scopus were searched from inception to 1 August 2026. Studies were eligible if they investigated tennis or padel, used wearable sensors, and applied ML or deep learning methods. Study selection and data extraction were independently performed by two reviewers. Risk of bias was assessed across domains related to participants, predictors, outcomes, and analysis/validation. Due to substantial heterogeneity, a qualitative synthesis was performed. Results: Of 393 records identified, 16 studies were included. Fourteen investigated tennis, one padel, and one included tennis data within a multisport study. IMU-based systems were the most frequently used technology. Most studies focused on movement and stroke recognition, while fewer addressed performance, workload, fatigue, or injury-related applications. Reported model performance was generally high, but validation strategies were heterogeneous, and participant-independent or external validation was uncommon. Nine studies presented some concerns, and seven were classified as having high risk of bias. Evidence directly addressing rehabilitation and return-to-sport was particularly limited. Conclusions: ML applied to wearable sensor data shows promising potential for movement recognition and performance assessment in tennis and padel. However, methodological limitations, small and selected cohorts, and limited external validation restrict current generalizability and practical translation. Future research should prioritize larger and more diverse cohorts, subject-independent and external validation, and prospective studies addressing injury prevention, rehabilitation, and return-to-sport.
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BibTeX
@article{moggio2026machine,
title = {{Machine Learning Applications for Wearable Sensor Data in Tennis and Padel: A Systematic Review of Movement Recognition, Performance Monitoring, and Emerging Injury-Related Applications.}},
author = {Lucrezia Moggio and Antonio Ammendolia and Alessandro de Sire and Nicola Marotta and Andrea Demeco and Melania Mercurio and Olimpio Galasso and Umile Giuseppe Longo and Michele Mercurio},
journal = {Journal of functional morphology and kinesiology},
year = {2026},
month = sep,
doi = {10.3390/jfmk11040391},
url = {https://europepmc.org/article/MED/42924670},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine Learning Applications for Wearable Sensor Data in Tennis and Padel: A Systematic Review of Movement Recognition, Performance Monitoring, and Emerging Injury-Related Applications.
AU - Lucrezia Moggio
AU - Antonio Ammendolia
AU - Alessandro de Sire
AU - Nicola Marotta
AU - Andrea Demeco
AU - Melania Mercurio
AU - Olimpio Galasso
AU - Umile Giuseppe Longo
AU - Michele Mercurio
PY - 2026
DA - 2026/09/29
JO - Journal of functional morphology and kinesiology
DO - 10.3390/jfmk11040391
UR - https://europepmc.org/article/MED/42924670
AB - Background: Wearable sensors combined with machine learning (ML) offer new opportunities for movement analysis and performance monitoring in tennis and padel. However, the current evidence and potential applications remain unclear. This systematic review aimed to summarize the applications of ML to wearable sensor data in tennis and padel, focusing on movement recognition, performance monitoring, workload and fatigue assessment, injury-related applications, and rehabilitation. Methods: PubMed and Scopus were searched from inception to 1 August 2026. Studies were eligible if they investigated tennis or padel, used wearable sensors, and applied ML or deep learning methods. Study selection and data extraction were independently performed by two reviewers. Risk of bias was assessed across domains related to participants, predictors, outcomes, and analysis/validation. Due to substantial heterogeneity, a qualitative synthesis was performed. Results: Of 393 records identified, 16 studies were included. Fourteen investigated tennis, one padel, and one included tennis data within a multisport study. IMU-based systems were the most frequently used technology. Most studies focused on movement and stroke recognition, while fewer addressed performance, workload, fatigue, or injury-related applications. Reported model performance was generally high, but validation strategies were heterogeneous, and participant-independent or external validation was uncommon. Nine studies presented some concerns, and seven were classified as having high risk of bias. Evidence directly addressing rehabilitation and return-to-sport was particularly limited. Conclusions: ML applied to wearable sensor data shows promising potential for movement recognition and performance assessment in tennis and padel. However, methodological limitations, small and selected cohorts, and limited external validation restrict current generalizability and practical translation. Future research should prioritize larger and more diverse cohorts, subject-independent and external validation, and prospective studies addressing injury prevention, rehabilitation, and return-to-sport.
SN - 2411-5142
ER -