Machine Learning Applications for Wearable Sensor Data in Tennis and Padel: A Systematic Review of Movement Recognition, Performance Monitoring, and Emerging Injury-Related Applications

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.