Wearable sensor big data analysis reveals spatiotemporal injury patterns in professional tennis players.

This study establishes a comprehensive analytical framework for characterizing spatiotemporal distribution patterns of sports injuries in professional tennis through advanced big data analysis of wearable device measurements. A multi-sensor integration system was developed to continuously monitor biomechanical parameters, physiological indicators, and movement patterns during training and competition. The research employed machine learning algorithms including LSTM networks, clustering analysis, and ensemble methods to identify injury patterns across temporal and spatial dimensions. Results revealed distinct seasonal periodicity in injury occurrence with peak frequencies during intensive training phases, while spatial analysis identified dominant injury concentrations in the shoulder-elbow complex (47.3%) and lumbar-hip region (31.8%). The spatiotemporal coupling analysis demonstrated that 73.2% of injury variance can be explained by the interaction between temporal patterns and spatial distributions. The Transformer-based prediction model achieved 91.5% accuracy with 0.956 AUC, significantly outperforming traditional statistical methods. These findings provide evidence-based foundations for developing intelligent injury prevention systems and optimizing training protocols in professional tennis environments.