Abstract
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.
Data availability
The datasets generated and analyzed during the current study are provided in Supplementary File 1, which contains anonymized biomechanical parameters, injury records, and model prediction outputs. Raw sensor data files are available from the corresponding author upon reasonable request, subject to ethical approval requirements for human subjects data sharing.
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Contributions
GH and YZ conceived and designed the study. GH developed the data acquisition system, implemented the machine learning algorithms, and conducted the experimental data collection. YZ supervised the research methodology and provided guidance on the spatiotemporal analysis framework. GH performed the statistical analysis and data preprocessing. Both authors contributed to the interpretation of results and manuscript preparation. YZ reviewed and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript. YZ served as the corresponding author and was responsible for overall project coordination and communication.
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This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of Guangzhou College of Technology and Business (Ethics Approval Number: LLSC2025006, approved on June. 15, 2025). All participants provided written informed consent prior to data collection. The study protocol was reviewed and approved by the Research Ethics Board for studies involving human participants in sports biomechanics research.
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Han, G., Zhang, Y. & Sun, B. Wearable sensor big data analysis reveals spatiotemporal injury patterns in professional tennis players. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44199-9
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DOI: https://doi.org/10.1038/s41598-026-44199-9
