Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

查看全文
  • Gege Han,
  • Yongping Zhang,
  • Bailing Sun

摘要

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.