通过融合生活与恢复数据，提升铁人三项运动员伤前预警准确率
Finding Pre-Injury Patterns in Triathletes from Lifestyle, Recovery and Load Dynamics Features
- 构建合成数据框架，模拟训练与生活多维度特征
- 睡眠紊乱、心率变异性低、压力高是关键早期预警信号
- 适合运动医学、智能穿戴与运动员健康管理团队
铁人三项训练包含高强度的游泳、骑行和跑步，因重复性生理负荷导致过度使用性损伤风险显著。现有预测方法多依赖训练量指标，常忽略睡眠质量、压力水平及个体生活方式等对恢复与受伤易感性的关键影响。本文提出一种专为铁人三项设计的合成数据生成框架，可生成符合生理规律的运动员档案，模拟个性化训练计划（含周期化与负荷管理），并整合睡眠质量、压力水平与恢复状态等日常因素。评估结果显示，机器学习模型（LASSO、随机森林、XGBoost）表现优异，最高AUC达0.86，识别出睡眠障碍、心率变异性降低及压力升高为重要早期损伤预警指标。该可穿戴设备驱动的方法不仅提升了预测精度，还有效缓解真实数据不足的问题，为实现全面、情境感知的运动员监测提供可行路径。
原文摘要 · Abstract (English)
Triathlon training, which involves high-volume swimming, cycling, and running, places athletes at substantial risk for overuse injuries due to repetitive physiological stress. Current injury prediction approaches primarily rely on training load metrics, often neglecting critical factors such as sleep quality, stress, and individual lifestyle patterns that significantly influence recovery and injury susceptibility. We introduce a novel synthetic data generation framework tailored explicitly for triathlon. This framework generates physiologically plausible athlete profiles, simulates individualized training programs that incorporate periodization and load-management principles, and integrates daily-life factors such as sleep quality, stress levels, and recovery states. We evaluated machine learning models (LASSO, Random Forest, and XGBoost) showing high predictive performance (AUC up to 0.86), identifying sleep disturbances, heart rate variability, and stress as critical early indicators of injury risk. This wearable-driven approach not only enhances injury prediction accuracy but also provides a practical solution to overcoming real-world data limitations, offering a pathway toward a holistic, context-aware athlete monitoring.
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