用机器学习分析多源数据，预测女足运动员伤病风险。
SoccerGuard: Investigating Injury Risk Factors for Professional Soccer Players with Machine Learning
- 融合主观报告、GPS数据与第三方统计，构建多模态输入框架。
- 优化窗口设置与数据平衡后，伤病预测准确率显著提升。
- 配备可视化界面，便于教练和医疗团队实时分析与干预。
我们提出SoccerGuard，一个基于机器学习（ML）的女性足球运动员伤病预测新框架。该框架可整合来自多个来源的数据，包括球员提供的主观健康与训练负荷报告、客观的GPS传感器测量数据、第三方球员统计数据以及经医疗人员核实的伤病记录。我们测试了多种配置，涉及合成数据生成、输入与输出窗口大小，以及不同机器学习模型的性能。结果表明，在合理配置与特征组合下，伤病事件预测可达到较高准确率。最优表现出现在输入窗口缩短、输出窗口扩大且数据集理想平衡时。框架还包含一个具备用户友好图形界面（GUI）的仪表板，支持交互式分析与可视化。
原文摘要 · Abstract (English)
We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury reports verified by medical personnel. We experiment with a number of different settings related to synthetic data generation, input and output window sizes, and ML models for prediction. Our results show that, given the right configurations and feature combinations, injury event prediction can be undertaken with considerable accuracy. The optimal results are achieved when input windows are reduced and larger combined output windows are defined, in combination with an ideally balanced data set. The framework also includes a dashboard with a user-friendly Graphical User Interface (GUI) to support interactive analysis and visualization.
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