网球中的多模态损伤风险预测
Multimodal Injury Risk Prediction in Tennis
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中文总结 AI 辅助
本文提出多模态网球运动员准备度预测框架PART，整合多源数据，可评估网球运动员健康、损伤风险等，在大学生网球运动员数据上表现良好，也适用于业余选手。
中文摘要 AI 辅助
机器学习对运动员表现和损伤风险的预测产生了显著的积极影响。该领域的大多数研究依赖主观观察和专家评估，这限制了其有效性。在足球、篮球和摔跤等运动中，一些研究尝试通过整合可穿戴设备读数等替代来源的数据，结合传统的主观观察和专家评估来解决这一挑战，以提高准确性。然而，网球领域的类似研究仍大多未被探索。在本文中，我们提出了一种用于网球的多模态运动员准备度预测框架（Predictive Athlete Readiness framework for Tennis，PART），以评估网球运动员的表现和损伤风险。通过利用机器学习和深度学习技术，PART处理从9名大学网球运动员收集的多源数据，包括生理指标、训练和比赛数据、可穿戴设备记录的睡眠数据、每日问卷的自我报告信息、跳跃评估以及比赛视频的动作分析。PART捕捉网球运动员的四个特征：整体健康状况、损伤风险、身体能力和比赛风格。通过监督学习整合这四个特征，它能够全面评估网球运动员的状况，并对特定风险身体部位（如上半身（如肘部）或下半身（如膝盖））进行高级预测。我们使用9名大学网球运动员的数据进行的评估表明，PART在预测整体健康状况和损伤风险方面表现出色。此外，我们的框架对经常因不正确的打球技术而受伤的业余网球运动员也具有应用前景。
英文摘要
Machine learning has had a significant positive impact on the prediction of athlete performance and injury risk. Most works in this field rely on subjective observations and expert assessments, which restrict their effectiveness. In sports like soccer, basketball, and wrestling, some studies attempt to address this challenge by integrating data from alternative sources, such as readings from wearable devices, alongside traditional subjective observations and expert assessments to enhance accuracy. However, similar research in tennis remains largely unexplored. In this paper, we propose a multimodal Predictive Athlete Readiness framework for Tennis (PART) to assess both performance and injury risk in tennis players. By leveraging machine learning and deep learning techniques, PART processes multiple sources of data collected from nine collegiate tennis players, including physiological metrics, training and match data, sleep data from wearable devices, self-reported information via daily questionnaires, jump assessments, and motion analysis from match play videos. PART captures four characteristics of tennis players: overall wellness, injury risk, physical capability, and playing style. By integrating these four characteristics by supervised learning, it is capable of providing a holistic assessment of the tennis athlete's condition, along with advanced forecasts of specific body areas at risk such as the upper body (e.g., elbows) or lower body (e.g., knees). Our evaluation, conducted with data from nine collegiate tennis players, shows that PART achieves strong performance in predicting both overall wellness and injury risk. Additionally, our framework also shows promise for recreational tennis players, who often suffer from injuries due to incorrect playing techniques.
发表机构
- Monmouth University（蒙茅斯大学）
机构由 AI 辅助整理，请以论文原文为准。
