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。AI 中文总结
本研究针对网球领域多模态损伤风险与表现预测方法不足的问题，提出PART多模态加权集成学习框架，整合多类数据提取专属特征，采用自适应权重策略，经9名大学网球运动员数据验证，可监测健康、估算损伤风险，对休闲球员也有应用潜力。
AI 中文摘要
机器学习已对体育行业产生积极影响，其最具前景的应用之一是预测运动员表现与损伤风险。近期研究采用最先进模型提升预测精度，但数据可获得性及对主观观察或专家评估的依赖仍限制了进展。为解决这些局限，足球、篮球、摔跤等项目的研究者已开始整合可穿戴设备读数等异构数据源与传统主观评估，然而网球领域类似的多模态方法仍未得到充分探索。本研究提出网球运动员预测框架（Predictive Athlete Readiness for Tennis，PART）这一多模态加权集成学习框架，用于监测网球运动员健康状况并估算其近期损伤风险。PART处理的输入涵盖生理指标、训练与比赛数据、可穿戴设备记录的睡眠信息、自我报告问卷、垂直跳跃评估及比赛视频的动作分析等多种类型。针对这些模态，专用机器学习与深度学习模型会独立提取四项运动员专属特征：整体健康状况、损伤风险、身体能力及比赛风格。为克服整合不同模态的复杂性，PART采用监督式加权集成整合策略，基于各预测模型的可靠性为其分配自适应权重。对9名大学网球运动员的多模态数据评估显示，PART在监测运动员健康状况与估算近期损伤易感性方面表现出色。除大学运动员外，该框架对休闲网球运动员也具有应用前景，可提供个性化见解以降低损伤风险并优化表现。
英文摘要
Machine learning has had a positive impact on the sports industry, with one of its most promising applications being the prediction of athlete performance and injury risk. Recent advances have employed state-of-the-art models to improve prediction accuracy, yet progress remains limited by data availability and the reliance on subjective observations or expert assessments. To address these limitations, researchers in sports such as soccer, basketball, and wrestling have begun integrating heterogeneous data sources, such as wearable device readings, with traditional subjective assessments. However, similar multimodal approaches remain underexplored in tennis. In this work, we propose a multimodal weighted ensemble learning framework, Predictive Athlete Readiness for Tennis (PART), to monitor athlete wellness and estimate near-term injury risk in tennis players. PART processes a wide range of inputs, including physiological metrics, training and match data, sleep information from wearable devices, self-reported questionnaires, vertical jump assessments, and motion analysis from match-play videos. From these modalities, specialized machine learning and deep learning models independently extract four athlete-specific characteristics: overall wellness, injury risk, physical capability, and playing style. To overcome the complexity of combining these diverse modalities, PART employs a supervised weighted ensemble integration strategy, assigning adaptive weights to each predictive model based on its reliability. Evaluation of multimodal data collected from nine collegiate tennis players demonstrates that PART achieves strong performance in monitoring athlete wellness and estimating near-term injury susceptibility. Beyond collegiate athletes, the framework also shows promise for recreational tennis players, offering personalized insights to mitigate injury risk and optimize performance.
Comments8 pages. Accepted author manuscript. Published as Early Access in IEEE Systems, Man, and Cybernetics Magazine
Journal refIEEE Systems, Man, and Cybernetics Magazine, Early Access, pp. 1-7, 2026
