English(EN) Multimodal Injury Risk Prediction in Tennis

新AI框架利用多模态数据预测网球运动员受伤风险

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了一个名为PART（Predictive Athlete Readiness framework for Tennis，网球运动员就绪度预测框架）的多模态框架，用于评估网球运动员的表现和受伤风险。该框架整合了生理指标、训练和比赛数据、可穿戴设备睡眠数据、每日问卷、跳跃评估以及比赛视频中的运动分析等数据。该系统捕捉四项关键的球员特征：整体健康状况、受伤风险、身体能力和比赛风格，并对特定身体部位的风险提供高级预测。 AI

影响该框架通过提供早期风险评估，有助于减少网球运动员的受伤。

排序理由该项目是一篇发表在arXiv上的研究论文，详细介绍了一个用于损伤预测的新机器学习框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Francisco Erramuspe Alvarez, Shobharani Polasa, Weihao Qu, Jay Wang, Ling Zheng · 2026-08-27 04:00

网球中的多模态损伤风险预测

arXiv:2608.25126v1 Announce Type: new Abstract: 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…

本文如何被排名

报道来源 [1]

网球中的多模态损伤风险预测

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