Problem
Research questions and friction points this paper is trying to address.
multimodal
injury risk
performance prediction
tennis
Multimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning
๐ค AI Summary
ๆฌๆๆๅบไธ็งๅคๆจกๆๅ ๆ้ๆๅญฆไน ๆกๆถPART๏ผ้่ฟๆดๅๅค็งๆฐๆฎๆบๆฅ้ขๆต็ฝ็่ฟๅจๅ็ๅไผค้ฃ้ฉๅ่กจ็ฐใ
๐ Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multimodal data
weighted ensemble learning
athlete wellness monitoring
injury risk prediction
adaptive weights
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