Multimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning
PART (Predictive Athlete Readiness for Tennis) is a multimodal weighted ensemble learning framework designed to monitor athlete wellness and estimate near-term injury risk in tennis players The system integrates heterogeneous data sources including wearable device metrics, self-reported questionnaires, vertical jump assessments, and motion analysis from match-play videos Four athlete-specific characteristics are independently extracted using specialized ML/DL models: overall wellness, injury ris
Analysis
TL;DR
- PART (Predictive Athlete Readiness for Tennis) is a multimodal weighted ensemble learning framework designed to monitor athlete wellness and estimate near-term injury risk in tennis players
- The system integrates heterogeneous data sources including wearable device metrics, self-reported questionnaires, vertical jump assessments, and motion analysis from match-play videos
- Four athlete-specific characteristics are independently extracted using specialized ML/DL models: overall wellness, injury risk, physical capability, and playing style
- A supervised weighted ensemble integration strategy assigns adaptive weights to each predictive model based on its reliability, addressing the complexity of combining diverse modalities
- Evaluation on nine collegiate tennis players demonstrates strong performance in wellness monitoring and injury susceptibility estimation, with potential applicability to recreational players
Why It Matters
This research addresses a critical gap in tennis analytics by introducing the first multimodal approach combining objective wearable data with subjective assessments for injury prediction in tennis. For AI practitioners working in sports analytics, it demonstrates how weighted ensemble learning can effectively fuse heterogeneous data modalities, offering a scalable template for other sports applications.
Technical Details
- Framework Architecture: PART employs a multimodal pipeline where specialized machine learning and deep learning models independently process different data streams before ensemble integration
- Input Modalities: Physiological metrics, training/match data, sleep information from wearables, self-reported questionnaires, vertical jump assessments, and video-based motion analysis
- Ensemble Strategy: Supervised weighted ensemble integration assigns adaptive weights to each predictive model based on reliability, rather than using simple averaging or concatenation
- Output Characteristics: Four distinct predictions—overall wellness, injury risk, physical capability, and playing style—each derived from modality-specific models
- Evaluation: Tested on multimodal data from nine collegiate tennis players, demonstrating strong performance in wellness monitoring and near-term injury susceptibility estimation
Industry Insight
- The weighted ensemble approach offers a practical blueprint for sports organizations looking to integrate wearable technology with traditional coaching assessments, potentially reducing injury rates and extending athlete careers
- The framework's adaptability to recreational players suggests commercial viability beyond elite sports, opening opportunities for consumer-facing tennis analytics products
- This work highlights the growing importance of multimodal fusion in domain-specific ML applications, where no single data source provides sufficient signal for reliable predictions
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