ArticleScientific reports2025
Multi-level data fusion enables collaborative dynamics analysis in team sports using wearable sensor networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales.BMC sports science, medicine & rehabilitation · 2026Article
- Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review.Journal of functional morphology and kinesiology · 2026Review
- A Robust Deep Learning Framework for Skill Level Discrimination in Tennis Strokes Using Bilateral IMU Measurements.Sensors (Basel, Switzerland) · 2026Article
- EdgeFuser: A tightly coupled adaptive framework for real-time athlete group analytics.iScience · 2026Article
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Authors and funding
3 authors.
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Abstract
This research proposes a novel multi-level data fusion method for analyzing collaborative dynamics in team sports using wearable sensor networks. We developed and validated this approach through controlled experiments with 40 semi-professional athletes across basketball and soccer scenarios. The multi-level fusion architecture integrates IMU, GPS, physiological, and positioning data through adaptive weight allocation and asynchronous alignment algorithms. Experimental validation demonstrated 8.6 dB improvement in signal quality and 42.3% enhancement in positional accuracy compared to single-source approaches. Cross-sport testing across basketball, soccer, volleyball, and handball showed consistent performance (84.2-91.4% accuracy) with real-time response times of 192-312ms. The developed collaborative dynamics indicator system revealed that temporal coordination parameters strongly correlate with team performance (r = 0.73), while four key metrics predict match outcomes with 73.6% accuracy. This methodology provides coaches and analysts with objective tools for quantifying previously subjective aspects of team coordination.
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