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Enhancing Biophysical Muscle Fatigue Model in the Dynamic Context of Soccer.
Arian Skoki1, Stefan Ivić2, Sandi Ljubic1,3
1Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Sensors (Basel, Switzerland)
|January 8, 2025
Summary
This study refines muscle fatigue models (MFMs) for soccer, improving accuracy in predicting player exertion during dynamic activities. The enhanced model better captures the demands of soccer, aiding tactical decision-making and team performance.
Area of Science:
- Sports Science
- Biomechanics
- Exercise Physiology
Background:
- Existing muscle fatigue models (MFMs) struggle with the dynamic exertion levels in soccer.
- Prior research shows limitations in capturing rapid changes in physical demand during matches.
Purpose of the Study:
- To enhance existing MFMs for improved robustness and applicability to soccer.
- To develop a streamlined model that encapsulates soccer-specific dynamics.
- To correlate real-time physical performance with tactical decisions.
Main Methods:
- Refined an existing muscle fatigue model.
- Tested the model on maximum hand-grip tests, a soccer-specific drill, a treadmill protocol, and sprint tests.
- Validated the model using actual soccer match data.
Main Results:
- Achieved a slight R2 improvement from 0.87 to 0.89 in maximum hand-grip tests.
- Demonstrated robustness to dynamic changes in soccer-specific drills and treadmill protocols.
- Showcased individualized fitting with R2 scores from 0.62 to 0.80 in sprint tests.
- Maintained robust performance on soccer match data with average R2 scores of 0.70–0.72.
Conclusions:
- The refined MFM offers enhanced accuracy for soccer-specific physical demands.
- The model's ability to handle dynamic exertion shifts improves its practical application.
- This approach can inform tactical decisions by linking them to real-time player performance, potentially enhancing team strategy and outcomes.
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