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<p>This study introduces a simulation-based wearable biomechanical sensor network framework intended to support real-time fatigue monitoring and performance prediction in gymnastic training environments. The work was motivated by the recognised need for objective, quantitative fatigue indicators that complement, rather than replace, the subjective ratings and laboratory-based assays currently used in gymnastics. Although sensor-based fatigue monitoring has been examined in endurance sports, the multi-planar, high-dynamic skills of gymnastics remain largely under-served; the framework therefore couples a sport-specific multi-modal sensor topology with personalised machine learning, an integration that to our knowledge has not been reported in the gymnastics literature. The proposed multi-node architecture integrates accelerometers, gyroscopes and force sensors, together with multi-feature fusion and machine learning-based fatigue classification, signal processing routines, and personalised prediction modules that capture the temporal dependencies of fatigue progression. In a computational validation built around virtual athlete models, the framework achieved a detection accuracy of approximately 93% across the six fatigue severity levels examined, while personalised algorithms outperformed generic counterparts by 12–18% within the same simulation pipeline. We emphasise that these figures are simulation-derived proof-of-concept results; they should not be interpreted as empirical validation, and confirmation through studies with real athletes wearing physical sensors in actual training environments is still required. The performance prediction module retained acceptable forecasting accuracy up to a 60-minute horizon, which in principle could support proactive training load management once the framework has been empirically validated. The real-time processing pipeline is, in principle, fast enough to feed immediate coaching feedback, and the modular architecture is intended to accommodate different gymnastic disciplines in future field studies. Taken together, the present work lays methodological groundwork for evidence-based training optimisation that, pending real-world validation, may eventually contribute to performance development and athlete-safety practice through objective biomechanical monitoring and predictive analytics.</p>

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Computational validation of a wearable biomechanical sensor network framework for real-time fatigue monitoring and performance prediction in gymnastic training

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Shengnan Ran,
  • Jiaojiao Hu

摘要

This study introduces a simulation-based wearable biomechanical sensor network framework intended to support real-time fatigue monitoring and performance prediction in gymnastic training environments. The work was motivated by the recognised need for objective, quantitative fatigue indicators that complement, rather than replace, the subjective ratings and laboratory-based assays currently used in gymnastics. Although sensor-based fatigue monitoring has been examined in endurance sports, the multi-planar, high-dynamic skills of gymnastics remain largely under-served; the framework therefore couples a sport-specific multi-modal sensor topology with personalised machine learning, an integration that to our knowledge has not been reported in the gymnastics literature. The proposed multi-node architecture integrates accelerometers, gyroscopes and force sensors, together with multi-feature fusion and machine learning-based fatigue classification, signal processing routines, and personalised prediction modules that capture the temporal dependencies of fatigue progression. In a computational validation built around virtual athlete models, the framework achieved a detection accuracy of approximately 93% across the six fatigue severity levels examined, while personalised algorithms outperformed generic counterparts by 12–18% within the same simulation pipeline. We emphasise that these figures are simulation-derived proof-of-concept results; they should not be interpreted as empirical validation, and confirmation through studies with real athletes wearing physical sensors in actual training environments is still required. The performance prediction module retained acceptable forecasting accuracy up to a 60-minute horizon, which in principle could support proactive training load management once the framework has been empirically validated. The real-time processing pipeline is, in principle, fast enough to feed immediate coaching feedback, and the modular architecture is intended to accommodate different gymnastic disciplines in future field studies. Taken together, the present work lays methodological groundwork for evidence-based training optimisation that, pending real-world validation, may eventually contribute to performance development and athlete-safety practice through objective biomechanical monitoring and predictive analytics.