神经肌肉适应力量和压力训练的计算分析:一个综合建模研究
1Department of Physical Education and Sport, Bucharest University of Economic Studies, 010374 Bucharest, Romania.
Sports (Basel, Switzerland)
|September 26, 2025
概括
这项研究通过计算模拟了来自力量和压力训练的神经肌肉适应. 综合训练,特别是带跳跃的背,显示了运动员的优越收益,优化了表现并降低了受伤风险.
科学领域:
- 生物力学和体育科学 生物力学和体育科学
- 计算机建模 计算建模
- 在田径运动中的机器学习
背景情况:
- 优化运动表现和预防伤害需要了解神经肌肉适应训练.
- 特定的训练模式,如力量和多重测量引发不同的神经肌肉反应.
- 计算方法为预测这些适应提供了一种新的方法.
研究的目的:
- 通过计算模型和预测神经肌肉适应力和压力训练.
- 整合肌肉骨模拟与机器学习进行预测分析.
- 确定最佳的训练组合,以提高运动成果.
主要方法:
- 使用经过验证的肌肉骨模型 (OpenSim 4.4) 缩放到一个代表性的运动员.
- 模拟的压力计 (跳跃) 和力量 (,死,腿压) 练习.
- 在生物机械数据 (GRF,RFD,肌肉激活,关节动力学) 上使用机器学习 (ANN,随机森林).
主要成果:
- 压力测量任务显示了高地面反应力 (GRF) 和力量发展速度 (RFD).
- 结合力量和压力训练的训练,与单独的力量训练相比,产生了更大的时刻.
- 机器学习预测了综合训练,特别是背和跳跃跳跃的优越神经肌肉收益.
结论:
- 集成计算建模为优化培训干预提供了强大的工具.
- 结合强度和压力训练策略可以显著提高神经肌肉适应能力.
- 这种方法有可能精确降低运动员受伤风险.
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