疲劳动力学和任务实现的相互作用使用最佳控制预测模拟.
P Puchaud1, B Michaud1, M Begon1
1Laboratoire de Simulation et Modélisation du Mouvement, Département de Kinésiologie, Université de Montréal, Laval, Canada.
Human movement science
|February 24, 2024
概括
人类运动的预测模型现在可以模拟疲劳. 通过将肌肉疲劳模型与刚性身体动力学相结合,研究人员揭示了不同的预测和反应策略,以优化重复任务并降低受伤风险.
科学领域:
- 生物力学 生物力学
- 人类运动模拟器
- 疲劳建模 疲劳建模
背景情况:
- 预测人类运动模拟对于优化重复任务中的技术至关重要.
- 当前的模型往往将执行器激活最小化,不准确地假设这将尽量减少疲劳.
- 为了实现现实的运动预测,需要更深入地了解疲劳诱导的适应.
研究的目的:
- 开发一种先进的模型来模拟人体运动中因疲劳引起的适应.
- 为了研究肌肉疲劳对重复性任务中的运动策略的影响.
- 为了比较不同的最佳控制问题 (OCP) 配方用于疲劳模拟.
主要方法:
- 接口了小的三部分疲劳模型与刚体动力学.
- 在Xia的疲劳模型中整合了一个稳定不变量,用于直接的多重射击.
- 模拟的最大重复的子双肩卷曲使用OCPs与不同的成本函数和地平线.
主要成果:
- 滑动地平线OCP (20-21次) 显示,随着疲劳的增加,摇摆策略的动力学偏差.
- 全地平线OCP (32次) 整个使用了摇摆策略,预测更多的重复.
- 滑动地平线的OCP展示了减少扭矩的反应性策略和减少疲劳的预测性策略.
结论:
- 综合模型准确地预测了疲劳引起的动力学适应.
- 不同的OCP配方揭示了不同的预测和反应性疲劳管理策略.
- 这种方法为优化性能和减少疲劳相关伤害提供了潜力.
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