运动神经元驱动的计算肌肉建模,具有运动单元分辨率和特定对象的肌肉骨解剖学
Arnault H Caillet1,2, Andrew T M Phillips1, Dario Farina2
1Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom.
PLoS computational biology
|December 7, 2023
概括
一个新的运动神经元驱动模型通过详细描述单个运动单元动力学来增强肌肉收缩模拟. 这提高了生理学准确性,并解码神经控制在人机界面的应用程序.
科学领域:
- 生物力学 生物力学
- 神经科学是一个神经科学.
- 计算机建模 计算建模
背景情况:
- 现有的EMG驱动的Hill型模型由于多尺度简化而提供有限的生理细节.
- 为了更准确地描述收缩过程中的肌肉内部动态,需要进行全面的描述.
研究的目的:
- 开发一种新的运动神经元驱动的神经肌肉模型,用于增强模拟人类自愿肌肉收缩.
- 提高肌肉收缩模型提供的生理准确性和神经机械信息.
主要方法:
- 创建了一个描述单个动力单元发电力的模型,由实验衍生的运动神经元输入控制.
- 通过将EMG信号转化为运动单元力量来模拟自愿肌肉收缩.
- 整合了对象特定和肌肉特定的参数,具有先进的运动单元激活动态.
主要成果:
- 该模型通过总结单个运动单元的力量,准确地预测了整个肌肉力量.
- 使用密集的EMG网格或对未识别的动力单元活动的计算估计,实现了准确的力预测.
- 证明了动力单元的招聘和解雇动态的全面描述.
结论:
- 这种由运动神经元驱动的模型通过整合运动控制和肌肉骨模型来推进神经肌肉模型.
- 该模型提供了更生理准确的肌肉收缩的表现.
- 潜在的应用包括神经肌肉控制研究和人机接口.
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