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首页> 外文期刊>Journal of Biomechanics >A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms.
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A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms.

机译：一参数神经激活到肌肉激活模型：从肌电图估计等距关节力矩。

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摘要

Nonlinearities have been observed in the isometric EMG-force relationship. However, these are generally not included when using EMG-driven Hill-type muscle models that account for muscle activation dynamics. In this paper, we present a formulation for a one-parameter transformation model (i.e., A-model) that accounts for the type of physiological nonlinearities observed at low levels of force. The general shape for the curvilinear portion of the curve was based on phenomenological data reported by Woods and Bigland-Ritchie. The one-parameter A-model is easy to implement, and when used with an EMG-driven Hill-type model, was shown to provide a better fit of the measured joint moment. Optimization methods were used to determine the appropriate curvature of the relationship for each muscle, and thus introduced a degree of "tuning" to each subject.
机译：在等距肌电图-力关系中已观察到非线性。但是，当使用EMG驱动的Hill型肌肉模型来说明肌肉激活动态时，通常不包括这些。在本文中，我们提出了一种单参数转换模型（即A模型）的公式，该模型考虑了在低力水平下观察到的生理非线性类型。曲线的曲线部分的总体形状基于伍兹和比格兰-里奇（Woodland and Bigland-Ritchie）报告的现象学数据。单参数A模型易于实现，并且与EMG驱动的Hill型模型一起使用时，可以更好地拟合所测量的关节力矩。使用优化方法来确定每条肌肉的适当曲率关系，从而为每位受试者引入一定程度的“调整”。

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