Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command
Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command
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DOI：
10.1123/jab.20.4.367
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发表时间：
2004-11-01
影响因子：
1.4
通讯作者：
Besier, TF
中科院分区：
文献类型：
--
作者：
Buchanan, TS;Lloyd, DG;Besier, TF
本文概述了正向动态神经肌肉骨骼建模。这些模型的目的是从神经信号中估计或预测肌肉力、关节力矩和/或关节运动学。这是一个四步的过程。在第一步中，肌肉激活动力学控制从神经信号到肌肉激活度量的转换——一个在0到1之间随时间变化的参数。在第二步中，肌肉收缩动力学表征了肌肉激活如何转化为肌肉力量。第三步需要一个肌肉骨骼几何模型来将肌肉力转换为关节力矩。最后，运动方程允许将关节力矩转化为关节运动。每一步都涉及复杂的非线性关系。本文的重点是前两个步骤所涉及的细节，因为这对生物力学家来说是最具挑战性的。然后通过应用于预测等距肘关节力矩和动态膝关节动力学的研究来解释全局过程。
This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation-a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.