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一个肌肉反射模型,它编码了腿部力学的原理,产生了人类行走的动力学和肌肉活动。

A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities.

机构信息

Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2010 Jun;18(3):263-73. doi: 10.1109/TNSRE.2010.2047592. Epub 2010 Apr 8.

Abstract

While neuroscientists identify increasingly complex neural circuits that control animal and human gait, biomechanists find that locomotion requires little control if principles of legged mechanics are heeded that shape and exploit the dynamics of legged systems. Here, we show that muscle reflexes could be vital to link these two observations. We develop a model of human locomotion that is controlled by muscle reflexes which encode principles of legged mechanics. Equipped with this reflex control, we find this model to stabilize into a walking gait from its dynamic interplay with the ground, reproduce human walking dynamics and leg kinematics, tolerate ground disturbances, and adapt to slopes without parameter interventions. In addition, we find this model to predict some individual muscle activation patterns known from walking experiments. The results suggest not only that the interplay between mechanics and motor control is essential to human locomotion, but also that human motor output could for some muscles be dominated by neural circuits that encode principles of legged mechanics.

摘要

虽然神经科学家识别出越来越复杂的控制动物和人类步态的神经回路,但生物力学学家发现,如果注意塑造和利用腿部系统动力学的腿部力学原理,那么运动只需要很少的控制。在这里,我们表明肌肉反射可能对连接这两个观察结果至关重要。我们开发了一个由肌肉反射控制的人类运动模型,该模型编码了腿部力学的原理。有了这种反射控制,我们发现该模型从与地面的动态相互作用中稳定为步行步态,再现了人类步行动力学和腿部运动学,耐受地面干扰,并适应没有参数干预的斜坡。此外,我们发现该模型可以预测一些已知的行走实验中的个体肌肉激活模式。结果表明,力学和运动控制之间的相互作用不仅对人类运动至关重要,而且对于某些肌肉,人类运动输出可能由编码腿部力学原理的神经回路主导。

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