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首页> 外文期刊>The Journal of Neuroscience: The Official Journal of the Society for Neuroscience >Identification of neural feedback for upright stance in humans: stabilization rather than sway minimization.
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Identification of neural feedback for upright stance in humans: stabilization rather than sway minimization.

机译:识别人类直立姿势的神经反馈:稳定而不是最小化摇摆。

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摘要

A fundamental issue in motor control is how to determine the task goals for a given behavior. Here, we address this question by separately identifying the musculoskeletal and feedback components of the human postural control loop. Eighteen subjects were perturbed by two mechanical perturbations (gentle pulling from behind at waist and shoulder levels) and one sensory perturbation (movement of a virtual visual scene). Body kinematics was described by the leg and trunk segment angles in the sagittal plane. Muscle activations were described by ankle and hip EMG signals, with each EMG signal computed as a weighted sum of rectified EMG signals from multiple muscles at the given joint. The mechanical perturbations were used to identify feedback, defined as the mapping from the two segment angles to the two EMG signals. The sensory perturbation was used to estimate parameters in a mechanistic model of the plant, defined as the mapping from the two EMG signals to the two segment angles. Using the plant model and optimal control theory, we compared identified feedback to optimal feedback for a range of cost functions. Identified feedback was similar to feedback that stabilizes upright stance with near-minimum muscle activation, but was not consistent with feedback that substantially increases muscle activation to reduce movements of the body's center of mass or center of pressure. The results suggest that the common assumption of reducing sway may not apply to musculoskeletal systems that are inherently unstable.
机译:电机控制中的一个基本问题是如何确定给定行为的任务目标。在这里,我们通过分别识别人体姿势控制回路的肌肉骨骼和反馈成分来解决此问题。 18名受试者受到两种机械性摄动(在腰部和肩部从后面向后方拉动)和一种感觉性摄动(虚拟视觉场景的移动)摄动。身体运动学由矢状面中的腿和躯干段角度来描述。肌肉的激活由脚踝和臀部的EMG信号描述,每个EMG信号的计算结果是给定关节处来自多块肌肉的整流EMG信号的加权总和。机械扰动用于识别反馈,定义为从两个段角到两个EMG信号的映射。感觉扰动被用来估计植物的机械模型中的参数,该模型被定义为从两个EMG信号到两个节段角的映射。使用工厂模型和最优控制理论,我们将确定的反馈与一系列成本函数的最优反馈进行了比较。识别出的反馈类似于通过近乎最小的肌肉激活来稳定直立姿势的反馈,但与实质上增加肌肉激活以减少身体质心或压力中心运动的反馈不一致。结果表明,减少摇摆的一般假设可能不适用于固有不稳定的肌肉骨骼系统。

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