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任务约束和肌肉用力的最小化导致在步态中存在少量的肌肉协同作用。

Task constraints and minimization of muscle effort result in a small number of muscle synergies during gait.

机构信息

Department of Mechanical Engineering, Katholieke Universiteit Leuven Leuven, Belgium.

Department of Kinesiology, Katholieke Universiteit Leuven Leuven, Belgium.

出版信息

Front Comput Neurosci. 2014 Sep 18;8:115. doi: 10.3389/fncom.2014.00115. eCollection 2014.

Abstract

Finding muscle activity generating a given motion is a redundant problem, since there are many more muscles than degrees of freedom. The control strategies determining muscle recruitment from a redundant set are still poorly understood. One theory of motor control suggests that motion is produced through activating a small number of muscle synergies, i.e., muscle groups that are activated in a fixed ratio by a single input signal. Because of the reduced number of input signals, synergy-based control is low dimensional. But a major criticism on the theory of synergy-based control of muscles is that muscle synergies might reflect task constraints rather than a neural control strategy. Another theory of motor control suggests that muscles are recruited by optimizing performance. Optimization of performance has been widely used to calculate muscle recruitment underlying a given motion while assuming independent recruitment of muscles. If synergies indeed determine muscle recruitment underlying a given motion, optimization approaches that do not model synergy-based control could result in muscle activations that do not show the synergistic muscle action observed through electromyography (EMG). If, however, synergistic muscle action results from performance optimization and task constraints (joint kinematics and external forces), such optimization approaches are expected to result in low-dimensional synergistic muscle activations that are similar to EMG-based synergies. We calculated muscle recruitment underlying experimentally measured gait patterns by optimizing performance assuming independent recruitment of muscles. We found that the muscle activations calculated without any reference to synergies can be accurately explained by on average four synergies. These synergies are similar to EMG-based synergies. We therefore conclude that task constraints and performance optimization explain synergistic muscle recruitment from a redundant set of muscles.

摘要

发现产生给定运动的肌肉活动是一个冗余问题,因为肌肉的数量远远多于自由度的数量。从冗余集合中确定肌肉募集的控制策略仍然知之甚少。一种运动控制理论表明,运动是通过激活少量的肌肉协同作用产生的,即通过单个输入信号以固定比例激活的肌肉群。由于输入信号的数量减少,基于协同作用的控制是低维的。但是,肌肉协同作用控制理论的一个主要批评是,肌肉协同作用可能反映任务约束而不是神经控制策略。另一种运动控制理论表明,肌肉通过优化性能来募集。性能优化已广泛用于计算给定运动下的肌肉募集,同时假设肌肉的独立募集。如果协同作用确实决定了给定运动下的肌肉募集,那么不基于协同作用控制建模的优化方法可能会导致肌肉激活,这些肌肉激活不会显示通过肌电图(EMG)观察到的协同肌肉动作。然而,如果协同肌肉动作是由性能优化和任务约束(关节运动学和外力)引起的,则可以预期这些优化方法会导致与基于 EMG 的协同作用相似的低维协同肌肉激活。我们通过优化性能来计算实验测量的步态模式下的肌肉募集,假设肌肉的募集是独立的。我们发现,没有任何协同作用参考的肌肉激活可以通过平均四个协同作用来准确解释。这些协同作用与基于 EMG 的协同作用相似。因此,我们得出结论,任务约束和性能优化可以解释从冗余肌肉集合中募集协同作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b608/4167006/71b924caec53/fncom-08-00115-g0001.jpg

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