Sharif Razavian Reza, Ghannadi Borna, McPhee John
Motion Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
Front Comput Neurosci. 2019 Apr 16;13:23. doi: 10.3389/fncom.2019.00023. eCollection 2019.
It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom. We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model. We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces. Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces. The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed.
有人提出，人类神经系统控制任务（或操作）空间中的运动。然而，对于任务相关和任务无关自由度控制的分离关注甚少。我们在一个计算模型中研究肌肉协同作用如何用于分别控制任务相关和冗余自由度。我们推广了一个现有的运动控制模型，并假设任务空间和冗余空间具有正交基向量。这一假设源于对人类神经系统紧密控制任务相关变量而让其余变量不受控制的观察。换句话说，在一个空间中控制变量不会影响另一个空间；因此，在两个空间中的驱动必须是正交的。我们通过选择在任务空间和冗余空间中产生具有正交方向力向量的肌肉协同作用，在模型中实现了这一假设。我们的实验结果表明，正交性假设在根据任务空间和冗余空间中测量的运动学/动力学重建肌肉活动方面表现良好。具体而言，我们发现，通过正交性假设可以捕捉到大约70%的测量肌肉活动变化，同时允许在两个空间中有效分离控制。所开发的运动控制模型是肌肉骨骼系统实时模拟以及生物机电系统基于模型控制的一个可行工具，在这些领域需要对人类运动控制器进行计算高效的表示。
