Chair of Automatic Control Engineering (LSR), Department of Electrical and Computer Engineering, Technical University of Munich, Munich, 80333, Germany.
Department of Neurology, Ludwig-Maximilian-University Munich, Munich, 81377, Germany.
Sci Rep. 2018 Apr 3;8(1):5583. doi: 10.1038/s41598-018-23792-7.
Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.
人类运动控制在生成多种任务所需的准确和适当的运动行为方面非常高效。本文研究了骨骼肌肉系统的运动学和动力学特性如何受到控制以实现这种效率。尽管最近的研究表明人类运动控制依赖于多个模型，但中枢神经系统 (CNS) 如何控制这种组合并未得到充分解决。在这项研究中，我们利用逆最优控制 (IOC) 框架来找到这些内部模型的组合以及这种组合如何针对不同的伸手任务发生变化。我们进行了一项实验，参与者执行了一套全面的自由空间伸手动作。结果表明，基于运动学和动力学的控制器之间存在权衡，具体取决于伸手任务。此外，这种权衡取决于手臂的初始和最终配置，这反过来又会影响要控制的骨骼肌肉负荷。有了这一认识，我们进一步提供了一种不适度量来证明其对不同逆内部模型贡献的影响。这种表述以及我们的分析不仅支持多个内部模型 (MIMs) 假说，还为 CNS 对人类伸手运动的控制提供了一个分层框架。
