Martineau T, Vaidyanathan R
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:1500-1505. doi: 10.1109/ICORR.2017.8009460.
A positive training synergy can be obtained when two individuals attempt to learn the same motor task while mechanically coupled to one another. In this paper, we have studied how mimicking this interaction through impedance control can be exploited to improve assistance delivered by hand exoskeleton devices during rehabilitation. In this context, the machine and user take complementary roles akin to two coupled individuals. We present the derivation of a dynamic model of the human hand for the purpose of controller development for new hand exoskeleton platforms. Using this model, we have simulated the behavior of an iterative impedance controller programmed for rehabilitative training. The controller interacts with cylindrical objects to be grasped by means of an inverse kinematic mapping and tuning of mechanical impedance characteristic of the finger joints. Through fusion of concepts from motor control theory, muscle impedance and task oriented control, the controller is capable of iteratively learn to accomplish simple tasks involving grasping and lifting while cooperating with a user. The controller is also capable of adapting to more complex dynamics for more dexterous tasks, such as pulling on a hand-bar or loosening the cap of a jar. We believe the human-robot synergy established in this investigation has benefits to therapy. It can be combined with a broad range of training exercises and represents an incremental step towards mimicking natural human motor responses.
当两个人在机械上相互耦合的同时尝试学习相同的运动任务时，可以获得积极的训练协同效应。在本文中，我们研究了如何通过阻抗控制来模拟这种相互作用，以改善手部外骨骼设备在康复过程中提供的辅助。在这种情况下，机器和用户扮演着类似于两个相互耦合个体的互补角色。我们提出了一种用于新型手部外骨骼平台控制器开发的人手动态模型的推导。利用这个模型，我们模拟了为康复训练编程的迭代阻抗控制器的行为。该控制器通过逆运动学映射和手指关节机械阻抗特性的调整与要抓取的圆柱形物体进行交互。通过融合运动控制理论、肌肉阻抗和面向任务的控制等概念，该控制器能够在与用户协作的同时迭代学习完成涉及抓取和举起的简单任务。该控制器还能够适应更复杂的动力学以完成更灵巧的任务，例如拉手柄或拧开瓶盖。我们相信在这项研究中建立的人机协同对治疗有益。它可以与广泛的训练练习相结合，代表了朝着模仿自然人类运动反应迈出的渐进一步。
