Taheri Hossein, Goodwin Stephen A, Tigue James A, Perry Joel C, Wolbrecht Eric T
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2128-2132. doi: 10.1109/EMBC.2016.7591149.
Robotic devices are a promising and dynamic tool in the realm of post-stroke rehabilitation. Researchers are still investigating how the use of robots affects motor learning and what design characteristics best encourage recovery. We present a parallel-actuated, end-effector robot designed to provide spatial assistance for upper-limb therapy while exhibiting low impedance and high backdrivability. A gradient based optimization was performed to find an optimal design that accounted for force isotropy, mechanical advantage, workspace size, and counter-balancing. A beta prototype has been built to these specifications (low impedance and high backdrivability) and has undergone initial controller performance as well as fit and function testing. By fitting a nonlinear model to experimental frequency response data, the apparent mass, viscous friction coefficient, and dynamic dry friction coefficient were determined to be 0.242 kg, 0.114 Ns/m, and 0.894 N respectively. The robot will serve as a testing platform to investigate motor learning and evaluate the efficacy of control schemes for post-stroke movement therapy.
机器人设备是中风后康复领域中一种很有前景且充满活力的工具。研究人员仍在研究机器人的使用如何影响运动学习，以及何种设计特点最能促进恢复。我们展示了一种平行驱动的末端执行器机器人，旨在为上肢治疗提供空间辅助，同时呈现出低阻抗和高反向驱动能力。进行了基于梯度的优化，以找到一种考虑了力的各向同性、机械优势、工作空间大小和平衡的最优设计。已按照这些规格（低阻抗和高反向驱动能力）制造了一个β原型，并进行了初步的控制器性能以及适配和功能测试。通过将非线性模型拟合到实验频率响应数据，确定表观质量、粘性摩擦系数和动态干摩擦系数分别为0.242千克、0.114牛·秒/米和0.894牛。该机器人将作为一个测试平台，用于研究运动学习并评估中风后运动治疗控制方案的疗效。
