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.
['Hossein Taheri', 'Stephen A. Goodwin', 'James A. Tigue', 'Joel C. Perry', 'Eric T. Wolbrecht']
Design and optimization of PARTNER: A parallel actuated robotic trainer for NEuroRehabilitation