Therapist-in-the-Loop robotics-assisted mirror rehabilitation therapy: An Assist-as-Needed framework

framework for robotics-assisted mirror rehabilitation therapy integrated with adaptive Assist-as-Needed (ANN) training, to be adjusted based on the impairment and disability level of the patient’s affected limb. Closed-loop system stability has been investigated using a combination of the Circle Criterion and the Small-Gain Theorem to account both for time-delay and the time-varying adaptive ANN training. Experiments to investigate the performance of the proposed framework are reported. I

Design and Control of Robotic Systems for Lower Limb Stroke Rehabilitation

Lower extremity stroke rehabilitation exhausts considerable health care resources, is labor intensive, and provides mostly qualitative metrics of patient recovery. To overcome these issues, robots can assist patients in physically manipulating their affected limb and measure the output motion. The robots that have been currently designed, however, provide assistance over a limited set of training motions, are not portable for in-home and in-clinic use, have high cost and may not provide sufficient safety or performance. This thesis proposes the idea of incorporating a mobile drive base into lower extremity rehabilitation robots to create a portable, inherently safe system that provides assistance over a wide range of training motions. A set of rehabilitative motion tasks were established and a six-degree-of-freedom (DOF) motion and force-sensing system was designed to meet high-power, large workspace, and affordability requirements. An admittance controller was implemented, and the feasibility of using this portable, low-cost system for movement assistance was shown through tests on a healthy individual. An improved version of the robot was then developed that added torque sensing and known joint elasticity for use in future clinical testing with a flexible-joint impedance controller