Adaptive control with state-dependent modeling of patient impairment for robotic movement therapy.

This paper presents an adaptive control approach for robotic movement therapy that learns a state-dependent model of patient impairment. Unlike previous work, this approach uses an unstructured inertial model that depends on both the position and direction of the desired motion in the robot's workspace. This method learns a patient impairment model that accounts for movement specific disability in neuro-muscular output (such as flexion vs. extension and slow vs. dynamic tasks). Combined with assist-as-needed force decay, this approach may promote further patient engagement and participation. Using the robotic therapy device, FINGER (Finger Individuating Grasp Exercise Robot), several experiments are presented to demonstrate the ability of the adaptive control to learn state-dependent abilities.