A Subject-Adaptive Controller for Wrist Robotic Rehabilitation

In order to derive maximum benefit from robot-assisted rehabilitation, it is critical that the implemented control algorithms promote the participant's active engagement in therapy. Assist-as-needed (AAN) controllers address this need by providing only appropriate assistance during movement execution. Often, these controllers depend on the definition of an optimal movement profile, against which the participant's movements are compared. In this paper, we present a novel subject-adaptive controller, consisting of two main components: AAN control algorithm and online trajectory recalculation. First, the AAN control algorithm is based on an adaptive controller and introduces a novel feedback gain modification algorithm. Coupled with the uniformly ultimately bounded stability property of the resulting dynamic system, the developed controller is capable of changing the amount of error allowed during movement execution, while simultaneously estimating the forces provided by the participant that contribute to movement execution. Second, we present a real-time trajectory generation algorithm based on a physiologically optimal and experimentally validated asymmetric wrist movement profile. The feedback gain modification and trajectory generation algorithms are validated with the RiceWrist system in an experimental study involving five healthy subjects, with the modified AAN adaptive controller decreasing its feedback control action when a subject shifts his behavior from passively riding along with the robot during movement to actively engaging and initiating movements to the desired on-screen targets.