An Assist-as-Needed Controller for Robotic Rehabilitation Therapy Based on RBF Network

Abstract

The rehabilitation training with active involvement and efforts of impaired subjects can enhance the outcome of rehabilitation therapies. To promote patients' voluntary engagement, a variety of assist as needed (AAN) controllers have been proposed for robot-aided therapy. However, patients' impairment level is not taken into account in the implementation of those control schemes. In this study, a novel AAN controller is developed for upper limb rehabilitation therapy. The control paradigm uses Gaussian radial basis function (RBF) network to learn the model of subjects' motor capability in workspace. The update of weight vectors of RBF network is based on a greedy strategy, which has the potential to promote subjects' voluntary engagement by providing task challenge for them. Considering the difference in the impairment degree of patients, the assistance and impedance level of robot control is regulated based on the task performance of patients. The results of experiments at four healthy subjects verify the feasibility and adaptability of the proposed AAN controller.