A novel approach for robot-assisted upper-limb rehabilitation

Abstract

This study presented a novel control approach for rehabilitation robotic system using the hybrid system theory and the subject’s bio-damping and bio-stiffness parameters. Resistance training was selected as a paradigm. The proposed control architecture incorporated the physical therapist’s behavior intervention, the stroke survivor’s muscle strength changes, and the robotic device’s motor control into a unified framework. The main focuses of this research were to (i) automatically monitor the subject’s muscle strength changes using the online identified bio-damping/stiffness parameters; (ii) make decisions on the modification of the desired resistive force so as to coincide with the subject’s muscle strength changes; and (iii) generate accommodating plans when the safety-related issues such as spasticity and the abnormal robotic working states happen during the execution of training tasks. A Barrett WAM compliant manipulator-based resistance training system and two experiments including four scenarios were developed to verify the proposed approach. Experimental results with healthy subjects showed that the hybrid system–based control architecture could administrate the subject’s muscle strength changes and the robotic device’s interventions in an automated and safe manner.