A Task Performance-Based sEMG-Driven Variable Stiffness Control Strategy for Upper Limb Bilateral Rehabilitation System

Abstract

Bilateral rehabilitation robotics can allow hemiplegia patients to regain the cooperative capabilities of both arms by synchronized coordination movements. Furthermore, the variable stiffness actuators (VSA) integrated robotics can offer compliant advantages for human-robot interaction. Although various studies have proposed to improve training safety and comfortability by VSA, few studies have focused on inducing patient active participation by VSA-based variable stiffness control for bilateral rehabilitation. In this article, an surface electromyography (sEMG) driven variable stiffness control framework with a novel training task quantitative factor TPI was proposed to promote patient active participation in upper limb bilateral rehabilitation. The proposed control law integrates an sEMG-driven musculoskeletal model for providing real-time dynamic reference stiffness from the nonparetic limb as a task skill learning guide to the affected limb. Furthermore, the proposed TPI is designed in the high-level controller for rendering smooth and automatic transition among three patient-robot interaction modes for inducing active participants. In the low-level controller, a position-based bilateral impedance control and a cascaded backstepping position control were implemented for compliant task position planning and tracking. Preliminary experimental results show that the proposed method can promote patient active participation by providing minimal intervention assistance for facilitating efficient upper limb rehabilitation.