A Therapist-Inspired Approach to Stiffness Modulation in Rehabilitation Exoskeletons

Abstract

In rehabilitation robotics, adapting exoskeleton behaviour to align with therapists' approach -where they intuitively vary their arm stiffness to provide tailored assistance to patients- is key for effective treatment and technology acceptance. Advanced robotic platforms have multiple setting parameters, but these often fail to precisely reflect therapists' desires. This work presents a novel framework to quantify and replicate therapists' stiffness when guiding arm movements, enabling its transfer to exoskeleton control. Using surface electromyography and torque sensors, we analyzed the relationship between muscle activity and joint torque to estimate stiffness, specifically focusing on the elbow joint. We tested our framework on the AGREE exoskeleton with three healthy participants simulating therapist roles and interacting with the robot under different conditions. Participants' activity was recorded and their elbow stiffness was then converted to robotic stiffness to control the elbow flexion-extension joint. Results show that our framework effectively captured participants' behavioural nuances and translated them into human-like robotic behaviours. When applied to AGREE, therapist-derived stiffness values generated trajectory-tracking errors similar to those observed when participants manually guided the robot. By mirroring therapists' adaptive support, this approach could enhance patient recovery and bridge the gap between human and robotic therapy in clinical settings.