Adaptive Upper Limb Robot-Assisted Rehabilitation: Learn-from-Therapist Demonstrations

Abstract

Robotic-assisted rehabilitation is a promising method for improving motor function in individuals with upper limb impairments. However, generation of personalized and adaptive assistance patterns remains a challenge. Our study introduces a Learn-from-Therapist Demonstration (LfTD) framework, which employs Dynamic Movement Primitives (DMP) to train a robot arm to learn from therapist skills. Initially, therapist movements were captured via visual tracking, and the DMP accurately learned and replicated these motions via a robotic arm to assist patients. These movements were then effectively generalized to new goals while maintaining the original motion patterns. Meanwhile, a Model Reference Adaptive Controller (MRAC) has been utilized to refine the robot's adaptive performance while assuring demonstration tracking. We assessed the LfTD's efficacy with a simulated two-link robot, which showed excellent learning, adaptation, and ability to perform complex rehabilitation tasks with precise trajectory tracking. Further tests evaluated the MRAC's robustness against introduced human deviations, demonstrating its resilience and adaptability. These findings highlight LfTD’s potential to improve upper limb robotic-assisted rehabilitation through precise, adaptable motion replication, setting the stage for clinical trials with actual robots.