A Control Framework for Adaptation of Training Task and Robotic Assistance for Promoting Motor Learning With an Upper Limb Rehabilitation Robot

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 12; pp. 7737 - 7747
• Main Authors: Wang, Chen, Peng, Liang, Hou, Zeng-Guang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive control; Assist as needed (AAN); Assistive robots; Feature extraction; Feedback control; Feedforward control; Learning; motor learning; nonlinear adaptive control; Oscillators; Performance evaluation; Radial basis function; Real-time systems; Rehabilitation; Rehabilitation robotics; Rehabilitation robots; Robot control; Robotics; Robots; Trajectory; Trajectory optimization
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2022.3163916

Robot-assisted rehabilitation has been a promising solution to improve motor learning of neurologically impaired patients. State-of-the-art control strategies are typically limited to the ignorance of heterogeneous motor capabilities of poststroke patients and therefore intervene suboptimally. In this article, we propose a control framework for robot-assisted motor learning, emphasizing the detection of human intention, generation of reference trajectories, and modification of robotic assistance. A real-time trajectory generation algorithm is presented to extract the high-level features in active arm movements using an adaptive frequency oscillator (AFO) and then integrate the movement rhythm with the minimum-jerk principle to generate an optimal reference trajectory, which synchronizes with the motion intention in the patient as well as the motion pattern in healthy humans. In addition, a subject-adaptive assistance modification algorithm is presented to model the patient's residual motor capabilities employing spatially dependent radial basis function (RBF) networks and then combining the RBF-based feedforward controller with the impedance feedback controller to provide only necessary assistance while simultaneously regulating the maximum-tolerated error during trajectory tracking tasks. We conduct simulation and experimental studies based on an upper limb rehabilitation robot to evaluate the overall performance of the motor-learning framework. A series of results showed that the difficulty level of reference trajectories was modulated to meet the requirements of subjects' intended motion, furthermore, the robotic assistance was compliantly optimized in response to the changing performance of subjects' motor abilities, highlighting the potential of adopting our framework into clinical application to promote patient-led motor learning.