Machine Learning in Robot-Assisted Upper Limb Rehabilitation: A Focused Review

• Published in: IEEE transactions on cognitive and developmental systems Vol. 15; no. 4; pp. 2053 - 2063
• Main Authors: Ai, Qingsong, Liu, Zemin, Meng, Wei, Liu, Quan, Xie, Sheng Q.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-12-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; End effectors; Estimation; Exoskeletons; Human motion; Human–robot interaction (HRI); intention recognition; Machine learning; quantitative assessment; Rehabilitation; Rehabilitation robotics; Rehabilitation robots; Robot control; Robot learning; Robot sensing systems; Robots; Training; upper limb rehabilitation
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2021.3098350

Robot-assisted rehabilitation, which can provide repetitive, intensive, and high-precision physics training, has a positive influence on the motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this article, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. First, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control, and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices.