Machine Learning in Robot-Assisted Upper Limb Rehabilitation: A Focused Review
Qingsong Ai, Zemin Liu, Wei Meng, Quan Liu, Sheng Quan Xie
Abstract
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.