Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation

This paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords (‘upper limb*’ or ‘upper body*’) and (‘rehabilitation*’ or ‘treatment*’) and (‘robot*’ or ‘device*’ or ‘exoskeleton*’). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users

Robotic Fingers in Reach-to-Grasp Tasks of Rehabilitation

The REHAROB robotic upper limb rehabilitation system was improved with a custom-designed and developed hand/finger therapy module. The new module extends the scope of the applicable motion therapy from passive to active reach-to-grasp activities of daily living tasks, and the range of treated anatomical joints was also extended to every proximal and distal upper limb anatomical joint. Finger exercising and object grasping are supported with a pair of two degree-of-freedom (DOF) robotic fingers. One of the robotic fingers moves the index/middle/ring fingers together, whereas the other robotic finger moves the thumb. A novel hypothesis was established, analyzed, and tested for setting the orientation of the robotic finger moving the thumb. The robotic thumb is not aligned with the patient's thumb; its orientation is optimized in the patient's hand reference system to maximize the efficiency in the opposite grasping task. While most concurrent systems utilize virtual objects for grasping tasks, the REHAROB system exercises five carefully selected reach-and-grasp type activities of daily living (ADL) with real objects. Actuating the human finger phalanges through custom development finger orthoses is described. An advanced feature of the hand/finger therapy module is the left-right hand side changeover by only alternating the orientation of the robotic fingers and exchanging the finger orthoses

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

Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on 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 paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, 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