Adaptive Modeling and Control of an Upper-Limb Rehabilitation Robot Using RBF Neural Networks

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

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A scoping review of design requirements for a home-based upper limb rehabilitation robot for stroke

Topics in Stroke Rehabilitation ◽

10.1080/10749357.2021.1943797 ◽

2021 ◽

pp. 1-15

Author(s):

Lutong Li ◽

Qiang Fu ◽

Sarah Tyson ◽

Nick Preston ◽

Andrew Weightman

Keyword(s):

Upper Limb ◽

Scoping Review ◽

Rehabilitation Robot ◽

Upper Limb Rehabilitation ◽

Home Based ◽

Design Requirements ◽

Limb Rehabilitation

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Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot

Technology and Health Care ◽

10.3233/thc-202633 ◽

2020 ◽

pp. 1-17

Author(s):

Qing Sun ◽

Shuai Guo ◽

Leigang Zhang

Keyword(s):

Upper Limb ◽

Constraint Equation ◽

Human Robot Interaction ◽

Rehabilitation Robot ◽

Connecting Rod ◽

Robot Interaction ◽

Upper Limb Rehabilitation ◽

Rehabilitation Training ◽

Limb Rehabilitation ◽

Training Trajectory

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.

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