Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors

卷:21

Stefano Mazzoleni¹  Mauro Callejas-Cuervo²  Manuel Andrés Vélez-Guerrero³ 

[1] Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy;

[2] School of Computer Science, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;

[3] Software Research Group, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;

关键词: robotic exoskeletons;    wearable devices;    artificial intelligence (AI);    artificial neural networks (ANN);    adaptive algorithms;    upper limbs;   

DOI  :  10.3390/s21062146

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown