Considerations for virtual environments for upper limb rehabilitation tasks

2014 ◽  
Author(s):  
V. Powell ◽  
W. Powell ◽  
M. Simmonds
Keyword(s):  
Virtual Environments ◽  
Upper Limb ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation ◽  
Rehabilitation Tasks

Upper Limb Rehabilitation with Virtual Environments

2019 ◽  
pp. 330-343
Author(s):  
Gustavo Caiza ◽  
Cinthya Calapaqui ◽  
Fabricio Regalado ◽  
Lenin F. Saltos ◽  
Carlos A. Garcia ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Upper Limb ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

scholarly journals Using UHF RFID Properties to Develop and Optimize an Upper-Limb Rehabilitation System

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3224 ◽  
Author(s):  
Walter Baccinelli ◽  
Maria Bulgheroni ◽  
Carlo Albino Frigo
Keyword(s):  
Upper Limb ◽  
Daily Living ◽  
Performance Metrics ◽  
Uhf Rfid ◽  
Upper Limb Rehabilitation ◽  
Home Based ◽  
Number Of Patients ◽  
Rehabilitation System ◽  
Limb Rehabilitation ◽  
Rehabilitation Tasks

Rehabilitation of the upper limb is an important aspect of the therapy for people affected by neuromotor diseases for the recovery of the capability to perform activities of daily living (ADLs). Nonetheless, the costs associated with the administration of rehabilitation therapy and the increasing number of patients highlight the need for new solutions. Technology-based solutions and, in particular, telerehabilitation could strongly impact in this field. In this paper, a new system based on radiofrequency (RF) technology is presented which is able to effectively provide home-based telerehabilitation and extract meaningful information on the therapy execution performance. The technology has been tuned to the needs of the rehabilitation system, optimizing the hardware, the communication protocol and the software control. A methodology for extracting the execution time of the rehabilitation tasks, the distance covered by the patient’s hand in each subtask and the velocity profile is presented. The results show that a highly usable system for the rehabilitation of the upper limb has been developed using the RF technology and that performance metrics can be reliably extracted by the acquired signals.

Upper-limb Rehabilitation Robot Based on Motor Imagery EEG

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

Upper-limb Rehabilitation Robot Motion Control Based on Dynamic Interpolation

ROBOT ◽  
2012 ◽  
Vol 34 (5) ◽  
pp. 539 ◽  
Author(s):  
Lizheng PAN ◽  
Aiguo SONG ◽  
Guozheng XU ◽  
Huijun LI ◽  
Baoguo XU
Keyword(s):  
Motion Control ◽  
Upper Limb ◽  
Robot Motion ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

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

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

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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