Upper Limb Rehabilitation Using a Planar Cable-Driven Parallel Robot with Various Rehabilitation Strategies

2014 ◽  
pp. 307-321 ◽  
Author(s):  
XueJun Jin ◽  
Dae Ik Jun ◽  
Xuemei Jin ◽  
Jeongan Seon ◽  
Andreas Pott ◽  
...  
Keyword(s):  
Upper Limb ◽  
Parallel Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation ◽  
Rehabilitation Strategies

Innovative development of a spherical parallel robot for upper limb rehabilitation

2018 ◽  
Vol 4 (4) ◽  
pp. 256 ◽  
Author(s):  
Calin Vaida ◽  
Nicolae Plitea ◽  
Giuseppe Carbone ◽  
Iosif Birlescu ◽  
Ionut Ulinici ◽  
...  
Keyword(s):  
Upper Limb ◽  
Parallel Robot ◽  
Innovative Development ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

Innovative development of a spherical parallel robot for upper limb rehabilitation

2018 ◽  
Vol 4 (4) ◽  
pp. 256
Author(s):  
Doina Pisla ◽  
Calin Vaida ◽  
Nicolae Plitea ◽  
Adrian Pisla ◽  
Ionut Ulinici ◽  
...  
Keyword(s):  
Upper Limb ◽  
Parallel Robot ◽  
Innovative Development ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

scholarly journals Risk-Based Assessment Engineering of a Parallel Robot Used in Post-Stroke Upper Limb Rehabilitation

2019 ◽  
Vol 11 (10) ◽  
pp. 2893 ◽  
Author(s):  
Paul Tucan ◽  
Calin Vaida ◽  
Nicolae Plitea ◽  
Adrian Pisla ◽  
Giuseppe Carbone ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Upper Limb ◽  
Parallel Robot ◽  
Hazard Identification ◽  
Assessment Process ◽  
Measuring System ◽  
Upper Limb Rehabilitation ◽  
Risk Assessment Process ◽  
Reliable Solution ◽  
Limb Rehabilitation

Recently, robotic-assisted stroke rehabilitation became an important research topic due to its capability to provide complex solutions to perform the customized rehabilitation motion with enhanced resources than the traditional rehabilitation. Involving robotic devices in the rehabilitation process would increase the number of possible rehabilitated patients, but placing the patient inside the workspace of the robot causes a series of risks that needs to be identified, analyzed and avoided. The goal of this work is to provide a reliable solution for an upper limb rehabilitation robotic structure designed as a result of a risk assessment process. The proposed approach implies a hazard identification process in terms of severity and probability, a failure mode and effects analysis to identify the possible malfunctions in the system and an AHP (Analytic Hierarchy Process) to prioritize the technical characteristics of the robotic structure. The results of the risk assessment process and of the AHP provide the base of the final design of the robotic structure, while another solution, in terms of minimizing the risk for the patient injury, is obtained using an external measuring system.

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 Sensitivity Based Selection of an Optimal Cable-Driven Parallel Robot Design for Rehabilitation Purposes

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Ferdaws Ennaiem ◽  
Abdelbadiâ Chaker ◽  
Juan Sebastián Sandoval Arévalo ◽  
Med Amine Laribi ◽  
Sami Bennour ◽  
...  
Keyword(s):  
Pareto Front ◽  
Parallel Robot ◽  
Elastic Stiffness ◽  
Upper Limb Rehabilitation ◽  
Daily Life Activities ◽  
System A ◽  
The Monte Carlo Method ◽  
Genetic Algorithm Method ◽  
Limb Rehabilitation ◽  
Selection Of

This paper deals with the design of an optimal cable-driven parallel robot (CDPR) for upper limb rehabilitation. The robot’s prescribed workspace is identified with the help of an occupational therapist based on three selected daily life activities, which are tracked using a Qualisys motion capture system. A preliminary architecture of the robot is proposed based on the analysis of the tracked trajectories of all the activities. A multi-objective optimization process using the genetic algorithm method is then performed, where the cable tensions and the robot size are selected as the objective functions to be minimized. The cables tensions are bounded between two limits, where the lower limit ensures a positive tension in the cables at all times and the upper limit represents the maximum torque of the motor. A sensitivity analysis is then performed using the Monte Carlo method to yield the optimal design selected out of the non-dominated solutions, forming the obtained Pareto front. The robot with the highest robustness toward the disturbances is identified, and its dexterity and elastic stiffness are calculated to investigate its performance.

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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