Using Orientation Sensors to Control a FES System for Upper-Limb Motor Rehabilitation

2018 ◽  
pp. 95-105
Author(s):  
Andrés F. Ruíz-Olaya ◽  
Alberto López-Delis ◽  
Adson Ferreira da Rocha
Keyword(s):  
Upper Limb ◽  
Motor 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.

scholarly journals Clinical Application of Virtual Reality for Upper Limb Motor Rehabilitation in Stroke: Review of Technologies and Clinical Evidence

2020 ◽  
Vol 9 (10) ◽  
pp. 3369
Author(s):  
Won-Seok Kim ◽  
Sungmin Cho ◽  
Jeonghun Ku ◽  
Yuhee Kim ◽  
Kiwon Lee ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Clinical Application ◽  
Upper Limb ◽  
Stroke Rehabilitation ◽  
Cochrane Library ◽  
Motor Rehabilitation ◽  
Novel Technologies ◽  
Ovid Medline ◽  
Meta Analyses ◽  
The Cochrane Library

Neurorehabilitation for stroke is important for upper limb motor recovery. Conventional rehabilitation such as occupational therapy has been used, but novel technologies are expected to open new opportunities for better recovery. Virtual reality (VR) is a technology with a set of informatics that provides interactive environments to patients. VR can enhance neuroplasticity and recovery after a stroke by providing more intensive, repetitive, and engaging training due to several advantages, including: (1) tasks with various difficulty levels for rehabilitation, (2) augmented real-time feedback, (3) more immersive and engaging experiences, (4) more standardized rehabilitation, and (5) safe simulation of real-world activities of daily living. In this comprehensive narrative review of the application of VR in motor rehabilitation after stroke, mainly for the upper limbs, we cover: (1) the technologies used in VR rehabilitation, including sensors; (2) the clinical application of and evidence for VR in stroke rehabilitation; and (3) considerations for VR application in stroke rehabilitation. Meta-analyses for upper limb VR rehabilitation after stroke were identified by an online search of Ovid-MEDLINE, Ovid-EMBASE, the Cochrane Library, and KoreaMed. We expect that this review will provide insights into successful clinical applications or trials of VR for motor rehabilitation after stroke.

Use of inertial sensors as devices for upper limb motor monitoring exercises for motor rehabilitation

2015 ◽  
Vol 5 (2) ◽  
pp. 91-102 ◽  
Author(s):  
Alexandre Balbinot ◽  
Jonas Crauss Rodrigues de Freitas ◽  
Daniel Santos Côrrea
Keyword(s):  
Upper Limb ◽  
Inertial Sensors ◽  
Motor Rehabilitation

scholarly journals Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report

2019 ◽  
Vol 13 ◽  
Author(s):  
Athanasios Vourvopoulos ◽  
Carolina Jorge ◽  
Rodolfo Abreu ◽  
Patrícia Figueiredo ◽  
Jean-Claude Fernandes ◽  
...  
Keyword(s):  
Case Report ◽  
Brain Imaging ◽  
Motor Imagery ◽  
Upper Limb ◽  
Clinical Case ◽  
Motor Rehabilitation

UPPER LIMB MOTOR REHABILITATION INTEGRATED WITH VIDEO GAMES FOCUSING ON TRAINING FINGERS’ FINE MOVEMENTS

2014 ◽  
Vol 29 (4) ◽  
Author(s):  
Chong Li ◽  
Zoltán Rusák ◽  
Yuemin Hou ◽  
Christopher Young ◽  
Linhong Ji
Keyword(s):  
Video Games ◽  
Upper Limb ◽  
Motor Rehabilitation

scholarly journals Determining the Accuracy of Oculus Touch Controllers for Motor Rehabilitation Applications Using Quantifiable Upper Limb Kinematics: Validation Study (Preprint)

2018 ◽  
Author(s):  
Leia C Shum ◽  
Bulmaro A Valdés ◽  
HF Machiel Van der Loos
Keyword(s):  
Upper Limb ◽  
Relative Position ◽  
Motion Tracking ◽  
Tracking System ◽  
Motor Rehabilitation ◽  
Step Size ◽  
Position Accuracy ◽  
Upper Limb Movements ◽  
Motion Tracking System ◽  
Play Space

BACKGROUND As commercial motion tracking technology becomes more readily available, it is necessary to evaluate the accuracy of these systems before using them for biomechanical and motor rehabilitation applications. OBJECTIVE This study aimed to evaluate the relative position accuracy of the Oculus Touch controllers in a 2.4 x 2.4 m play-space. METHODS Static data samples (n=180) were acquired from the Oculus Touch controllers at step sizes ranging from 5 to 500 mm along 16 different points on the play-space floor with graph paper in the x (width), y (height), and z (depth) directions. The data were compared with reference values using measurements from digital calipers, accurate to 0.01 mm; physical blocks, for which heights were confirmed with digital calipers; and for larger step sizes (300 and 500 mm), a ruler with hatch marks to millimeter units. RESULTS It was found that the maximum position accuracy error of the system was 3.5 ± 2.5 mm at the largest step size of 500 mm along the z-axis. When normalized to step size, the largest error found was 12.7 ± 9.9% at the smallest step size in the y-axis at 6.23 mm. When the step size was <10 mm in any direction, the relative position accuracy increased considerably to above 2% (approximately 2 mm at maximum). An average noise value of 0.036 mm was determined. A comparison of these values to cited visual, goniometric, and proprioceptive resolutions concludes that this system is viable for tracking upper-limb movements for biomechanical and rehabilitation applications. The accuracy of the system was also compared with accuracy values from previous studies using other commercially available devices and a multicamera, marker-based professional motion tracking system. CONCLUSIONS The study found that the linear position accuracy of the Oculus Touch controllers was within an agreeable range for measuring human kinematics in rehabilitative upper-limb exercise protocols. Further testing is required to ascertain acceptable repeatability in multiple sessions and rotational accuracy.

Effectiveness of Sensory and Motor Rehabilitation of the Upper Limb Following the Principles of Neuroplasticity: Patients Stable Poststroke

2003 ◽  
Vol 17 (3) ◽  
pp. 176-191 ◽  
Author(s):  
Nancy Byl ◽  
Jennifer Roderick ◽  
Olfat Mohamed ◽  
Monica Hanny ◽  
Josh Kotler ◽  
...  
Keyword(s):  
Upper Limb ◽  
Motor Rehabilitation
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