scholarly journals Novel Upper-Limb Rehabilitation System Based on Attention Technology for Post-Stroke Patients: A Preliminary Study

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 2720-2731 ◽  
Author(s):  
Bor-Shing Lin ◽  
Jean-Lon Chen ◽  
Hsiu-Chi Hsu
Keyword(s):  
Upper Limb ◽  
Stroke Patients ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Rehabilitation System ◽  
Preliminary Study ◽  
Limb Rehabilitation

scholarly journals Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

2019 ◽  
Vol 9 (8) ◽  
pp. 1620 ◽  
Author(s):  
Bai ◽  
Song ◽  
Li
Keyword(s):  
Upper Limb ◽  
Motion Tracking ◽  
Stroke Patients ◽  
Mean Velocity ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Post Stroke ◽  
Reachable Workspace ◽  
Rehabilitation System ◽  
Limb Rehabilitation

In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.

scholarly journals Biomechanical Assessment of Reaching Movements in Post-Stroke Patients During a Robot-Aided Rehabilitation

2011 ◽  
Vol 8 (1) ◽  
pp. 39-54 ◽  
Author(s):  
S. Mazzoleni ◽  
F. Posteraro ◽  
M. Filippi ◽  
F. Forte ◽  
S. Micera ◽  
...  
Keyword(s):  
Upper Limb ◽  
Quantitative Methods ◽  
Point Of View ◽  
Reaching Movements ◽  
Stroke Patients ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Biomechanical Assessment ◽  
Limb Rehabilitation ◽  
Force Data

The main goal of this paper is to describe a method for the assessment of the motor performance in post-stroke subjects who have been undergone a robot-aided upper limb rehabilitation treatment. The motivation for adopting such methodology relies on the need of quantitative methods for the evaluation of the effects of robot-aided rehabilitation treatments, which assumes great importance from the clinical point of view. The method is based on the analysis of biomechanical parameters computed from force data recorded during the execution of planar reaching movements. Data from 17 chronic post-stroke patients and 5 healthy subjects were analysed. The results show the effectiveness of the proposed method, which can contribute to quantitatively evaluate the effects of a robot-mediated therapy on the upper limb of chronic post-stroke subjects.

Developing an mHealth App for Post-stroke Upper Limb Rehabilitation: Feedback from U.S. and Ethiopian Rehabilitation Clinicians (Preprint)

2018 ◽  
Author(s):  
Charmayne Hughes ◽  
Alejandra Padilla ◽  
Amy Hintze ◽  
Tatiana Mariscal ◽  
Michael Sera ◽  
...  
Keyword(s):  
Upper Limb ◽  
Cultural Context ◽  
Online Survey ◽  
Design Features ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Rehabilitation System ◽  
Survey Results ◽  
Limb Rehabilitation ◽  
Rehabilitation Clinic

UNSTRUCTURED Stroke is the leading cause of adult disability worldwide, with 70% of survivors exhibiting residual impairments of the upper limb that require frequent in-person visits to rehabilitation clinic over several months. This study explored rehabilitation clinician’s preferences for design features to be included in an mHealth-enabled app for post-stroke upper-limb rehabilitation. Data were collected via online survey, sampling participants from Ethiopia (n = 37) and the U.S. (n = 40). Survey results indicated that Ethiopian and U.S. rehabilitation clinicians have different opinions about the importance of design features that should be included in a stroke tele-rehabilitation system which are likely due to differences in culture, the availability of human and physical resources, and how the field of rehabilitation is organized and managed. Our results, thus, indicate that mHealth technologies but must be tailored to the geographical and cultural context of the end-users. 

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