scholarly journals Developing an mHealth app for post-stroke upper limb rehabilitation: Feedback from US and Ethiopian rehabilitation clinicians

2019 ◽  
Vol 26 (2) ◽  
pp. 1104-1117 ◽  
Author(s):  
Charmayne Mary Lee Hughes ◽  
Alejandra Padilla ◽  
Amy Hintze ◽  
Tatiana Mariscal Raymundo ◽  
Michael Sera ◽  
...  
Keyword(s):  
Upper Limb ◽  
Cultural Context ◽  
Online Survey ◽  
The United States ◽  
Design Features ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Survey Results ◽  
Limb Rehabilitation ◽  
Rehabilitation Clinic

Stroke is the leading cause of adult disability worldwide, with 70 percent 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 = 69) and the United States ( n = 75). Survey results indicated that Ethiopian and US 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 must be tailored to the geographical and cultural context of the end users.

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. 

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 Does kinematics add meaningful information to clinical assessment in post-stroke upper limb rehabilitation? A case report

2016 ◽  
Vol 28 (8) ◽  
pp. 2408-2413 ◽  
Author(s):  
Matteo Bigoni ◽  
Silvia Baudo ◽  
Veronica Cimolin ◽  
Nicola Cau ◽  
Manuela Galli ◽  
...  
Keyword(s):  
Case Report ◽  
Upper Limb ◽  
Clinical Assessment ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Meaningful Information ◽  
Limb Rehabilitation
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