http://www.jneuroengrehab.com/content/12/1/42
1
Interdisciplinary Division of Biomedical Engineering, The Hong Kong
Polytechnic University, Hong Kong, S.A.R., China
2 Department of Electronic Engineering Division of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, S.A.R., China
2 Department of Electronic Engineering Division of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, S.A.R., China
Journal of NeuroEngineering and Rehabilitation 2015, 12:42
doi:10.1186/s12984-015-0033-5
The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/12/1/42
The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/12/1/42
| Received: | 29 December 2014 |
| Accepted: | 10 April 2015 |
| Published: | 25 April 2015 |
© 2015 Susanto et al.; licensee BioMed Central.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Abstract
Background
While constraint-induced movement therapy (CIMT) is one of the most promising techniques
for upper limb rehabilitation after stroke, it requires high residual function to
start with. Robotic device, on the other hand, can provide intention-driven assistance
and is proven capable to complement conventional therapy. However, with many robotic
devices focus on more proximal joints like shoulder and elbow, recovery of hand and
fingers functions have become a challenge. Here we propose the use of robotic device
to assist hand and fingers functions training and we aim to evaluate the potential
efficacy of intention-driven robot-assisted fingers training.
Methods
Participants (6 to 24 months post-stroke) were randomly assigned into two groups:
robot-assisted (robot) and non-assisted (control) fingers training groups. Each participant
underwent 20-session training. Action Research Arm Test (ARAT) was used as the primary
outcome measure, while, Wolf Motor Function Test (WMFT) score, its functional tasks
(WMFT-FT) sub-score, Fugl-Meyer Assessment (FMA), its shoulder and elbow (FMA-SE)
sub-score, and finger individuation index (FII) served as secondary outcome measures.
Results
Nineteen patients completed the 20-session training (Trial Registration: HKClinicalTrials.com
HKCTR-1554); eighteen of them came back for a 6-month follow-up. Significant improvements
(p < 0.05) were found in the clinical scores for both robot and control group after
training. However, only robot group maintained the significant difference in the ARAT
and FMA-SE six months after the training. The WMFT-FT score and time post-training
improvements of robot group were significantly better than those of the control group.
Conclusions
This study showed the potential efficacy of robot-assisted fingers training for hand
and fingers rehabilitation and its feasibility to facilitate early rehabilitation
for a wider population of stroke survivors; and hence, can be used to complement CIMT.
No comments:
Post a Comment