Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15430
Title: Effects of electromyography-driven robot-aided hand training with neuromuscular electrical stimulation on hand control performance after chronic stroke
Authors: Rong, W
Tong, KY
Hu, XL
Ho, SK
Keywords: Biofeedback
Neurorehabilitation
Sensorimotor integration
Issue Date: 2015
Publisher: Informa Healthcare
Source: Disability and rehabilitation: Assistive technology, 2015, v. 10, no. 2, p. 149-159 How to cite?
Journal: Disability and Rehabilitation: Assistive Technology 
Abstract: Purpose: An electromyography-driven robot system integrated with neuromuscular electrical stimulation (NMES) was developed to investigate its effectiveness on post-stroke rehabilitation. Methods: The performance of this system in assisting finger flexion/extension with different assistance combinations was evaluated in five stroke subjects. Then, a pilot study with 20-sessions training was conducted to evaluate the training's effectiveness. Results: The results showed that combined assistance from the NMES-robot could improve finger movement accuracy, encourage muscle activation of the finger muscles and suppress excessive muscular activities in the elbow joint. When assistances from both NMES and the robot were 50% of their maximum assistances, finger-tracking performance had the best results, with the lowest root mean square error, greater range of motion, higher voluntary muscle activations of the finger joints and lower muscle co-contraction in the finger and elbow joints. Upper limb function improved after the 20-session training, indicated by the increased clinical scores of Fugl-Meyer Assessment, Action Research Arm Test and Wolf Motor Function Test. Muscle co-contraction was reduced in the finger and elbow joints reflected by the Modified Ashworth Scale. Conclusions: The findings demonstrated that an electromyography-driven NMES-robot used for chronic stroke improved hand function and tracking performance. Further research is warranted to validate the method on a larger scale.
URI: http://hdl.handle.net/10397/15430
ISSN: 1748-3107
DOI: 10.3109/17483107.2013.873491
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