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Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient

Overview of attention for article published in Frontiers in Neurology, March 2017
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Title
Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient
Published in
Frontiers in Neurology, March 2017
DOI 10.3389/fneur.2017.00107
Pubmed ID
Authors

Zhiyuan Lu, Kai-yu Tong, Henry Shin, Sheng Li, Ping Zhou

Abstract

A hand exoskeleton driven by myoelectric pattern recognition was designed for stroke rehabilitation. It detects and recognizes the user's motion intent based on electromyography (EMG) signals, and then helps the user to accomplish hand motions in real time. The hand exoskeleton can perform six kinds of motions, including the whole hand closing/opening, tripod pinch/opening, and the "gun" sign/opening. A 52-year-old woman, 8 months after stroke, made 20× 2-h visits over 10 weeks to participate in robot-assisted hand training. Though she was unable to move her fingers on her right hand before the training, EMG activities could be detected on her right forearm. In each visit, she took 4× 10-min robot-assisted training sessions, in which she repeated the aforementioned six motion patterns assisted by our intent-driven hand exoskeleton. After the training, her grip force increased from 1.5 to 2.7 kg, her pinch force increased from 1.5 to 2.5 kg, her score of Box and Block test increased from 3 to 7, her score of Fugl-Meyer (Part C) increased from 0 to 7, and her hand function increased from Stage 1 to Stage 2 in Chedoke-McMaster assessment. The results demonstrate the feasibility of robot-assisted training driven by myoelectric pattern recognition after stroke.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 118 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 17%
Student > Master 10 8%
Student > Doctoral Student 10 8%
Student > Bachelor 10 8%
Researcher 8 7%
Other 14 12%
Unknown 47 39%
Readers by discipline Count As %
Engineering 36 30%
Nursing and Health Professions 7 6%
Medicine and Dentistry 5 4%
Computer Science 4 3%
Neuroscience 4 3%
Other 14 12%
Unknown 49 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 March 2017.
All research outputs
#20,411,380
of 22,961,203 outputs
Outputs from Frontiers in Neurology
#8,864
of 11,842 outputs
Outputs of similar age
#269,893
of 309,705 outputs
Outputs of similar age from Frontiers in Neurology
#122
of 154 outputs
Altmetric has tracked 22,961,203 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,842 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 309,705 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.