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A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, January 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
154 Dimensions

Readers on

mendeley
208 Mendeley
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Title
A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees
Published in
Journal of NeuroEngineering and Rehabilitation, January 2017
DOI 10.1186/s12984-016-0212-z
Pubmed ID
Authors

Xiangxin Li, Oluwarotimi Williams Samuel, Xu Zhang, Hui Wang, Peng Fang, Guanglin Li

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 208 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 208 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 15%
Student > Master 30 14%
Student > Bachelor 25 12%
Researcher 19 9%
Student > Doctoral Student 12 6%
Other 29 14%
Unknown 62 30%
Readers by discipline Count As %
Engineering 80 38%
Computer Science 18 9%
Neuroscience 10 5%
Medicine and Dentistry 8 4%
Sports and Recreations 3 1%
Other 18 9%
Unknown 71 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 October 2020.
All research outputs
#7,646,569
of 23,281,392 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#508
of 1,302 outputs
Outputs of similar age
#142,547
of 422,774 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#9
of 12 outputs
Altmetric has tracked 23,281,392 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,302 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 57% of its peers.
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 422,774 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.