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X Demographics
Mendeley readers
Attention Score in Context
| Title |
EMG-based facial gesture recognition through versatile elliptic basis function neural network
|
|---|---|
| Published in |
BioMedical Engineering OnLine, July 2013
|
| DOI | 10.1186/1475-925x-12-73 |
| Pubmed ID | |
| Abstract |
Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 1% |
| France | 1 | 1% |
| Unknown | 67 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 18 | 26% |
| Student > Ph. D. Student | 17 | 25% |
| Student > Bachelor | 7 | 10% |
| Student > Doctoral Student | 3 | 4% |
| Other | 3 | 4% |
| Other | 11 | 16% |
| Unknown | 10 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 27 | 39% |
| Computer Science | 12 | 17% |
| Neuroscience | 4 | 6% |
| Psychology | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Other | 9 | 13% |
| Unknown | 13 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 19 July 2013.
All research outputs
#14,110,001
of 22,714,025 outputs
Outputs from BioMedical Engineering OnLine
#365
of 822 outputs
Outputs of similar age
#96,115
of 172,131 outputs
Outputs of similar age from BioMedical Engineering OnLine
#4
of 7 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 55% 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 172,131 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.