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Mendeley readers
Attention Score in Context
| Title |
Recognition of Handwriting from Electromyography
|
|---|---|
| Published in |
PLOS ONE, August 2009
|
| DOI | 10.1371/journal.pone.0006791 |
| Pubmed ID | |
| Authors |
Michael Linderman, Mikhail A. Lebedev, Joseph S. Erlichman |
| Abstract |
Handwriting--one of the most important developments in human culture--is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals - the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear. |
Mendeley readers
The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Indonesia | 1 | <1% |
| Norway | 1 | <1% |
| India | 1 | <1% |
| Brazil | 1 | <1% |
| United Kingdom | 1 | <1% |
| Philippines | 1 | <1% |
| Unknown | 126 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 18% |
| Student > Master | 22 | 16% |
| Researcher | 15 | 11% |
| Student > Bachelor | 14 | 10% |
| Student > Doctoral Student | 12 | 9% |
| Other | 27 | 20% |
| Unknown | 20 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 31 | 23% |
| Computer Science | 24 | 18% |
| Agricultural and Biological Sciences | 11 | 8% |
| Medicine and Dentistry | 10 | 7% |
| Psychology | 8 | 6% |
| Other | 30 | 22% |
| Unknown | 20 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 07 June 2021.
All research outputs
#1,607,356
of 22,715,151 outputs
Outputs from PLOS ONE
#20,848
of 193,929 outputs
Outputs of similar age
#4,840
of 92,241 outputs
Outputs of similar age from PLOS ONE
#69
of 520 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 193,929 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has done well, scoring higher than 89% 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 92,241 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 520 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.