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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Voluntary EMG-to-force estimation with a multi-scale physiological muscle model
|
|---|---|
| Published in |
BioMedical Engineering OnLine, September 2013
|
| DOI | 10.1186/1475-925x-12-86 |
| Pubmed ID | |
| Authors |
Mitsuhiro Hayashibe, David Guiraud |
| Abstract |
EMG-to-force estimation based on muscle models, for voluntary contraction has many applications in human motion analysis. The so-called Hill model is recognized as a standard model for this practical use. However, it is a phenomenological model whereby muscle activation, force-length and force-velocity properties are considered independently. Perreault reported Hill modeling errors were large for different firing frequencies, level of activation and speed of contraction. It may be due to the lack of coupling between activation and force-velocity properties. In this paper, we discuss EMG-force estimation with a multi-scale physiology based model, which has a link to underlying crossbridge dynamics. Differently from the Hill model, the proposed method provides dual dynamics of recruitment and calcium activation. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 2% |
| Malaysia | 1 | 1% |
| Ireland | 1 | 1% |
| Brazil | 1 | 1% |
| Sri Lanka | 1 | 1% |
| Unknown | 83 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 25% |
| Student > Master | 16 | 18% |
| Researcher | 13 | 15% |
| Student > Bachelor | 6 | 7% |
| Other | 5 | 6% |
| Other | 12 | 13% |
| Unknown | 15 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 43 | 48% |
| Computer Science | 4 | 4% |
| Medicine and Dentistry | 4 | 4% |
| Sports and Recreations | 4 | 4% |
| Agricultural and Biological Sciences | 3 | 3% |
| Other | 10 | 11% |
| Unknown | 21 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 05 September 2013.
All research outputs
#3,923,197
of 22,719,618 outputs
Outputs from BioMedical Engineering OnLine
#89
of 822 outputs
Outputs of similar age
#34,776
of 196,871 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 9 outputs
Altmetric has tracked 22,719,618 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.6. 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 196,871 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them