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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Determining grasp selection from arm trajectories via deep learning to enable functional hand movement in tetraplegia
|
|---|---|
| Published in |
Bioelectronic Medicine, August 2020
|
| DOI | 10.1186/s42234-020-00053-5 |
| Pubmed ID | |
| Authors |
Nikunj Bhagat, Kevin King, Richard Ramdeo, Adam Stein, Chad Bouton |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 6 | 21% |
| Researcher | 6 | 21% |
| Student > Ph. D. Student | 3 | 10% |
| Student > Master | 3 | 10% |
| Professor | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 7 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 7 | 24% |
| Unspecified | 6 | 21% |
| Computer Science | 2 | 7% |
| Agricultural and Biological Sciences | 1 | 3% |
| Social Sciences | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 9 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 35. 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 10 April 2021.
All research outputs
#1,195,027
of 26,216,692 outputs
Outputs from Bioelectronic Medicine
#20
of 148 outputs
Outputs of similar age
#32,620
of 428,430 outputs
Outputs of similar age from Bioelectronic Medicine
#2
of 5 outputs
Altmetric has tracked 26,216,692 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 148 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.0. This one has done well, scoring higher than 86% 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 428,430 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 92% of its contemporaries.
We're also able to compare this research output to 5 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.