| Title |
Explaining the unique nature of individual gait patterns with deep learning
|
|---|---|
| Published in |
Scientific Reports, February 2019
|
| DOI | 10.1038/s41598-019-38748-8 |
| Pubmed ID | |
| Authors |
Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller, Wolfgang I. Schöllhorn |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 7% |
| United States | 1 | 7% |
| Taiwan | 1 | 7% |
| France | 1 | 7% |
| Unknown | 11 | 73% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 67% |
| Scientists | 5 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 356 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 356 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 69 | 19% |
| Student > Master | 61 | 17% |
| Researcher | 57 | 16% |
| Student > Bachelor | 24 | 7% |
| Student > Doctoral Student | 18 | 5% |
| Other | 49 | 14% |
| Unknown | 78 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 109 | 31% |
| Computer Science | 46 | 13% |
| Sports and Recreations | 17 | 5% |
| Neuroscience | 14 | 4% |
| Medicine and Dentistry | 12 | 3% |
| Other | 48 | 13% |
| Unknown | 110 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 01 June 2020.
All research outputs
#3,396,720
of 25,872,466 outputs
Outputs from Scientific Reports
#28,962
of 143,626 outputs
Outputs of similar age
#71,599
of 368,966 outputs
Outputs of similar age from Scientific Reports
#1,046
of 4,317 outputs
Altmetric has tracked 25,872,466 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 143,626 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has done well, scoring higher than 79% 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 368,966 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 80% of its contemporaries.
We're also able to compare this research output to 4,317 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.