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Modeling long-term human activeness using recurrent neural networks for biometric data

Overview of attention for article published in BMC Medical Informatics and Decision Making, May 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
29 Mendeley
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Title
Modeling long-term human activeness using recurrent neural networks for biometric data
Published in
BMC Medical Informatics and Decision Making, May 2017
DOI 10.1186/s12911-017-0453-1
Pubmed ID
Authors

Zae Myung Kim, Hyungrai Oh, Han-Gyu Kim, Chae-Gyun Lim, Kyo-Joong Oh, Ho-Jin Choi

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 %
Student > Ph. D. Student 9 31%
Researcher 7 24%
Student > Bachelor 6 21%
Student > Doctoral Student 2 7%
Student > Master 1 3%
Other 1 3%
Unknown 3 10%
Readers by discipline Count As %
Computer Science 6 21%
Medicine and Dentistry 4 14%
Engineering 4 14%
Nursing and Health Professions 3 10%
Agricultural and Biological Sciences 2 7%
Other 7 24%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 March 2020.
All research outputs
#5,618,073
of 17,388,379 outputs
Outputs from BMC Medical Informatics and Decision Making
#630
of 1,577 outputs
Outputs of similar age
#127,629
of 318,698 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 17,388,379 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,577 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 56% 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 318,698 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 1 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