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Development of a skateboarding trick classifier using accelerometry and machine learning

Overview of attention for article published in Research on Biomedical Engineering, December 2017
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1 X user

Citations

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16 Dimensions

Readers on

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47 Mendeley
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Title
Development of a skateboarding trick classifier using accelerometry and machine learning
Published in
Research on Biomedical Engineering, December 2017
DOI 10.1590/2446-4740.04717
Authors

Nicholas Kluge Corrêa, Júlio César Marques de Lima, Thais Russomano, Marlise Araujo dos Santos

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 11 23%
Student > Master 9 19%
Student > Ph. D. Student 4 9%
Researcher 3 6%
Student > Postgraduate 2 4%
Other 5 11%
Unknown 13 28%
Readers by discipline Count As %
Engineering 12 26%
Sports and Recreations 9 19%
Arts and Humanities 2 4%
Computer Science 2 4%
Physics and Astronomy 2 4%
Other 5 11%
Unknown 15 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 January 2019.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Research on Biomedical Engineering
#38
of 74 outputs
Outputs of similar age
#279,317
of 444,941 outputs
Outputs of similar age from Research on Biomedical Engineering
#3
of 4 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 74 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 444,941 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.