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A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

Overview of attention for article published in Frontiers in Robotics and AI, December 2017
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Mentioned by

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5 X users

Citations

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27 Dimensions

Readers on

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44 Mendeley
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Title
A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation
Published in
Frontiers in Robotics and AI, December 2017
DOI 10.3389/frobt.2017.00068
Authors

Nitin Sharma, Nicholas Andrew Kirsch, Naji A. Alibeji, Warren E. Dixon

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Student > Master 5 11%
Professor 4 9%
Researcher 4 9%
Student > Doctoral Student 3 7%
Other 6 14%
Unknown 13 30%
Readers by discipline Count As %
Engineering 23 52%
Chemical Engineering 1 2%
Unspecified 1 2%
Philosophy 1 2%
Nursing and Health Professions 1 2%
Other 3 7%
Unknown 14 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 December 2017.
All research outputs
#14,370,803
of 23,012,811 outputs
Outputs from Frontiers in Robotics and AI
#822
of 1,511 outputs
Outputs of similar age
#239,217
of 440,922 outputs
Outputs of similar age from Frontiers in Robotics and AI
#16
of 22 outputs
Altmetric has tracked 23,012,811 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,511 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one is in the 41st percentile – i.e., 41% 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 440,922 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.