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FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration

Overview of attention for article published in Frontiers in Robotics and AI, December 2020
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  • Average Attention Score compared to outputs of the same age and source

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5 X users

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37 Mendeley
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Title
FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration
Published in
Frontiers in Robotics and AI, December 2020
DOI 10.3389/frobt.2020.573096
Pubmed ID
Authors

Mohammad Anvaripour, Mahta Khoshnam, Carlo Menon, Mehrdad Saif

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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 14%
Unspecified 4 11%
Researcher 3 8%
Student > Master 2 5%
Student > Doctoral Student 1 3%
Other 2 5%
Unknown 20 54%
Readers by discipline Count As %
Unspecified 4 11%
Computer Science 3 8%
Engineering 3 8%
Psychology 3 8%
Business, Management and Accounting 2 5%
Other 3 8%
Unknown 19 51%
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 12 December 2020.
All research outputs
#14,242,332
of 23,267,128 outputs
Outputs from Frontiers in Robotics and AI
#760
of 1,535 outputs
Outputs of similar age
#262,281
of 509,311 outputs
Outputs of similar age from Frontiers in Robotics and AI
#35
of 68 outputs
Altmetric has tracked 23,267,128 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,535 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 46th percentile – i.e., 46% 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 509,311 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.