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A 3D Computational Model of Transcutaneous Electrical Nerve Stimulation for Estimating Aβ Tactile Nerve Fiber Excitability

Overview of attention for article published in Frontiers in Neuroscience, May 2017
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Title
A 3D Computational Model of Transcutaneous Electrical Nerve Stimulation for Estimating Aβ Tactile Nerve Fiber Excitability
Published in
Frontiers in Neuroscience, May 2017
DOI 10.3389/fnins.2017.00250
Pubmed ID
Authors

Kaihua Zhu, Liming Li, Xuyong Wei, Xiaohong Sui

Abstract

Tactile sensory feedback plays an important role in our daily life. Transcutaneous electrical nerve stimulation (TENS) is widely accepted to produce artificial tactile sensation. To explore the underlying mechanism of tactile sensation under TENS, this paper presented a novel 3D TENS computational model including an active Aβ tactile nerve fiber (TNF) model and a forearm finite element model with the fine-layered skin structure. The conduction velocity vs. fiber diameter and strength-duration relationships in this combined TENS model matched well with experimental data. Based on this validated TENS model, threshold current variation were further investigated under different stimulating electrode sizes with varied fiber diameters. The computational results showed that the threshold current intensity increased with electrode size, and larger nerve fibers were recruited at lower current intensities. These results were comparable to our psychophysical experimental data from six healthy subjects. This novel 3D TENS model would further guide the floorplan of the surface electrodes, and the stimulating paradigms for tactile sensory feedback.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 18%
Researcher 13 16%
Student > Ph. D. Student 13 16%
Student > Bachelor 7 9%
Student > Doctoral Student 5 6%
Other 12 15%
Unknown 15 19%
Readers by discipline Count As %
Engineering 35 44%
Neuroscience 8 10%
Computer Science 3 4%
Materials Science 2 3%
Nursing and Health Professions 2 3%
Other 7 9%
Unknown 22 28%
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 23 May 2017.
All research outputs
#17,289,387
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#8,070
of 11,542 outputs
Outputs of similar age
#207,510
of 325,242 outputs
Outputs of similar age from Frontiers in Neuroscience
#148
of 204 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 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 24th percentile – i.e., 24% 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 325,242 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 204 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.