Evidence map›Paper›PMID 34458383›Full record

ArticleIEEE Haptics Symposium : [proceedings]. IEEE Haptics Symposium2020

Identifying 3-D spatiotemporal skin deformation cues evoked in interacting with compliant elastic surfaces.

Bingxu Li, Steven Hauser, Gregory J Gerling

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In one paragraph

Article in IEEE Haptics Symposium : [proceedings]. IEEE Haptics Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bingxu LiSchool of Engineering and Applied Science, University of Virginia, Charlottesville, VA 22904 USA.
Steven HauserSchool of Engineering and Applied Science, University of Virginia, Charlottesville, VA 22904 USA.
Gregory J GerlingSchool of Engineering and Applied Science, University of Virginia, Charlottesville, VA 22904 USA.

Funding

CRCNS: Neural Representations of Object Compliance in the Periphery R01NS105241 · NINDS · UNIVERSITY OF VIRGINIA · PI GERLING, GREGORY JOHN, LUMPKIN, ELLEN A · 2017 to 2019
$990k
NINDS NIH HHS R01 NS105241
6 · The paper itself

Abstract

We regularly touch soft, compliant fruits and tissues. To help us discriminate them, we rely upon cues embedded in spatial and temporal deformation of finger pad skin. However, we do not yet understand, in touching objects of various compliance, how such patterns evolve over time, and drive perception. Using a 3-D stereo imaging technique in passive touch, we develop metrics for quantifying skin deformation, across compliance, displacement, and time. The metrics map 2-D estimates of terminal contact area to 3-D metrics that represent spatial and temporal changes in penetration depth, surface curvature, and force. To do this, clouds of thousands of 3-D points are reduced in dimensionality into stacks of ellipses, to be more readily comparable between participants and trials. To evaluate the robustness of the derived 3-D metrics, human subjects experiments are performed with stimulus pairs varying in compliance and discriminability. The results indicate that metrics such as penetration depth and surface curvature can distinguish compliances earlier, at less displacement. Observed also are distinct modes of skin deformation, for contact with stiffer objects, versus softer objects that approach the skin's compliance. These observations of the skin's deformation may guide the design and control of haptic actuation.

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.