A quantitative perceptual model for tactile roughness

Chelsea Tymms, Esther P. Gardner, Denis Zorin

Research output: Contribution to journalArticle

Abstract

Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort, and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.

Original languageEnglish (US)
Article number168
JournalACM Transactions on Graphics
Volume37
Issue number5
DOIs
StatePublished - Nov 1 2018

Fingerprint

Surface roughness
Geometry
Drawing (graphics)
Textures
Printing
Elasticity
Skin
Friction
Fabrication
Chemical analysis
Experiments

Keywords

  • Fabrication
  • Perception
  • Roughness

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

Cite this

A quantitative perceptual model for tactile roughness. / Tymms, Chelsea; Gardner, Esther P.; Zorin, Denis.

In: ACM Transactions on Graphics, Vol. 37, No. 5, 168, 01.11.2018.

Research output: Contribution to journalArticle

Tymms, Chelsea ; Gardner, Esther P. ; Zorin, Denis. / A quantitative perceptual model for tactile roughness. In: ACM Transactions on Graphics. 2018 ; Vol. 37, No. 5.
@article{2dcfd84dd0234c1b8e2d6be55933b2f7,
title = "A quantitative perceptual model for tactile roughness",
abstract = "Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort, and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.",
keywords = "Fabrication, Perception, Roughness",
author = "Chelsea Tymms and Gardner, {Esther P.} and Denis Zorin",
year = "2018",
month = "11",
day = "1",
doi = "10.1145/3186267",
language = "English (US)",
volume = "37",
journal = "ACM Transactions on Graphics",
issn = "0730-0301",
publisher = "Association for Computing Machinery (ACM)",
number = "5",

}

TY - JOUR

T1 - A quantitative perceptual model for tactile roughness

AU - Tymms, Chelsea

AU - Gardner, Esther P.

AU - Zorin, Denis

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort, and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.

AB - Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort, and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.

KW - Fabrication

KW - Perception

KW - Roughness

UR - http://www.scopus.com/inward/record.url?scp=85061245489&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061245489&partnerID=8YFLogxK

U2 - 10.1145/3186267

DO - 10.1145/3186267

M3 - Article

AN - SCOPUS:85061245489

VL - 37

JO - ACM Transactions on Graphics

JF - ACM Transactions on Graphics

SN - 0730-0301

IS - 5

M1 - 168

ER -