Communities & Collections
Researchers & Labs
Titles
DGIST
LIBRARY
DGIST R&D
Detail View
Department of Brain Sciences
Laboratory of Chemical Senses
1. Journal Articles
Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition
Kim, Kyungsoo
;
Sim, Minkyung
;
Lim, Sung‐Ho
;
Kim, Dongsu
;
Lee, Doyoung
;
Shin, Kwonsik
;
Moon, Cheil
;
Choi, Ji-Woong
;
Jang, Jae Eun
Department of Electrical Engineering and Computer Science
Advanced Electronic Devices Research Group(AEDRG) - Jang Lab.
1. Journal Articles
Department of Brain Sciences
Laboratory of Chemical Senses
1. Journal Articles
Department of Electrical Engineering and Computer Science
CSP(Communication and Signal Processing) Lab
1. Journal Articles
Citations
WEB OF SCIENCE
Citations
SCOPUS
Metadata Downloads
XML
Excel
Title
Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition
Issued Date
2021-04
Citation
Kim, Kyungsoo. (2021-04). Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition. Advanced Science, 8(7), 2002362. doi: 10.1002/advs.202002362
Type
Article
Author Keywords
machine learning
;
P(VDF-TrFE)
;
piezoelectric effect
;
tactile avatars
Keywords
Digital devices
;
Topography
;
Electronic device
;
Learning structure
;
Physical information
;
Sliding velocities
;
Tactile information
;
Tactile perception
;
Tactile sensation
;
Tactile sensing system
;
Deep learning
ISSN
2198-3844
Abstract
As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s−1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences. © 2021 The Authors. Advanced Science published by Wiley-VCH GmbH
URI
http://hdl.handle.net/20.500.11750/12981
DOI
10.1002/advs.202002362
Publisher
John Wiley and Sons Inc
Show Full Item Record
File Downloads
000615812000001.pdf
공유
공유하기
Related Researcher
Moon, Cheil
문제일
Department of Brain Sciences
read more
Total Views & Downloads