Deep Multi-Modal Network Based Data-Driven Haptic Textures Modeling

Joolekha Bibi Joolee; Seokhee Jeon

doi:10.1109/WHC49131.2021.9517223

Deep Multi-Modal Network Based Data-Driven Haptic Textures Modeling

Joolekha Bibi Joolee
, Seokhee Jeon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

This paper presents a novel data-driven approach for haptic texture modeling using a deep multi-modal network. The network is trained using contact acceleration data that are collected when a stylus is scanned on a textured surface with diverse scanning velocities, directions, and forces, which used for recreating the acceleration profile in real-time. We present some preliminary results to demonstrate the effectiveness of the proposed approach.

Original language	English
Title of host publication	2021 IEEE World Haptics Conference, WHC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1140
Number of pages	1
ISBN (Electronic)	9781665418713
DOIs	https://doi.org/10.1109/WHC49131.2021.9517223
Publication status	Published - 6 Jul 2021
Event	2021 IEEE World Haptics Conference, WHC 2021 - Virtual, Montreal, Canada Duration: 6 Jul 2021 → 9 Jul 2021

Publication series

Name	2021 IEEE World Haptics Conference, WHC 2021

Conference

Conference	2021 IEEE World Haptics Conference, WHC 2021
Country/Territory	Canada
City	Virtual, Montreal
Period	6/07/21 → 9/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Access to Document

10.1109/WHC49131.2021.9517223

Cite this

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title = "Deep Multi-Modal Network Based Data-Driven Haptic Textures Modeling",

abstract = "This paper presents a novel data-driven approach for haptic texture modeling using a deep multi-modal network. The network is trained using contact acceleration data that are collected when a stylus is scanned on a textured surface with diverse scanning velocities, directions, and forces, which used for recreating the acceleration profile in real-time. We present some preliminary results to demonstrate the effectiveness of the proposed approach.",

author = "Joolee, \{Joolekha Bibi\} and Seokhee Jeon",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE World Haptics Conference, WHC 2021 ; Conference date: 06-07-2021 Through 09-07-2021",

year = "2021",

month = jul,

day = "6",

doi = "10.1109/WHC49131.2021.9517223",

language = "English",

series = "2021 IEEE World Haptics Conference, WHC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2021 IEEE World Haptics Conference, WHC 2021",

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}

Joolee, JB & Jeon, S 2021, Deep Multi-Modal Network Based Data-Driven Haptic Textures Modeling. in 2021 IEEE World Haptics Conference, WHC 2021. 2021 IEEE World Haptics Conference, WHC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1140, 2021 IEEE World Haptics Conference, WHC 2021, Virtual, Montreal, Canada, 6/07/21. https://doi.org/10.1109/WHC49131.2021.9517223

Deep Multi-Modal Network Based Data-Driven Haptic Textures Modeling. / Joolee, Joolekha Bibi; Jeon, Seokhee.
2021 IEEE World Haptics Conference, WHC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1140 (2021 IEEE World Haptics Conference, WHC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Jeon, Seokhee

PY - 2021/7/6

Y1 - 2021/7/6

N2 - This paper presents a novel data-driven approach for haptic texture modeling using a deep multi-modal network. The network is trained using contact acceleration data that are collected when a stylus is scanned on a textured surface with diverse scanning velocities, directions, and forces, which used for recreating the acceleration profile in real-time. We present some preliminary results to demonstrate the effectiveness of the proposed approach.

AB - This paper presents a novel data-driven approach for haptic texture modeling using a deep multi-modal network. The network is trained using contact acceleration data that are collected when a stylus is scanned on a textured surface with diverse scanning velocities, directions, and forces, which used for recreating the acceleration profile in real-time. We present some preliminary results to demonstrate the effectiveness of the proposed approach.

UR - https://www.scopus.com/pages/publications/85115154420

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DO - 10.1109/WHC49131.2021.9517223

M3 - Conference contribution

AN - SCOPUS:85115154420

T3 - 2021 IEEE World Haptics Conference, WHC 2021

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BT - 2021 IEEE World Haptics Conference, WHC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE World Haptics Conference, WHC 2021

Y2 - 6 July 2021 through 9 July 2021

ER -

Deep Multi-Modal Network Based Data-Driven Haptic Textures Modeling

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