Tangible UI by object and material classification with radar

Hui Shyong Yeo; Barrett Ens; Aaron John Quigley

doi:10.1145/3132818.3132824

Tangible UI by object and material classification with radar

Hui Shyong Yeo, Barrett Ens, Aaron John Quigley

School of Computer Science

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

Abstract

Radar signals penetrate, scatter, absorb and reflect energy into proximate objects and ground penetrating and aerial radar systems are well established. We describe a highly accurate system based on a combination of a monostatic radar (Google Soli), supervised machine learning to support object and material classification based Uls. Based on RadarCat techniques, we explore the development of tangible user interfaces without modification of the objects or complex infrastructures. This affords new forms of interaction with digital devices, proximate objects and micro-gestures.

Original language	English
Title of host publication	SA '17 SIGGRAPH Asia 2017 Emerging Technologies
Place of Publication	New York
Publisher	ACM
Number of pages	2
ISBN (Electronic)	9781450354042
DOIs	https://doi.org/10.1145/3132818.3132824
Publication status	Published - 27 Nov 2017
Event	10th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia - Bangkok, Thailand Duration: 27 Nov 2017 → 30 Nov 2017 Conference number: 10 https://sa2017.siggraph.org/

Conference

Conference	10th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia
Country/Territory	Thailand
City	Bangkok
Period	27/11/17 → 30/11/17
Internet address	https://sa2017.siggraph.org/

Keywords

Radar sensing
Tangible interaction
Object recognition

Access to Document

10.1145/3132818.3132824

Yeo-2017-Tangible-UI-SA17-AAM
© 2017, the Owner(s) / the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3132818.3132824
Accepted author manuscript, 14.9 MB

Cite this

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title = "Tangible UI by object and material classification with radar",

abstract = "Radar signals penetrate, scatter, absorb and reflect energy into proximate objects and ground penetrating and aerial radar systems are well established. We describe a highly accurate system based on a combination of a monostatic radar (Google Soli), supervised machine learning to support object and material classification based Uls. Based on RadarCat techniques, we explore the development of tangible user interfaces without modification of the objects or complex infrastructures. This affords new forms of interaction with digital devices, proximate objects and micro-gestures.",

keywords = "Radar sensing, Tangible interaction, Object recognition",

author = "Yeo, {Hui Shyong} and Barrett Ens and Quigley, {Aaron John}",

year = "2017",

month = nov,

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doi = "10.1145/3132818.3132824",

language = "English",

booktitle = "SA '17 SIGGRAPH Asia 2017 Emerging Technologies",

publisher = "ACM",

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url = "https://sa2017.siggraph.org/",

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T1 - Tangible UI by object and material classification with radar

AU - Yeo, Hui Shyong

AU - Ens, Barrett

AU - Quigley, Aaron John

N1 - Conference code: 10

PY - 2017/11/27

Y1 - 2017/11/27

N2 - Radar signals penetrate, scatter, absorb and reflect energy into proximate objects and ground penetrating and aerial radar systems are well established. We describe a highly accurate system based on a combination of a monostatic radar (Google Soli), supervised machine learning to support object and material classification based Uls. Based on RadarCat techniques, we explore the development of tangible user interfaces without modification of the objects or complex infrastructures. This affords new forms of interaction with digital devices, proximate objects and micro-gestures.

AB - Radar signals penetrate, scatter, absorb and reflect energy into proximate objects and ground penetrating and aerial radar systems are well established. We describe a highly accurate system based on a combination of a monostatic radar (Google Soli), supervised machine learning to support object and material classification based Uls. Based on RadarCat techniques, we explore the development of tangible user interfaces without modification of the objects or complex infrastructures. This affords new forms of interaction with digital devices, proximate objects and micro-gestures.

KW - Radar sensing

KW - Tangible interaction

KW - Object recognition

U2 - 10.1145/3132818.3132824

DO - 10.1145/3132818.3132824

M3 - Conference contribution

BT - SA '17 SIGGRAPH Asia 2017 Emerging Technologies

PB - ACM

CY - New York

T2 - 10th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia

Y2 - 27 November 2017 through 30 November 2017

ER -

Tangible UI by object and material classification with radar

Abstract

Conference

Keywords

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Cite this