Tangible UI by object and material classification with radar
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(Published version)
Date
2017
Authors
Yeo, H.S.
Ens, B.
Quigley, A.
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Conference paper
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SIGGRAPH Asia 2017 Emerging Technologies, SA 2017, 2017, iss.14, pp.1-2
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SIGGRAPH Asia 2017 Emerging Technologies, SA 2017 (27 Nov 2017 - 30 Nov 2017 : Bangkok, Thailand)
Abstract
Radar signals penetrate, scaffer, 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 UIs. 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.
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Copyright 2017 held by the owner/author(s).