Summary: Radar signals penetrate, scatter, absorb and reflect energy into proximate objects, indeed 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 to the objects or complex infrastructures. This affords new forms of interaction with digital devices and proximate objects.
Hui-Shyong Yeo, University of St Andrews
Barrett Ens, University of South Australia
Aaron Quigley, University of St Andrews