RadarCat : Radar Categorization for input & interaction

Hui Shyong Yeo, Gergely Flamich, Patrick Maurice Schrempf, David Cameron Christopher Harris-Birtill, Aaron John Quigley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

70 Citations (Scopus)


In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.
Original languageEnglish
Title of host publicationProceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16)
Number of pages9
ISBN (Print)9781450341899
Publication statusPublished - 16 Oct 2016
Event29th ACM User Interface Software and Technology Symposium - Hitotsubashi Hall, National Center of Sciences Building, Tokyo, Japan
Duration: 16 Oct 201619 Oct 2016
Conference number: 29


Conference29th ACM User Interface Software and Technology Symposium
Abbreviated titleUIST
Internet address


  • Context-aware interaction
  • Machine learning
  • Material classification
  • Object recognition
  • Ubiquitous computing


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