RadarCat : Radar Categorization for input & interaction
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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.
Yeo , H S , Flamich , G , Schrempf , P M , Harris-Birtill , D C C & Quigley , A J 2016 , RadarCat : Radar Categorization for input & interaction . in Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16) . ACM , pp. 833-841 , 29th ACM User Interface Software and Technology Symposium , Tokyo , Japan , 16/10/16 . https://doi.org/10.1145/2984511.2984515conference
Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16)
Copyright © 2016, the Author(s). This work is 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 http://dx.doi.org/10.1145/2984511.2984515
DescriptionThe research described here was supported by the University of St Andrews and the Scottish Informatics and Computer Science Alliance (SICSA).
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