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• W2967503331 abstract "Activity recognition is a core component of many intelligent and context-aware systems. We present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras. We demonstrate our approach, which utilizes an RF-radar sensor mounted under the work surface, in three domains: recognizing work activities at a convenience-store counter, recognizing common office deskwork activities, and estimating the position of customers in a showroom environment. Our examples illustrate potential benefits for both post-hoc business analytics and for real-time applications. Our solution was able to classify seven clerk activities with 94.9% accuracy using data collected in a lab environment and able to recognize six common deskwork activities collected in real offices with 95.3% accuracy. Using two sensors simultaneously, we demonstrate coarse position estimation around a large surface with 95.4% accuracy. We show that using multiple projections of RF signal leads to improved recognition accuracy. Finally, we show how smartwatches worn by users can be used to attribute an activity, recognized with the RF sensor, to a particular user in multi-user scenarios. We believe our solution can mitigate some of users’ privacy concerns associated with cameras and is useful for a wide range of intelligent systems." @default.
• W2967503331 created "2019-08-22" @default.
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• W2967503331 date "2019-08-09" @default.
• W2967503331 modified "2023-10-16" @default.
• W2967503331 title "Unobtrusive Activity Recognition and Position Estimation for Work Surfaces Using RF-Radar Sensing" @default.
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• W2967503331 doi "https://doi.org/10.1145/3241383" @default.
• W2967503331 hasPublicationYear "2019" @default.
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