Below the Surface: Unobtrusive Activity Recognition for Work Surfaces using RF-radar sensing

Activity recognition is a core component of many intelligent and context-aware systems. In this paper, 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 two work domains; recognizing work activities at a convenience-store counter (useful for post-hoc analytics) and recognizing common office deskwork activities (useful for real-time applications). We classify seven clerk activities with 94.9% accuracy using data collected in a lab environment, and recognize six common deskwork activities collected in real offices with 95.3% 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.