Unobtrusive Activity Recognition and Position Estimation for Work Surfaces Using RF-Radar Sensing

Author:

Avrahami Daniel1,Patel Mitesh1,Yamaura Yusuke2,Kratz Sven1,Cooper Matthew1

Affiliation:

1. FXPAL, Palo Alto, CA, USA

2. FUJI XEROX Communication Technology Laboratory, Yokohama, Japan

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

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1. Beyond Radar Waves: The First Workshop on Radar-Based Human-Computer Interaction;Companion of the16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems;2024-06-24

2. Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials;IEEE Access;2024

3. RadarSense: Accurate Recognition of Mid-air Hand Gestures with Radar Sensing and Few Training Examples;ACM Transactions on Interactive Intelligent Systems;2023-09-11

4. Flexible gesture input with radars: systematic literature review and taxonomy of radar sensing integration in ambient intelligence environments;Journal of Ambient Intelligence and Humanized Computing;2023-04-10

5. Understanding User Motion;Handbook of Human Computer Interaction;2023

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