Continuous Multi-user Activity Tracking via Room-Scale mmWave Sensing
Published in ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2023), 2024
Authors: Argha Sen, Anirban Das, Swadhin Pradhan, Sandip Chakraborty
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Abstract
This paper solves a complex, multi-user human-activity recognition problem with the help of mmWave sensing. Such continuous detection of human activities is essential for developing a pervasive interactive smart space. Existing literature lacks robust wireless sensing mechanisms capable of continuously monitoring multiple users’ activities without prior knowledge of the environment. Developing such a mechanism requires simultaneous localization and tracking of multiple subjects. In addition, it requires identifying their activities at various scales, some being macro-scale activities like walking, squats, etc., while others are micro-scale activities like typing or sitting, etc. In this paper, we develop a holistic system called MARS using a single Commercial-off-the-shelf (COTS) Millimeter Wave (mmWave) radar, which employs an intelligent model to sense both macro and micro activities. In addition, it uses a dynamic spatial time-sharing approach to sense different subjects simultaneously. A thorough evaluation of MARS shows that it can infer activities continuously with a weighted F1-Score of >94% and an average response time of 2 sec, with 5 subjects and 19 different activities.