Abstract:

This paper delves into the transformative potential of Machine Learning (ML) in Radio Frequency (RF) sensing applications. We focus on pivotal domains such as device localization, occupancy detection, activity monitoring, and biometric sensing, showcasing how ML is redefining the boundaries of what is possible. By harnessing the power of ML, we showcase how to unlock unprecedented performance enhancements in these critical areas. We provide a comprehensive review of cutting-edge ML-driven RF sensing methodologies and offer an overview of publicly available datasets that are propelling this field forward. Moreover, we present key challenges that remain - from the quality and labeling of RF sensor data to robustness, privacy, and explainability of ML models. Through this exploration, we lay the path for future scientific and engineering innovations in the ever-evolving landscape of RF sensing.