In this paper, we propose a novel, generalizable, and scalable idea that eliminates the need
for collecting Radio Frequency (RF) measurements, when training RF sensing systems for
human-motion-related activities. Existing learning-based RF sensing systems require
collecting massive RF training data, which depends heavily on the particular sensing
setup/involved activities. Thus, new data needs to be collected when the setup/activities
change, significantly limiting the practical deployment of RF sensing systems. On the other …