  Thanks to the ubiquitousness of Wi-Fi access points and devices, Wi-Fi
sensing enables transformative applications in remote health care, security,
and surveillance. Existing work has explored the usage of machine learning on
channel state information (CSI) computed from Wi-Fi packets to classify events
of interest. However, most of these algorithms require a significant amount of
data collection, as well as extensive computational power for additional CSI
feature extraction. Moreover, the majority of these models suffer from poor
accuracy when tested in a new/untrained environment. In this paper, we propose
ReWiS, a novel framework for robust and environment-independent Wi-Fi sensing.
The key innovation of ReWiS is to leverage few-shot learning (FSL) as the
inference engine, which (i) reduces the need for extensive data collection and
application-specific feature extraction; (ii) can rapidly generalize to new
tasks by leveraging only a few new samples. We prototype ReWiS using
off-the-shelf Wi-Fi equipment and showcase its performance by considering a
compelling use case of human activity recognition. Thus, we perform an
extensive data collection campaign in three different propagation environments
with two human subjects. We evaluate the impact of each diversity component on
the performance and compare ReWiS with a traditional convolutional neural
network (CNN) approach. Experimental results show that ReWiS improves the
performance by about 40% with respect to existing single-antenna low-resolution
approaches. Moreover, when compared to a CNN-based approach, ReWiS shows a 35%
more accuracy and less than 10% drop in accuracy when tested in different
environments, while the CNN drops by more than 45%.
