  Thanks to the ubiquitous deployment of Wi-Fi hotspots, channel state
information (CSI)-based Wi-Fi sensing can unleash game-changing applications in
many fields, such as healthcare, security, and entertainment. However, despite
one decade of active research on Wi-Fi sensing, most existing work only
considers legacy IEEE 802.11n devices, often in particular and
strictly-controlled environments. Worse yet, there is a fundamental lack of
understanding of the impact on CSI-based sensing of modern Wi-Fi features, such
as 160-MHz bandwidth, multiple-input multiple-output (MIMO) transmissions, and
increased spectral resolution in IEEE 802.11ax (Wi-Fi 6). This work aims to
shed light on the impact of Wi-Fi 6 features on the sensing performance and to
create a benchmark for future research on Wi-Fi sensing. To this end, we
perform an extensive CSI data collection campaign involving 3 individuals, 3
environments, and 12 activities, using Wi-Fi 6 signals. An anonymized ground
truth obtained through video recording accompanies our 80-GB dataset, which
contains almost two hours of CSI data from three collectors. We leverage our
dataset to dissect the performance of a state-of-the-art sensing framework
across different environments and individuals. Our key findings suggest that
(i) MIMO transmissions and higher spectral resolution might be more beneficial
than larger bandwidth for sensing applications; (ii) there is a pressing need
to standardize research on Wi-Fi sensing because the path towards a truly
environment-independent framework is still uncertain. To ease the experiments'
replicability and address the current lack of Wi-Fi 6 CSI datasets, we release
our 80-GB dataset to the community.
