A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels
In the last years, several machine learning-based techniques have been
proposed to monitor human movements from Wi-Fi channel readings. However, the
development of domain-adaptive algorithms that robustly work across different
environments is still an open problem, whose solution requires large datasets
characterized by strong domain diversity, in terms of environments, persons and
Wi-Fi hardware. To date, the few public datasets available are mostly obsolete
- as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain
little or no domain diversity, thus dramatically limiting the advancements in
the design of sensing algorithms. The present contribution aims to fill this
gap by providing a dataset of IEEE 802.11ac channel measurements over an 80 MHz
bandwidth channel featuring notable domain diversity, through measurement
campaigns that involved thirteen subjects across different environments, days,
and with different hardware. Novel experimental data is provided by blocking
the direct path between the transmitter and the monitor, and collecting
measurements in a semi-anechoic chamber (no multi-path fading). Overall, the
dataset - available on IEEE DataPort [1] - contains more than thirteen hours of
channel state information readings (23.6 GB), allowing researchers to test
activity/identity recognition and people counting algorithms.