Abstract
Activity recognition, e.g., identifying individuals, recognizing their
physical activities, or estimating their number in a room, based on
WiFi’s Channel State Information (CSI) has been studied intensively in
the last decade.
While most existing works consider analyzing CSI data from a single
person in a rather constrained environment, almost none of them has been
successful in generalizing these results to unconstrained, real-world
environments, in particular, when multiple individuals are present.
In this paper, to address this problem, we introduce a fully annotated
dataset ($\approx$ 70 GB of data) containing CSI and
environmental data collected from two real-world offices over multiple
days of continuous monitoring. To the best of our knowledge, this is the
first freely available dataset of its kind.
On the one hand, our dataset evidences that vastly disregarded
{\em implicit changes} in the environment – due to
small objects being repositioned, added or removed – are the main
reason for the lack of generalizability by existing approaches. On the
other hand, we expect it to promote further research work in this area
and, thereby, to facilitate general solutions for CSI-based activity
recognition in real-world environments.