  Taking advantage of the rich information provided by Wi-Fi measurement
setups, Wi-Fi-based human behavior sensing leveraging Channel State Information
(CSI) measurements has received a lot of research attention in recent years.
The CSI-based human sensing algorithms typically either rely on an explicit
channel propagation model or, more recently, adopt machine learning so as to
robustify feature extraction. In most related work, the considered CSI is
extracted from a single dedicated Access Point (AP) communication setup. In
this paper, we consider a more realistic setting where a legacy network of
multiple APs is already deployed for communications purposes and leveraged for
sensing benefits using machine learning. The use of legacy network presents
challenges and opportunities as many Wi-Fi links can present with richer yet
unequally useful data sets. In order to break the curse of dimensionality
associated with training over a too large dimensional CSI, we propose a link
selection mechanism based on Reinforcement Learning (RL) which allows for
dimension reduction while preserving the data that is most relevant for human
behavior sensing. The method is based on a sequential state decision-making
process in which the CSI is modeled as a part of the state. From actual
experiment results, our method is shown to perform better than state-of-the-art
approaches in a scenario with multiple available Wi-Fi links.
