  In this article, we present a survey of recent advances in passive human
behaviour recognition in indoor areas using the channel state information (CSI)
of commercial WiFi systems. Movement of human body causes a change in the
wireless signal reflections, which results in variations in the CSI. By
analyzing the data streams of CSIs for different activities and comparing them
against stored models, human behaviour can be recognized. This is done by
extracting features from CSI data streams and using machine learning techniques
to build models and classifiers. The techniques from the literature that are
presented herein have great performances, however, instead of the machine
learning techniques employed in these works, we propose to use deep learning
techniques such as long-short term memory (LSTM) recurrent neural network
(RNN), and show the improved performance. We also discuss about different
challenges such as environment change, frame rate selection, and multi-user
scenario, and suggest possible directions for future work.
