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

A Survey of Human Activity Recognition Using WiFi CSI