  Device-free context awareness is important to many applications. There are
two broadly used approaches for device-free context awareness, i.e. video-based
and radio-based. Video-based applications can deliver good performance, but
privacy is a serious concern. Radio-based context awareness has drawn
researchers attention instead because it does not violate privacy and radio
signal can penetrate obstacles. Recently, deep learning has been introduced
into radio-based device-free context awareness and helps boost the recognition
accuracy. The present works design explicit methods for each radio based
application. They also use one additional step to extract features before
conducting classification and exploit deep learning as a classification tool.
The additional initial data processing step introduces unnecessary noise and
information loss. Without initial data processing, it is, however, challenging
to explore patterns of raw signals. In this paper, we are the first to propose
an innovative deep learning based general framework for both signal processing
and classification. The key novelty of this paper is that the framework can be
generalised for all the radio-based context awareness applications. We also
eliminate the additional effort to extract features from raw radio signals. We
conduct extensive evaluations to show the superior performance of our proposed
method and its generalisation.
