No Need of Data Pre-processing: A General Framework for Radio-Based Device-Free Context Awareness

TL;DR

This paper introduces a novel, general deep learning framework for radio-based device-free context awareness that eliminates the need for initial data pre-processing, improving accuracy and simplifying implementation.

Contribution

It presents the first unified deep learning framework applicable to all radio-based context awareness applications, removing the need for feature extraction from raw signals.

Findings

Superior performance demonstrated through extensive evaluations

Framework generalizes across multiple radio-based applications

Eliminates unnecessary noise from initial data processing

Abstract

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.