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

Bo Wei*, Kai Li, Chengwen Luo, Weitao Xu, Jin Zhang, Kuan Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers’ attention instead because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. 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 with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.
Original languageEnglish
Article number29
Number of pages26
JournalACM Transactions on Internet of Things
Volume2
Issue number4
Early online date16 Aug 2021
DOIs
Publication statusE-pub ahead of print - 16 Aug 2021

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