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2019 | OriginalPaper | Chapter

Device-Free Activity Recognition for Underground Spaces Based on Convolutional Neural Network

Authors : Qizhen Zhou, Jianchun Xing, Xuewei Zhang, Wei Chen

Published in: Advancements in Smart City and Intelligent Building

Publisher: Springer Singapore

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Abstract

Inspired by the excellent work of wireless sensing, we propose a non-invasive activity recognition system, Under-Sense, for underground space sensing with a pair of commodity Wi-Fi devices. Firstly, by extracting relative phase information from all 90 subcarriers, we construct fine-grained images and then compress the rectangle images into k-dimension by singular value decomposition (SVD). A nine-layer convolutional neural network (CNN) is designed to automatically extract important features from constructed images and classify five human activities. Our results show Under-Sense could achieve 99.5% average accuracy in the empty meeting room and 96.7% in complex student studio environment.

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Metadata
Title
Device-Free Activity Recognition for Underground Spaces Based on Convolutional Neural Network
Authors
Qizhen Zhou
Jianchun Xing
Xuewei Zhang
Wei Chen
Copyright Year
2019
Publisher
Springer Singapore
Book
Advancements in Smart City and Intelligent Building

Print ISBN: 978-981-13-6732-8

Electronic ISBN: 978-981-13-6733-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-6733-5_53