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

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


    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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    Titel :

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

    Beteiligte:
    Zhou, Qizhen (Autor:in) / Xing, Jianchun (Autor:in) / Zhang, Xuewei (Autor:in) / Chen, Wei (Autor:in)
    Erschienen in:

    Advancements in Smart City and Intelligent Building ; 8 ; 573-581

    Advances in Intelligent Systems and Computing ; 890 , 8

    Erscheinungsdatum :

    2019-01-01

    Format / Umfang :

    9 pages

    ISBN :

    978-981-13-6732-8 , 978-981-13-6733-5

    ISSN :

    2194-5357 , 2194-5365

    DOI :

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

    Medientyp :

    Aufsatz/Kapitel (Buch)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Smart underground space , Wi-Fi-based sensing , Activity recognition , Convolutional neural network Energy , Energy Efficiency , Transportation , Sustainable Architecture/Green Buildings , Sustainable Development , Robotics and Automation

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