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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Zhou, Qizhen (author) / Xing, Jianchun (author) / Zhang, Xuewei (author) / Chen, Wei (author)


    Publication date :

    2019-01-01


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Human activity recognition for static and dynamic activity using convolutional neural network

    Minarno, Agus Eko / Kusuma, Wahyu Andhyka / Kurniawan, Yoga Anggi | BASE | 2021

    Free access

    Convolutional neural network based vehicle turn signal recognition

    Yoneda, Keisuke / Kuramoto, Akisue / Suganuma, Naoki | IEEE | 2017


    Car Recognition System Using Convolutional Neural Network

    Chauhan, Shweta / Kumar, Rahul / Kumar, Bibek | IEEE | 2023


    Medical Image Recognition Based on Improved Convolutional Neural Network

    Zhou, Chuanhong / Zhang, Yiyang / Yang, Lihua | Springer Verlag | 2021


    Traffic Sign Recognition Method Based on Convolutional Neural Network

    Li, Jitong / Chen, Yuguang / Lin, Honghao et al. | ASCE | 2024