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  1. Book ; Online: Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN

    Showmik, Ishtiaque Ahmed / Sanam, Tahsina Farah / Imtiaz, Hafiz

    2022  

    Abstract: Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick ... ...

    Abstract Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.

    Comment: \c{opyright} 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
    Subject code 006
    Publishing date 2022-12-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: CoMuTe

    Sanam, Tahsina Farah / Godrich, Hana

    A Convolutional Neural Network Based Device Free Multiple Target Localization Using CSI

    2020  

    Abstract: With the growth of Internet-of-Things (IoT), Location based Services (LBS) are gaining significant attention over the past years. Location information is one of the important ingredients for many LBS where the system requires to localize multiple targets ...

    Abstract With the growth of Internet-of-Things (IoT), Location based Services (LBS) are gaining significant attention over the past years. Location information is one of the important ingredients for many LBS where the system requires to localize multiple targets in indoor setting. As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. In this paper, we propose CoMuTe, the first convolutional neural network (CNN) based device free multiple target localization leveraging the Channel State Information (CSI) from multiple wireless links. The system represents the CSIs as Multi-link Time-Frequency (MLTF) image by organizing them as time-frequency matrices and utilize these MLTF images as the input feature for CNN network. The CoMuTe models multi target localization as a multi label spot classification approach under the assumption that each MLTF image is associated with multiple labels/spots. The localization is performed with a training stage and a localization stage. In the training stage, the CSI based MLTF images are constructed with single target at each location. The constructed images are used to train the CNN via a gradient based optimization algorithm. In the localization stage, the test MLTF image obtained for targets at multiple spots is fed to the CNN network and locations of the targets are calculated using sigmoid activation function in the output layer under the multi label classification framework. Extensive experiments are conducted to select appropriate parameters for the CNN architecture as well as for the system design. The experimental results demonstrate the superior performance of CoMuTe over existing multi target localization approaches.

    Comment: 18 pages, 11 figures
    Keywords Electrical Engineering and Systems Science - Signal Processing
    Subject code 006
    Publishing date 2020-03-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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