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WiFi's Unspoken Tales: Deep Neural Network Decodes Human Behavior from Channel State Information

Published: 03 April 2024 Publication History

Abstract

WiFi Channel State Information (CSI) represents the characteristics of wireless channels in wireless networks. WiFi CSI plays a pivotal role in wireless communications, primarily due to the variability of channel characteristics across time and space. By leveraging data analysis techniques based on Deep Neural Networks, we can capture the variations in channel characteristics associated with people's movements and behaviors indoors using WiFi CSI data. This offers a novel approach to Human Behavior Recognition, providing an alternative to camera-based methods that potentially infringe on privacy. In this paper, we delve into the structural design analysis of deep neural networks for human behavior recognition using WiFi CSI data and explore the training strategies vital for delivering extended services.

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  1. WiFi's Unspoken Tales: Deep Neural Network Decodes Human Behavior from Channel State Information

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    cover image ACM Conferences
    BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies
    December 2023
    187 pages
    ISBN:9798400704734
    DOI:10.1145/3632366
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 03 April 2024

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    Author Tags

    1. channel state information
    2. deep learning
    3. wifi sensing
    4. human behavior recognition
    5. human sensing

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