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Shahverdi H, Shahbazian R, Fard Moshiri P, Asvadi R, Ghorashi S A. Convolutional Neural Network Based Human Activity Recognition using CSI. itrc 2023; 15 (2) : 5
URL: http://ijict.itrc.ac.ir/article-1-513-en.html
URL: http://ijict.itrc.ac.ir/article-1-513-en.html
1- Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
2- Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Italy
3- CCognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
4- Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
5- Department of Computer Science & Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London, UK. , s.a.ghorashi@uel.ac.uk
2- Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Italy
3- CCognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
4- Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
5- Department of Computer Science & Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London, UK. , s.a.ghorashi@uel.ac.uk
Abstract: (897 Views)
Human activity recognition (HAR) has the potential to significantly impact applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Because of the prevalence of wireless devices, the Wi-Fi-based approach has attracted a lot of attention among other existing methods such as sensor-based and vision-based HAR. Wi-Fi devices can be used to distinguish between daily activities such as "walking," "running," and "sleeping," which affect Wi-Fi signal propagation. This paper proposes a Deep Learning method for HAR tasks that makes use of channel state information (CSI). We convert the CSI data to RGB images and classify the activity recognition using a 2D-Convolutional Neural Network (CNN). We evaluate the performance of the proposed method on two publicly available datasets for CSI data. Our experiments show that converting data into RGB images improves performance and accuracy compared to our previous method by at least 5%.
Article number: 5
Keywords: Activity Recognition, Channel State Information, Convolutional Neural Network, Deep Learning, WiFi.
Type of Study: Research |
Subject:
Information Technology
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