Hossein Shahverdi - Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
Reza Shahbazian - Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Italy
Parisa Fard Moshiri - CCognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
Reza Asvadi - Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Seyed Ali Ghorashi - Department of Computer Science & Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London, UK.

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 ۲D-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 ۵%.

Activity Recognition, Channel State Information, Convolutional Neural Network, Deep Learning, WiFi.