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Article

A CSI-Based Human Activity Recognition Using Deep Learning

1
Cognitive Telecommunication Research Group, Department of Electrical Engineering, Shahid Beheshti University G. C., Tehran 1983969411, Iran
2
Electrical Engineering Research Group, Faculty of Technology and Engineering Research Center, Standard Research Institute, Alborz 31745-139, Iran
3
Department of Computer Science & Digital Technologies, School of Architecture, Computing, and Engineering, University of East London, London E15 4LZ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Ivan Miguel Serrano Pires
Sensors 2021, 21(21), 7225; https://doi.org/10.3390/s21217225
Received: 6 September 2021 / Revised: 21 October 2021 / Accepted: 22 October 2021 / Published: 30 October 2021
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities. View Full-Text
Keywords: activity recognition; Internet of Things; smart house; deep learning; channel state information activity recognition; Internet of Things; smart house; deep learning; channel state information
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MDPI and ACS Style

Fard Moshiri, P.; Shahbazian, R.; Nabati, M.; Ghorashi, S.A. A CSI-Based Human Activity Recognition Using Deep Learning. Sensors 2021, 21, 7225. https://doi.org/10.3390/s21217225

AMA Style

Fard Moshiri P, Shahbazian R, Nabati M, Ghorashi SA. A CSI-Based Human Activity Recognition Using Deep Learning. Sensors. 2021; 21(21):7225. https://doi.org/10.3390/s21217225

Chicago/Turabian Style

Fard Moshiri, Parisa, Reza Shahbazian, Mohammad Nabati, and Seyed A. Ghorashi. 2021. "A CSI-Based Human Activity Recognition Using Deep Learning" Sensors 21, no. 21: 7225. https://doi.org/10.3390/s21217225

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