@inproceedings{5c1be5056a26400a8b9cf362b6dfee30,
title = "A Doppler-based Human Activity Recognition System using WiFi Signals",
abstract = "WiFi-based human activity recognition has drawn a lot of attention in recent years due to the low cost and high popularity of WiFi devices. The wireless monitoring system is able to efficiently detect abnormal activities like falling and body shaking, without privacy invasion. In this paper, we propose a framework using Doppler Frequency Shift-based methodology to extract the features and classify different activities with channel state information collected from WiFi devices. The experimental results demonstrate the reliability of our method for the application of activity recognition.",
keywords = "Doppler effect, WiFi sensing, channel state information, deep learning, human activity recognition",
author = "Yao Ge and Shibo Li and Minjian Shentu and Ahmad Taha and Shuyuan Zhu and Jonathan Cooper and Muhammad Imran and Qammer Abbasi",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE Sensors, SENSORS 2021 ; Conference date: 31-10-2021 Through 04-11-2021",
year = "2021",
doi = "10.1109/SENSORS47087.2021.9639680",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Sensors, SENSORS 2021 - Conference Proceedings",
address = "United States",
}