A Doppler-based Human Activity Recognition System using WiFi Signals

Ge, Y., Li, S., Shentu, M., Taha, A. , Zhu, S., Cooper, J. , Imran, M. and Abbasi, Q. (2021) A Doppler-based Human Activity Recognition System using WiFi Signals. In: IEEE Sensors 2021, Sydney, Australia, 31 Oct - 04 Nov 2021, ISBN 9781728195018 (doi: 10.1109/SENSORS47087.2021.9639680)

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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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhu, Professor Shuyuan and Ge, Yao and Taha, Dr Ahmad and Abbasi, Dr Qammer and Imran, Professor Muhammad and Li, Shibo and Cooper, Professor Jonathan
Authors: Ge, Y., Li, S., Shentu, M., Taha, A., Zhu, S., Cooper, J., Imran, M., and Abbasi, Q.
College/School:College of Science and Engineering > School of Engineering > Biomedical Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISSN:2168-9229
ISBN:9781728195018
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in 2021 IEEE Sensors
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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