Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2015
Abstract
Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts sophisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95% for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%.
Keywords
Non-invasive, human detection, calibration-free, human breathing, channel state information
Discipline
Digital Communications and Networking | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Journal on Selected Areas in Communications
Volume
33
Issue
11
First Page
2329
Last Page
2342
ISSN
0733-8716
Identifier
10.1109/JSAC.2015.2430294
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
WU, Chenshu; YANG, Zheng; ZHOU, Zimu; LIU, Xuefeng; LIU, Yunhao; and CAO, Jiannong.
Non-invasive detection of moving and stationary human with WiFi. (2015). IEEE Journal on Selected Areas in Communications. 33, (11), 2329-2342.
Available at: https://ink.library.smu.edu.sg/sis_research/4884
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/JSAC.2015.2430294