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Title: Non-invasive detection of moving and stationary human with wifi
Authors: Wu, C
Yang, Z
Zhou, Z
Liu, X
Liu, Y
Cao, J 
Keywords: Calibration-free
Channel state information
Human breathing
Human detection
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE journal on selected areas in communications, 2015, v. 33, no. 11, 2430294, p. 2329-2342 How to cite?
Journal: IEEE journal on selected areas in communications 
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%.
ISSN: 0733-8716
DOI: 10.1109/JSAC.2015.2430294
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