FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals

Zeng, Y, Wu, D, Gao, R, Gu, T and Zhang, D 2018, 'FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals', Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, no. 3, pp. 1-19.


Document type: Journal Article
Collection: Journal Articles

Title FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals
Author(s) Zeng, Y
Wu, D
Gao, R
Gu, T
Zhang, D
Year 2018
Journal name Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number 3
Start page 1
End page 19
Total pages 19
Publisher Association for Computing Machinery
Abstract Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.
Subject Networking and Communications
Mobile Technologies
Ubiquitous Computing
Keyword(s) Human-centered computing
Ubiquitous and mobile computing systems and tools
Respiration Sensing
WiFi
Channel state information (CSI)
DOI - identifier 10.1145/3264958
Copyright notice © 2018 Association for Computing Machinery.
ISSN 2474-9567
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