FullBreathe: full human respiration detection exploiting complementarity of CSI phase and amplitude of WiFi signals

Youwei Zeng, Dan Wu, Ruiyang Gao, Tao Gu, Daqing Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number148
Number of pages19
JournalACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume2
Issue number3
DOIs
Publication statusPublished - Sep 2018
Externally publishedYes

Keywords

  • Respiration Sensing
  • WiFi
  • Channel state information (CSI)

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