A breath detection method in a non-contact sensing mode

Abstract

The invention discloses a breath detection method adopting a non-contact perception mode. A method for recognizing a breath detection area comprises the following steps: by adopting the position of asending device and the position of a receiving device as oval focuses, by adopting the distance between the position of the sending device and the position of the receiving device as the focal length,and according to the wavelength of a radio-frequency signal sent by the sending device, constructing a first Fresnel area; according to different clearances, calculating the amplitudes of the signalswhen the human body enters the different positions of the first Fresnel area; and according to the calculated amplitudes of the signals of the different positions, recognizing a detectable area and an undetectable area in the breath detection area.

Description

technical field [0001] The invention relates to a non-contact breathing detection method, in particular to a non-contact breathing detection method based on commercial wireless local area network equipment. Background technique [0002] Respiratory rate is an important vital sign, which can not only be used to monitor disease and health status, but also can predict some emergency situations that require immediate clinical treatment, such as respiratory arrest, etc. Nearly 5% of the population is reported to suffer from breathing-related disorders such as sleep apnea syndrome (SAS). A recent study also showed that breathing abnormalities are a major cause of SIDS. In many cases, patients with respiratory disease exhibit only brief or random symptoms. Therefore, low-cost and continuous respiratory monitoring in a home setting is essential. [0003] Two commonly used methods for continuous respiratory monitoring in clinical practice are chest impedance method and carbon diox...

AI Technical Summary

Problems solved by technology

For example, USRP [1] and UWB [2] signals are used for breath detection, but these are dedicated hardware devices, which limit their application in real life

Documents [3]-[6] proposed breath detection based on WiFi radio frequency signals. However, these detection systems can only perform breath detection at a specific position, cannot identify the blind area of ​​breath detection, and cannot explain the effect of breath detection in different postures. Reason for difference

Literature [7] introduced a breath detection method based on the Fresnel zone model, using reflection theory to guide breath detection outside the first Fresnel zone, however, more complex diffraction effects dominate in the first Fresnel zone Factors, it is still impossible to quantitatively describe the quality of the detection effect in this area

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