Vital sign detection method based on millimeter-wave radar

Abstract

The invention discloses a vital sign detection method based on millimeter-wave radar, which is used for detecting heartbeat and respiratory rate of a living body. According to the method, a multi-antenna transceiving mode is adopted, distance dimension FFT is carried out on data of each receiving antenna, FFT is carried out along the dimension of each receiving antenna, and then the position of each to-be-detected target in a two-dimensional plane is determined by measuring phase change; and for an extracted target life signal, a band-pass filter is firstly used for dividing the signal into different frequency bands, then an improved empirical mode decomposition method is used for calculating respiratory and heartbeat frequencies, harmonic interference is further removed, a window local averaging method is used for replacing a cubic spline interpolation method in the empirical mode decomposition method to calculate an original signal local mean value, signal decomposition time is shortened, and the endpoint effect of an empirical mode algorithm is overcome.

Description

technical field [0001] The present invention relates to the field of radar signal detection, and more specifically, to a method for detecting vital signs based on millimeter-wave radar. Background technique [0002] Vital sign parameters are common indicators used to judge whether the life activities of the human body are normal or not, mainly including heartbeat, respiration, body temperature, blood pressure, end-tidal carbon dioxide and pulse oxygen saturation, etc. Among them, heartbeat and respiration are two parameters that are particularly important. The current method mainly adopts contact detection technology. The contact detection of biomedical signals refers to the use of electrodes or sensors to directly or indirectly contact the human body to achieve the purpose of detecting medical information. In many cases, due to the limitations of various conditions and Other considerations require remote measurements of respiration and heartbeat. The current main applicat...

AI Technical Summary

Problems solved by technology

However, the frequency of the heartbeat and breathing signals to be processed is relatively close, and the heartbeat signal is relatively weak. The traditional time-frequency signal analysis method cannot accurately express the change of the frequency of the signal at each time point, and even the breathing It can be distinguished from the heartbeat signal; the traditional Fourier transform needs to use a large amount of data for experiments, and the resolution of this method is relatively low

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