Noncontact detecting based multi-target human respiratory signal monitoring method and device

Abstract

The invention discloses a noncontact detecting based multi-target human respiratory signal monitoring method and device. The method comprises the following steps: 1) detecting a target area through a noncontact detecting system to obtain a target signal containing a multi-target human respiratory signal; 2) separating the obtained target signal based on a window averaging-empirical mode decomposition algorithm to obtain a plurality of mode components; 3) performing spectral analysis on each obtained mode component; screening to obtain human respiratory signals based on the spectral analysis result; recognizing each screen human respiratory signal to obtain respiratory signals corresponding to different target human bodies. The device comprises a signal detecting module, a WA-EMD signal separating module and a multi-target signal recognizing module. The method and the device have the advantages that the method is simple; the multi-target human respiratory signals can be effectively separated and monitored; the mode mixing resistance is high, and the noise resistance is high; in addition, the separating accuracy and the separating efficiency are high.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a method and device for monitoring multi-target human breathing signals based on non-contact detection. Background technique [0002] The breathing signal of the human body is an important indicator to measure the health status of the human body. The monitoring of the human breathing signal is of great significance in medical monitoring and family monitoring. Through real-time monitoring of human respiratory information, the respiratory signal is used as an indicator to judge the severity and danger of the patient, so that the patient's condition and development trend can be understood in time, and abnormal conditions can be found in time. [0003] For the detection of human respiratory signals, there are currently two detection methods, one is contact detection, such as the use of breathing belts, etc., this type of detection needs to be fixed on the human body, the op...

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Problems solved by technology

[0004] For the signal analysis methods in the non-contact detection method, the current time-frequency analysis method, FFT analysis method, wavelet analysis method and EMD (empirical mode decomposition) algorithm, etc., among them, because the breathing signal frequency of many people is very close, the traditional time-frequency analysis method The analysis method cannot accurately represent the frequency change of the signal at each time point, and even cannot distinguish the respiratory signal; for the FFT transformation method, it requires a large number of experiments to obtain data, analyze and process them, and the implementation process is complicated. And more importantly, the resolution is low; the wavelet analysis method will generate many harmonics, which will affect the detection of the respiratory signal, so it is not suitable for the detection and analysis of the respiratory signal; the human respiratory signal is accompanied by random noise, and the breathing between different people The frequencies are very close, and the EMD algorithm needs to rely heavily on the mean value of the extreme points of the signal. Therefore, there are problems such as mode aliasing and poor noise robustness when decomposing the signal, making it impossible to effectively separate the respiratory frequency.

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