CN-121996965-A - Non-contact life signal processing method in strong clutter environment

CN121996965ACN 121996965 ACN121996965 ACN 121996965ACN-121996965-A

Abstract

The invention provides a non-contact life signal processing method in a strong clutter environment, which belongs to the field of data processing, and comprises the steps of marking extreme points and first and last signal data in a signal data sequence of a user as data to be corrected; according to the numerical value difference and the acquisition time difference of each piece of data to be corrected and the left and right sides of the data to be corrected, the amplitude adjustment parameter and the slope adjustment parameter of each piece of data to be corrected are obtained, the numerical value of the data to be corrected is combined, the correction value of the data to be corrected is obtained, and then the upper envelope and the lower envelope are obtained. The invention aims to solve the problems that the upper envelope line and the lower envelope line obtained through extreme points in a signal data sequence are greatly influenced by noise due to the fact that noise in the environment where a user is located has a great influence on the signal data of the user, so that the signal data of the user are processed through an EMD algorithm, and the accuracy of an obtained heart rate signal and a respiratory signal is low.

Inventors

CHEN JIAWEN
WEI JIAQI
SUN XIANGHONG

Assignees

西北大学

Dates

Publication Date: 20260508
Application Date: 20260127

Claims (8)

1. The non-contact life signal processing method in strong clutter environment is characterized by comprising the following steps: Acquiring a signal data sequence of a user; the method comprises the steps of recording extreme points and first and last signal data in a signal data sequence of a user as data to be corrected, obtaining amplitude differences of each data to be corrected and surrounding data to be corrected according to the numerical value differences of each data to be corrected and the data to be corrected on the left side and the right side of the data to be corrected; obtaining the slope of each piece of data to be corrected according to the difference between the value of each piece of data to be corrected and the data to be corrected on the left side and the right side of the data to be corrected and the difference of the acquisition time; Judging whether the numerical value of each data to be corrected is corrected according to whether the amplitude difference between each data to be corrected and the surrounding data to be corrected is larger than the average value of the amplitude differences between all the data to be corrected and the surrounding data to be corrected, if not, the numerical value of the data to be corrected is the corrected value, if so, the corrected value of the data to be corrected is obtained according to the average value of the amplitude differences between all the data to be corrected and the surrounding data to be corrected, the numerical value of the data to be corrected, the amplitude adjustment parameter and the slope adjustment parameter, the corrected value of the data to be corrected is obtained, and further an upper envelope line, a lower envelope line and a corrected signal data sequence are obtained, and the breathing signal and the heart rate signal of a user are obtained through an EMD algorithm.
2. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein the specific calculation formula for obtaining the amplitude difference between each data to be corrected and the surrounding data to be corrected is as follows: in the formula, Represent the first The amplitude difference between the data to be corrected and the surrounding data to be corrected, Represent the first The value of the data to be corrected, Represent the first The value of the data to be corrected, Represent the first The value of the data to be corrected, Representing an absolute value function.
3. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein the specific calculation formula for obtaining the amplitude adjustment parameter of each data to be corrected is as follows: in the formula, Represent the first The amplitude adjustment parameters of the data to be corrected, Represent the first The amplitude difference between the data to be corrected and the surrounding data to be corrected, Representing the average of the differences in amplitude of all the data to be corrected from the surrounding data to be corrected, Represents the maximum value of the difference in amplitude between all the data to be corrected and the surrounding data to be corrected, Indicating the iferson brackets.
4. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein the specific calculation formula for obtaining the slope of each data to be corrected is as follows: in the formula, Represent the first The slope of the data to be corrected is, Represent the first The value of the data to be corrected, Represent the first The value of the data to be corrected, Represent the first The value of the data to be corrected, Represent the first The acquisition time of the data to be corrected, Represent the first The acquisition time of the data to be corrected, Represent the first The acquisition time of the data to be corrected, Representing an absolute value function.
5. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein the specific calculation formula for obtaining the slope adjustment parameter of each data to be corrected is as follows: in the formula, Represent the first The slope adjustment parameters of the data to be corrected, Represent the first The slope of the data to be corrected is, Representing the average of the slopes of all the data to be corrected, Which means that by means of 180 degrees, Representing an arctangent function.
6. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein if no correction is performed, the value of the data to be corrected is the specific calculation formula of the correction value thereof is as follows: in the formula, Represent the first A correction value of the data to be corrected, Represent the first The value of the data to be corrected, Represent the first The amplitude difference between the data to be corrected and the surrounding data to be corrected, And representing the average value of the amplitude differences of all the data to be corrected and the surrounding data to be corrected.
7. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein the specific calculation formula of the correction value for obtaining the data to be corrected is as follows: When (when) In the time-course of which the first and second contact surfaces, In the formula, Represent the first A correction value of the data to be corrected, Represent the first The value of the data to be corrected, Represent the first The slope adjustment parameters of the data to be corrected, Represent the first The amplitude difference between the data to be corrected and the surrounding data to be corrected, Representing the average of the differences in amplitude of all the data to be corrected from the surrounding data to be corrected, Represent the first And the amplitude adjustment parameters of the data to be corrected.
8. The method for processing non-contact vital signals in a strong clutter environment according to claim 1, wherein the specific steps of obtaining the upper and lower envelopes are as follows: If at first The value of the data to be corrected is a maximum point before correction, so that when the upper and lower envelopes are obtained, the first The data to be corrected are also used as a maximum value point after correction; if the first is The value of the data to be corrected is a minimum value point before correction, so that when the upper and lower envelopes are acquired, the first The data to be corrected are also used as a minimum value point after correction; Before correcting all the data to be corrected, if the 2 nd data to be corrected is a maximum value point, the last data to be corrected is taken as a minimum value point after correction when the upper envelope and the lower envelope are acquired, and if the 2 nd data to be corrected is a minimum value point, the last 1 data to be corrected is taken as a maximum value point after correction when the upper envelope and the lower envelope are acquired; Before all the data to be corrected are corrected, if the 2 nd data to be corrected is a maximum point, the 1 st data to be corrected is taken as a minimum point after correction when the upper envelope and the lower envelope are acquired; Modifying the value of each piece of data to be modified into a modification value of the data in a signal data sequence of a user, and obtaining an upper envelope line in an EMD algorithm according to the data to be modified which is taken as a maximum value point when the upper envelope line and the lower envelope line are obtained; And obtaining a lower envelope curve in the EMD algorithm according to the data to be corrected serving as the minimum value point when the upper envelope curve and the lower envelope curve are obtained.

Description

Non-contact life signal processing method in strong clutter environment Technical Field The invention belongs to the field of data processing, and particularly relates to a non-contact vital signal processing method in a strong clutter environment. Background The invention relates to the technical field of radar signal processing and biomedical engineering intersection, in particular to a non-contact vital sign monitoring method based on a Frequency Modulation Continuous Wave (FMCW) radar or an Ultra Wideband (UWB) radar, which is suitable for extracting weak respiratory and heartbeat signals with high precision under the environments of strong clutter and low signal to noise ratio. The non-contact life signal monitoring technology, in particular to the extraction of respiratory and heartbeat frequencies through radar sensing thoracic cavity micro-motion, has wide application prospect in the fields of medical monitoring, disaster search and rescue, sleep monitoring and the like. However, in practical applications, such as respiratory and heartbeat vital sign signals are often disturbed by strong ambient noise, especially in strong clutter, low signal-to-noise environments, where these signals are prone to noise inundation. The traditional processing method, such as fourier transform, separates regular mixed periodic signals, but the life signals affected by strong clutter are often less regular periodic, so that the fourier transform cannot effectively cope with the problems, and the signals are not thoroughly decomposed, so that the life signals are difficult to distinguish clearly. The performance of the traditional method such as simple band-pass filtering, fourier transformation and the like is drastically reduced in a strong clutter environment, so that the life signal of a user is difficult to accurately separate from signals collected by other sensors such as a mattress test sensor or a PPG photo-volume pulse wave and the like in the current technology. Therefore, a processing method capable of effectively suppressing strong clutter and accurately demodulating weak vital signals is needed. Since EMD is suitable for decomposing complex biological signals into different eigenmode functions, the invention extracts the needed weak biological signals from the acquired original signals through EMD algorithm. However, in a strong noise environment, when an EMD algorithm is used for constructing the upper envelope and the lower envelope of a signal, the influence of abnormal extreme points is easy to cause the overshoot or undershoot of the envelope, so that the precision and the accuracy of signal decomposition are affected, and the error combination of signal components is caused, so that the final life signal detection effect is affected. When the acquired original signals are decomposed through the current EMD algorithm, the respiratory signals and heart rate signals obtained through decomposition have a large difference with actual signals. Disclosure of Invention In order to solve the problem that when a required signal is extracted from an original signal of a user acquired in a non-contact user mode, the original signal possibly contains noise, so that the upper envelope line and the lower envelope line acquired according to extreme points in the original signal are greatly influenced by the noise, and a large difference exists between the extracted signal and an actually required signal when the required signal is extracted from the original signal through an EMD algorithm. In order to achieve the above object, the present invention provides the following technical solutions: Acquiring a signal data sequence of a user; the method comprises the steps of recording extreme points and first and last signal data in a signal data sequence of a user as data to be corrected, obtaining amplitude differences of each data to be corrected and surrounding data to be corrected according to the numerical value differences of each data to be corrected and the data to be corrected on the left side and the right side of the data to be corrected; obtaining the slope of each piece of data to be corrected according to the difference between the value of each piece of data to be corrected and the data to be corrected on the left side and the right side of the data to be corrected and the difference of the acquisition time; Judging whether the numerical value of each data to be corrected is corrected according to whether the amplitude difference between each data to be corrected and the surrounding data to be corrected is larger than the average value of the amplitude differences between all the data to be corrected and the surrounding data to be corrected, if not, the numerical value of the data to be corrected is the corrected value, if so, the corrected value of the data to be corrected is obtained according to the average value of the amplitude differences between all the data to be corrected and the surrounding data