Random interference elimination method and system applied to human physiological information monitoring radar

Abstract

The invention discloses a random interference elimination method and system applied to a human body physiological information monitoring radar. The method comprises the steps that a millimeter wave radar is used for transmitting a frequency modulation continuous wave signal to a human body target to obtain an echo signal; obtaining a maximum signal intensity value and breathing waveform energy according to the echo signal; obtaining an intermediate frequency digital signal according to the echo signal, and obtaining an estimated respiration value and an estimated heartbeat value through spectrum estimation; calculating the confidence coefficient of the estimated respiration value and the estimated heartbeat value, setting a confidence coefficient index threshold value, comparing the calculated confidence coefficient with the confidence coefficient index threshold value, and taking a comparison result as a preprocessed heartbeat value and a preprocessed respiration value; the maximum signal intensity value and the breathing waveform energy are used for filtering the preprocessed signals, a sudden change threshold value is set according to human physiological priori knowledge, abnormal sudden change data are filtered out, filtering processing is conducted on the filtered data through a filter, the final heartbeat value and the final breathing value are obtained, and the method is rapid and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of millimeter wave radar data processing, and in particular to a random interference elimination method and system applied to a human physiological information monitoring radar. Background Art

[0002] The physiological signals of the human body, especially the heartbeat and breathing signals, are closely related to the basic health status of the human body and have important application value in medical care, sports rehabilitation, diagnosis of mental illness, intelligent control, etc. In recent years, people have proposed human physiological information monitoring equipment based on millimeter wave radar, which can continuously and stably monitor human physiological information with high precision in a non-contact manner. It has the advantages of being non-constrained, non-invasive, portable, easy to operate, and real-time data visualization.

[0003] Millimeter wave radar further estimates heart rate and respiration values ​​by calculating the phase change of the echo signal caused by the tiny movement of the human chest and abdomen. During the monitoring process, the interference of the monitored person's environment and random body movement will affect the accuracy and stability of vital sign estimation. Therefore, it is necessary to design a method that can eliminate random interference.

[0004] Medical research shows that the breathing rate of an adult is 0.1-0.5HZ, and the heart rate is 0.8-2HZ. Under normal circumstances, the heart rate value will fluctuate, but large changes in a short period of time are likely to be caused by random interference. Random interference can cause large errors in millimeter-wave radar monitoring of human heart rate and breathing data, reducing the accuracy of the estimated heart rate and breathing signal measurement results. Summary of the invention

[0005] The technical problem to be solved by the present invention is to eliminate the large errors in the millimeter wave radar monitoring of human heartbeat and breathing data caused by random interference.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A random interference elimination method applied to a human physiological information monitoring radar, comprising:

[0008] S10, using a millimeter wave radar to transmit a frequency modulated continuous wave signal toward a human target to obtain an echo signal;

[0009] S20, performing fast Fourier transform on the echo signal to determine the distance range of the human target, and searching within the distance range to obtain the maximum signal strength value; then filtering and separating the signal after fast Fourier transform through a bandpass filter to obtain a respiratory waveform, and performing exponential smoothing on the respiratory waveform to obtain the respiratory waveform energy;

[0010] S30, obtaining an intermediate frequency digital signal according to the echo signal, and obtaining an estimated respiration value and an estimated heartbeat value by spectrum estimation of the intermediate frequency digital signal;

[0011] S40, calculating the confidence of the estimated respiration value and the estimated heartbeat value, setting a confidence index threshold, and comparing the confidence of the estimated respiration value and the confidence of the estimated heartbeat value with the confidence index threshold, and using the comparison result as the preprocessed heartbeat value and respiration value;

[0012] S50, based on prior knowledge of human physiology, using the maximum signal strength value and the energy of the respiratory waveform, filtering the heartbeat value and the respiratory value obtained after the preprocessing, and filtering out useless heartbeat data and respiratory data analyzed from the environmental interference signal;

[0013] S60, setting a mutation threshold according to prior knowledge of human physiology, filtering out abnormal mutation data caused by random interference from the heartbeat data and respiratory data after filtering out environmental interference signals;

[0014] S70, taking the data filtered out in step S60 as input, filtering the data using a Savitzky-Golay filter to obtain final heart rate and respiration values.

[0015] Advantages: The present invention first pre-processes the data, then performs two filtering operations to filter out useless heartbeat data and respiratory data analyzed from environmental interference signals and abnormal mutation data caused by random interference, and finally filters the data through a Savitzky-Golay filter to make the filtered data more accurate.

[0016] Among them, the echo signal is wireless data, which varies greatly, so it is first pre-processed, then filtered twice to remove interference signals, and finally the data obtained by Savitzky-Golay filter is relatively stable. At present, digital filtering technology is mostly used to remove outliers caused by random interference. Kalman filtering is a commonly used data filtering method, which is applied to linear systems with white noise, wherein the noise part conforms to Gaussian distribution. For Kalman filter, in order to obtain accurate filtering results, the estimation of the covariance matrix of process noise and the covariance matrix of measurement noise is very important. However, in the data filtering process of real-time monitoring of vital signs, the noise levels of different monitoring objects and the same monitoring object at different times and in different environments are different. The filtering method for eliminating random interference proposed by the present invention is applied to a movable real-time monitoring vital sign monitoring device. When real-time filtering is performed on a mobile device, the accuracy of Kalman filtering will be reduced. Therefore, the present invention uses a Savitzky-Golay filter. The Savitzky-Golay filter has a small amount of calculation. As long as two parameters are set: the filter window size and the polynomial fitting order, smoothing correction processing can be performed. While effectively eliminating noise, the data trend and shape are maintained, the distortion of the original signal is reduced, and the time delay problem of the existing smoothing technology is overcome. Therefore, it is beneficial to the monitoring of heartbeat and breathing, and the changing trend of heartbeat and breathing is maintained while eliminating abnormal values, which is very important for extracting weak and critical vital signs signals.

[0017] In one embodiment of the present invention, spectrum estimation includes spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points; wherein the confidence of spectrum estimation based on fast Fourier transform is calculated, and the formula for obtaining the confidence is:

[0018]

[0019] In the formula, CM represents the confidence level, Q k It is represented by the spectrum energy of the kth peak point, and n is the window for calculating the confidence level, which takes the value of [0, N].

[0020] The confidence formula is used to obtain the confidence of the heartbeat value and the confidence of the breathing value estimated based on the spectrum of the fast Fourier transform.

[0021] In one embodiment of the present invention, obtaining the preprocessed heartbeat value in the following manner includes:

[0022] When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected;

[0023] When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected;

[0024] When V peakH When <40, the heart rate value estimated by the spectrum based on fast Fourier transform is selected;

[0025] In other cases, the heartbeat value estimated based on the spectrum of the number of peak points is selected;

[0026] Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the estimated heartbeat values ​​of the two spectra; and, "0.4", "8", "15" and "40" are the set confidence index thresholds;

[0027] Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

[0028] In one embodiment of the present invention, in S50, filtering out useless heartbeat data and breathing data parsed from the environmental interference signal includes:

[0029] The maximum signal strength value is used to determine whether a human target exists, and data with a signal strength value less than the maximum signal strength value is filtered out; the breathing waveform energy is then used to determine the breathing state of the human target, and data of the human target that is not within the monitoring range of the millimeter-wave radar is filtered out.

[0030] In one embodiment of the present invention, in S60, filtering out abnormal mutation data caused by random interference includes:

[0031] Set up a sliding window to store the estimated heart rate and respiration values; compare adjacent values ​​in the sliding window. When the difference is greater than the mutation threshold, keep the previous estimate. After T consecutive comparisons, if the difference is still greater than the mutation threshold, trust the current estimate.

[0032] In one embodiment of the present invention, in S70, obtaining the final heart rate value and respiration value includes:

[0033] According to the input data, set the filter window width;

[0034] Continue to perform i-order polynomial fitting on the window data to establish the window fitting equation;

[0035] According to the window width and the window fitting equation, a window linear equation group is formed;

[0036] Convert the window linear equations into matrix expressions and solve them by the least squares method;

[0037] Substitute the result of the least squares solution back into the matrix expression to obtain the predicted value within the window; and the predicted value within the window is used as the final heart rate value and respiration value.

[0038] The present invention also provides a random interference elimination system applied to a human physiological information monitoring radar, comprising:

[0039] The echo signal module is used to transmit a frequency modulated continuous wave signal toward a human target using a millimeter wave radar to obtain an echo signal;

[0040] The filtering index module is used to perform fast Fourier transform on the echo signal, determine the distance range of the human target, and search within the distance range to obtain the maximum signal strength value; then the signal after fast Fourier transform is filtered and separated through a bandpass filter to obtain the respiratory waveform, and the respiratory waveform is exponentially smoothed to obtain the respiratory waveform energy;

[0041] A spectrum estimation module is used to obtain an intermediate frequency digital signal according to the echo signal, and obtain an estimated respiration value and an estimated heartbeat value by spectrum estimation of the intermediate frequency digital signal;

[0042] A preprocessing module is used to calculate the confidence of the estimated respiration value and the estimated heartbeat value, set a confidence index threshold, and compare the confidence of the estimated respiration value and the confidence of the estimated heartbeat value with the confidence index threshold, and use the comparison result as the preprocessed heartbeat value and respiration value;

[0043] The first filtering module is used to filter the heartbeat value and respiratory value obtained after preprocessing based on the prior knowledge of human physiology, using the maximum signal strength value and the respiratory waveform energy, and filter out the useless heartbeat data and respiratory data analyzed in the environmental interference signal;

[0044] The second filtering module is used to set a mutation threshold according to prior knowledge of human physiology, and filter out abnormal mutation data caused by random interference from the heartbeat data and respiratory data after filtering out environmental interference signals;

[0045] The filtering module is used to take the data filtered by the second filtering module as input, perform filtering processing using a Savitzky-Golay filter, and obtain the final heartbeat value and respiration value.

[0046] In one embodiment of the present invention, spectrum estimation includes spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points; wherein the confidence of spectrum estimation based on fast Fourier transform is calculated, and the formula for obtaining the confidence is:

[0047]

[0048] In the formula, CM represents the confidence level, Q k It is represented by the spectrum energy of the kth peak point, and n is the window for calculating the confidence level, which takes the value of [0, N].

[0049] The confidence formula is used to obtain the confidence of the heartbeat value and the confidence of the breathing value estimated based on the spectrum of the fast Fourier transform.

[0050] In one embodiment of the present invention, obtaining the preprocessed heartbeat value in the following manner includes:

[0051] When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected;

[0052] When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected;

[0053] When V peakH When <40, the heart rate value estimated by the spectrum based on fast Fourier transform is selected;

[0054] In other cases, the heartbeat value estimated based on the spectrum of the number of peak points is selected;

[0055] Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the estimated heartbeat values ​​of the two spectra; and, "0.4", "8", "15" and "40" are the set confidence index thresholds;

[0056] Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

[0057] In one embodiment of the present invention, filtering out useless heartbeat data and breathing data parsed from environmental interference signals includes:

[0058] The maximum signal strength value is used to determine whether a human target exists, and data with a signal strength value less than the maximum signal strength value is filtered out; the breathing waveform energy is then used to determine the breathing state of the human target, and data of the human target that is not within the monitoring range of the millimeter wave radar is filtered out;

[0059] Filter out abnormal mutation data caused by random interference, including:

[0060] Set a sliding window to store the estimated heart rate and respiration values; compare adjacent values ​​in the sliding window. When the difference is greater than the mutation threshold, keep the previous estimate. After T consecutive comparisons, if the difference is still greater than the mutation threshold, trust the current estimate.

[0061] Compared with the prior art, the present invention has the following beneficial effects: the present invention can eliminate the problem of sudden changes in monitoring data caused by random body movements and environmental influences when monitoring human physiological information in a non-contact manner. The present invention can be applied to the fields of vital sign monitoring, health management, etc. to achieve continuous and stable physiological information monitoring, which is of great significance for improving the level of medical care and the quality of human life. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flowchart of a random interference elimination method applied to a human physiological information monitoring radar according to an embodiment of the present invention.

[0063] Figure 2 This is a line chart comparing the heartbeat value estimated based on the spectrum of peak points and the three-lead measurement value of the electrocardiogram monitor according to an embodiment of the present invention.

[0064] Figure 3 It is a line chart comparing the heartbeat value estimated based on the fast Fourier transform spectrum and the three-lead measurement value of the electrocardiogram monitor according to an embodiment of the present invention.

[0065] Figure 4 This is a line chart comparing the heartbeat value processed with prior knowledge of human physiological information and the three-lead measurement value of the electrocardiogram monitor according to an embodiment of the present invention.

[0066] Figure 5 This is a line chart comparing the heartbeat value processed by the Savitzky-Golay filter and the three-lead measurement value of the electrocardiogram monitor according to an embodiment of the present invention.

[0067] Figure 6 The present invention is a block diagram of a random interference elimination system applied to a human physiological information monitoring radar according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described in conjunction with the accompanying drawings of the specification.

[0069] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0070] See also Figure 1 As shown, the present invention provides a random interference elimination method applied to a human physiological information monitoring radar, comprising:

[0071] S10, using a millimeter wave radar to transmit a frequency modulated continuous wave signal toward a human target to obtain an echo signal.

[0072] S20, performing fast Fourier transform on the echo signal to determine the distance range of the human target, and searching within the distance range to obtain the maximum signal strength value; then filtering and separating the signal after fast Fourier transform through a bandpass filter to obtain a respiratory waveform, and performing exponential smoothing on the respiratory waveform to obtain the respiratory waveform energy.

[0073] S30, obtaining an intermediate frequency digital signal according to the echo signal, and obtaining an estimated respiration value and an estimated heartbeat value by spectrum estimation of the intermediate frequency digital signal.

[0074] In one embodiment of the present invention, there are many spectrum estimation methods. This embodiment applies spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points, and applies these two spectrum estimation methods to estimate the breathing value and the heartbeat value respectively. In addition, this embodiment does not improve these two spectrum estimation methods.

[0075] S40, calculating the confidence of the estimated respiration value and the estimated heartbeat value, setting a confidence index threshold, and comparing the confidence of the estimated respiration value and the confidence of the estimated heartbeat value with the confidence index threshold, and using the comparison result as the preprocessed heartbeat value and respiration value.

[0076] In one embodiment of the present invention, the confidence of the spectrum estimation based on the fast Fourier transform is calculated by dividing the signal power of the maximum peak and some frequency components around it by the signal power of the remaining frequency components in the frequency region spectrum, and comparing the relative strength of the peak with its background noise or other irrelevant signals. The confidence formula for spectrum estimation based on the fast Fourier transform is:

[0077]

[0078] In the formula, CM represents the confidence level, Q k It is represented as the spectrum energy of the kth peak point, and n is represented as the set window for calculating the confidence, which is [0, N]. Using the confidence formula, the confidence of the heartbeat value and the confidence of the breathing value estimated based on the fast Fourier transform spectrum are obtained respectively.

[0079] In one embodiment of the present invention, obtaining the preprocessed heartbeat value in the following manner includes:

[0080] When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0081] When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0082] When V peakH When <40, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0083] In other cases, a heartbeat value estimated based on the spectrum of the number of peak points is selected.

[0084] Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the heartbeat values ​​estimated by the two spectra. And, "0.4", "8", "15" and "40" are the set confidence index thresholds, and the confidence index thresholds are obtained based on experiments.

[0085] Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

[0086] S50, based on prior knowledge of human physiology, using the maximum signal strength value and the energy of the respiratory waveform, the heartbeat value and the respiratory value obtained after preprocessing are filtered to filter out useless heartbeat data and respiratory data analyzed from the environmental interference signal.

[0087] In one embodiment of the invention, the maximum signal strength value is used to determine whether a human target exists, and data less than the maximum signal strength value is filtered out. The maximum signal strength value is the maximum value of the signal strength of a human target detected in the echo signal within a specific distance range, and is used to determine the existence of a human target. If a human target exists within a certain distance range, the signal strength value will be very large. If there is no human target, and only the echo reflected by some objects placed in the environment, the signal strength value will be small. The maximum signal strength value can be used to filter whether there is a person within the monitoring range of the millimeter wave radar.

[0088] In one embodiment of the present invention, the breathing waveform energy is used to judge the breathing state of the human target, and the data of the human target that is not within the monitoring range of the millimeter wave radar is filtered out. The breathing waveform energy is used to judge the breathing state of the human target. If there is a human target within the monitoring range, the breathing wave energy will change significantly. When the human target breathes normally, the breathing waveform energy value range is about 100 to several thousand, and this value will drop significantly when the human body holds its breath, and generally drops below 3 when holding its breath. By detecting the value of the breathing waveform energy, the current breathing state of the human target can be preliminarily judged. At the same time, it can assist in judging whether there is a human target within the radar monitoring range. If there is no human target within the monitoring range, this value range is between 0 and 0.5.

[0089] S60, setting a mutation threshold according to prior knowledge of human physiology, filtering out abnormal mutation data caused by random interference from the heartbeat data and respiratory data after filtering out environmental interference signals.

[0090] In one embodiment of the present invention, the maximum signal strength value and the breathing waveform energy are used to first filter out the heartbeat value and breathing value extracted from the echo signal reflected by the environmental interference because the human target is not within the monitoring range of the millimeter wave radar, to ensure that the echo signal contains the physiological information of the human target, i.e., the person being detected.

[0091] In one embodiment of the present invention, a sliding window is set to store the estimated heart rate and breathing values; the adjacent values ​​in the sliding window are compared, and when the difference is greater than the mutation threshold, the previous estimated value is kept, and after T consecutive comparisons, if the difference is still greater than the mutation threshold, the estimated value of this time is trusted, and the mutation data caused by random interference can be filtered out. The specific number of comparisons is set according to the specific situation. Specifically, random interference can be such as a large movement of a human target, or an unconscious shaking of the body when sitting quietly.

[0092] S70, taking the data filtered out in step S60 as input, filtering the data using a Savitzky-Golay filter to obtain final heart rate and respiration values.

[0093] In one embodiment of the present invention, in general, wireless data changes greatly, and is first pre-processed in steps S30 and S40, then filtered in steps S50 and S60, and finally filtered by a Savitzky-Golay filter, so that the processed data will be more stable.

[0094] In one embodiment of the present invention, digital filtering technology is currently mostly used to remove outliers caused by random interference. Kalman filtering is a commonly used data filtering method, which is applied to linear systems with white noise, wherein the noise part conforms to the Gaussian distribution. For the Kalman filter, in order to obtain accurate filtering results, the estimation of the covariance matrix of the process noise and the covariance matrix of the measurement noise is very important. However, in the data filtering process of real-time monitoring of vital signs, the noise levels of different monitoring objects and the same monitoring object at different times and in different environments are different. This embodiment is applied to a movable real-time monitoring vital signs monitoring device. Real-time filtering is performed on the mobile device, and the accuracy of the Kalman filter will be reduced. Therefore, this embodiment uses the Savitzky-Golay filter. The Savitzky-Golay filter has a small amount of calculation. As long as two parameters are set: the filter window size and the polynomial fitting order, smoothing correction processing can be performed. While effectively removing noise, the data trend and shape are maintained, the distortion of the original signal is reduced, and the delay problem of the existing smoothing technology is overcome. Therefore, it is conducive to the monitoring of heartbeat and breathing. While excluding abnormal values, the changing trend of heartbeat and breathing is maintained, which is crucial for extracting weak and critical vital signs signals.

[0095] In one embodiment of the present invention, the Savitzky-Golay filter is a smoothing filtering method using the local least squares method, which extracts valuable information from the basic trend of the input data, and uses the trend to remove abnormal values ​​in the input data, so that the filtered data is more accurate and more suitable for the analysis of heartbeat and respiratory signals. The heartbeat value and the respiratory value are obtained by filtering with the Savitzky-Golay filter. The Savitzky-Golay filter replaces the input data with the fitting value, which can remove abnormal data and make the measured respiratory estimation value and heartbeat estimation value more accurate.

[0096] In one embodiment of the present invention, obtaining the final heart rate value and respiration value includes:

[0097] S71, according to the input data, set the filter window width; wherein the filter window width w is: w = 2m + 1; the input data is: x = (x -m ,x -m+1 ,...,x0,...,x m-1 ,x m ); m has no specific meaning and is only represented as a numerical index.

[0098] S72, continue to perform i-order polynomial fitting on the window data to establish a window fitting equation; the window fitting equation is as follows:

[0099] y=a0+a1x+a2x 2 +...+a i x i ;

[0100] Among them, [a0,a1,...,a i ] is represented as the fitting parameter, and y is represented as the dependent variable of the window fitting equation.

[0101] S73, based on the window width and in combination with the window fitting equation, a window linear equation group is formed, and the window linear equation group is as follows:

[0102]

[0103] Among them, e represents the error.

[0104] S74, converting the window linear equations into a matrix expression and solving it by the least square method; wherein the matrix expression is:

[0105] Y (2m+1)×1 =X (2m+1)×i ·A i×1 +E (2m+1)×1 ;

[0106] Where Y (2m+1)×1 Represented as a matrix of dependent variables in the windowed linear system, X (2m+1)×i Represented as a matrix of input data, A i×1 Expressed as a matrix of fitting parameters, E (2m+1)×1 is represented as a matrix of errors.

[0107] S74, substituting the result of the least squares method solution back into the matrix expression to obtain the predicted value within the window; and the predicted value within the window is used as the final heart rate value and respiration value.

[0108] In this embodiment, the least squares method is used to solve the matrix expression The specific acquisition formula is as follows:

[0109]

[0110] Where T stands for transpose.

[0111] And the predicted value Obtained by the following formula:

[0112]

[0113] Where B = X·(X T ·X) -1 ·X T .

[0114] When the input data is a heartbeat signal, the Savitzky-Golay filter outputs the final heartbeat value. When the input data is a breathing signal, the Savitzky-Golay filter outputs the final breathing value.

[0115] Specific example

[0116] In one embodiment of the present invention, in steps S10 and S20, the parameters of the millimeter wave radar are a starting frequency of 77 GHz, a frequency modulation bandwidth of 4 GHz, a frame period of 50 ms, and a preset distance between 0.4 m and 0.8 m. The distance range of the human target is determined by the approximate positional relationship between the millimeter wave radar and the human body, and the maximum signal strength value is obtained by searching within the distance range. In this embodiment, after experimental testing, when there is someone in the test range of 0.4 m to 0.8 m, the maximum signal strength value is above 50000000. After experimental testing, when the monitored person breathes normally within the test range of 0.4 m to 0.8 m, the range value of the respiratory waveform energy fluctuates between 100 and 3000.

[0117] In this embodiment, according to the difference between the heartbeat and the respiratory frequency, a bandpass filter is used to filter to obtain the respiratory waveform, and the energy of the respiratory waveform is calculated.

[0118] In one embodiment of the present invention, spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points are performed on the intermediate frequency digital signal in the buffer, the heart beat value and respiration value per minute are estimated, and the estimated value is compared with the heart beat value measured by three leads of the electrocardiogram monitoring device.

[0119] In this embodiment, Figure 2 This is a line graph comparing the heart rate value estimated based on the spectrum of the number of peak points and the value measured by the three-lead ECG monitor. The horizontal axis is the measurement time in seconds, with a total of 433 seconds of data. The vertical axis is the heart rate value in beats per minute. It can be seen from the line graph that the estimated heart rate value starts to increase from zero, and then fluctuates around 80 beats per minute. However, there is a certain difference compared with the heart rate value measured by the three-lead ECG monitoring device, and the value is not accurate.

[0120] In this embodiment, Figure 3The line graph compares the heart rate value estimated based on the fast Fourier transform spectrum and the three-lead measurement value of the ECG monitor. The horizontal axis is the measurement time in seconds, with a total of 433 seconds of data, and the vertical axis is the heart rate value in times per minute. It can be seen from the line graph that the estimated heart rate value fluctuates greatly, but sometimes it can keep up with the changes in the heart rate value measured by the three-lead ECG monitor.

[0121] In this embodiment, the estimated heartbeat values ​​obtained by the two spectrum estimations have their own characteristics. The confidence of the spectrum estimation based on fast Fourier transform is calculated using the confidence formula, where the heartbeat confidence window is 10 and the breathing confidence window is 5. According to experimental tests, the confidence of the spectrum estimation based on fast Fourier transform is mainly between 0.1 and 0.6 within the test range of 0.4m-0.8m.

[0122] In this embodiment, in step S40, a threshold of the confidence index is set, and one of the spectrum estimation based on fast Fourier transform or the spectrum estimation based on the number of peak points is selected as the heartbeat value and respiration value obtained by preprocessing. Specifically, the selection conditions are:

[0123] When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0124] When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0125] When V peakH When <40, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0126] In other cases, a heartbeat value estimated based on the spectrum of the number of peak points is selected.

[0127] Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the estimated heartbeat values ​​of the two spectra; and "0.4", "8", "15" and "40" are the set confidence indicator thresholds.

[0128] Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

[0129] In one embodiment of the present invention, in steps S50 and S60, based on prior knowledge of human physiological information, the maximum signal strength value and respiratory waveform energy are used as two data indicators to further filter out useless heartbeat data and respiratory data analyzed from environmental interference signals.

[0130] In one embodiment of the present invention, specifically, according to the prior knowledge of human physiological information, after multiple tests, a suitable threshold is set. When the average value of the maximum signal strength value of 100 consecutive frames of data is less than 50000000, and the average value of the breathing waveform energy of 50 consecutive frames of data is less than 30, it is considered that the data measured within the current radar monitoring range are the reflection signals of objects in the environment, and there is no human target, and these data are discarded. When there is a human target, when the average value of the breathing waveform energy of 50 consecutive frames of data is less than 3, it can be preliminarily determined that the monitored person is in a breath-holding state.

[0131] In one embodiment of the present invention, a mutation threshold is set based on prior knowledge of human physiological information, a sliding window of appropriate size is set to store data, and adjacent values ​​are compared. When it is found that the difference is greater than the threshold, the previous estimate is kept. If the difference is still greater than the threshold after multiple comparisons, the current estimate is trusted. This algorithm can filter out mutation data caused by random interference.

[0132] In one embodiment of the present invention, the frame period of the radar transmission signal is 50ms, that is, 20 frames of data are obtained in 1 second. When the monitored person is sitting still, the heart rate fluctuation within 1 second will not exceed 10 times per minute. Therefore, the mutation threshold is set to 10, and the difference between the current data and the previous data is calculated. When it is found that the difference is greater than 10, the current data is discarded and the previous data is kept. The selected data and the kept data are compared and compared within 5 seconds. When the difference is less than the mutation threshold of 10 or the difference is always greater than the mutation threshold of 10 within 10 seconds, the current data is trusted. The algorithm can filter out mutation data due to random body movement.

[0133] In this embodiment, specifically, Figure 4 It is a line graph comparing the heart rate value processed by the algorithm of prior knowledge of human physiological information and the three-lead measurement value of the ECG monitor. The horizontal axis is the measurement time in seconds, with a total of 433 seconds of data, and the vertical axis is the heart rate value in beats per minute.

[0134] According to the prior knowledge of human physiological information, after multiple tests, the appropriate threshold is set. When the average value of the maximum signal strength value of 100 consecutive frames of data is less than 50000000, and the average value of the respiratory waveform energy of 50 consecutive frames of data is less than 30, it is considered that there is no human target in the current radar monitoring range, and the measured data is the reflection signal of the object in the environment, and these data are discarded. When it is determined that there is a human target, the heartbeat value and respiratory value estimated by the confidence selection spectrum are calculated, and the difference between the current selected data and the last selected data is calculated. When the difference is greater than 10, the last selected data is kept first, and the selected data and the kept data are compared within 10 seconds. When the difference is less than 10 or the difference is greater than 10 within 10 seconds, the selected data is updated as the estimated value after processing.

[0135] In one embodiment of the present invention, in step S70, the filter window width is selected as 21, and the fitting order i is 6. According to actual test results, the best filtering effect is achieved when the fitting order is 6, and the filter window is selected as 21, that is, filtering is performed on about 1 second of data, which is conducive to real-time monitoring. Figure 5 This is a line graph comparing the heart rate value processed by the Savitzky-Golay filter and the three-lead measurement value of the ECG monitor. It can be seen that in the first 60 seconds when the millimeter-wave radar starts working, there is a large error between the estimated value and the instrument measurement value. There is also a sudden change from 60 to 180 seconds, but the error between the two gradually decreases. From 180 to 433 seconds, the accuracy of the estimated value has been significantly improved.

[0136] See also Figures 1 to 6 As shown, the present invention also provides a random interference elimination system applied to a human physiological information monitoring radar, comprising:

[0137] The echo signal module 10 is used to transmit a frequency modulated continuous wave signal toward a human target using a millimeter wave radar to obtain an echo signal.

[0138] The filtering index module 20 is used to perform fast Fourier transform on the echo signal, determine the distance range of the human target, and search within the distance range to obtain the maximum signal strength value; then the signal after fast Fourier transform is filtered and separated through a bandpass filter to obtain the respiratory waveform, and the respiratory waveform is exponentially smoothed to obtain the respiratory waveform energy.

[0139] The spectrum estimation module 30 is used to obtain an intermediate frequency digital signal according to the echo signal, and obtain an estimated respiration value and an estimated heartbeat value by spectrum estimation on the intermediate frequency digital signal.

[0140] The preprocessing module 40 is used to calculate the confidence of the estimated breathing value and the estimated heartbeat value, set a confidence index threshold, and compare the confidence of the estimated breathing value and the confidence of the estimated heartbeat value with the confidence index threshold, and use the comparison result as the preprocessed heartbeat value and breathing value.

[0141] The first filtering module 50 is used to filter the heartbeat value and respiratory value obtained after preprocessing based on prior knowledge of human physiology, using the maximum signal strength value and respiratory waveform energy, and filter out useless heartbeat data and respiratory data analyzed from the environmental interference signal.

[0142] The second filtering module 60 is used to set a mutation threshold according to prior knowledge of human physiology, and to filter out abnormal mutation data caused by random interference from the heartbeat data and respiratory data after filtering out environmental interference signals.

[0143] The filtering module 70 is used to take the data filtered by the second filtering module 60 as input, perform filtering processing using a Savitzky-Golay filter, and obtain the final heartbeat value and respiration value.

[0144] In one embodiment of the present invention, the spectrum estimation includes spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points. The confidence of the spectrum estimation based on fast Fourier transform is calculated, and the formula for obtaining the confidence is:

[0145]

[0146] In the formula, CM represents the confidence level, Q k It is represented as the spectrum energy of the kth peak point, and n is the window for calculating the confidence level, which takes the value of [0, N].

[0147] The confidence formula is used to obtain the confidence of the heartbeat value and the confidence of the breathing value estimated based on the spectrum of the fast Fourier transform.

[0148] In one embodiment of the present invention, obtaining the preprocessed heartbeat value in the following manner includes:

[0149] When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0150] When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0151] When V peakH When <40, the heart rate value estimated based on the spectrum of fast Fourier transform is selected.

[0152] In other cases, a heartbeat value estimated based on the spectrum of the number of peak points is selected.

[0153] Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the estimated heartbeat values ​​of the two spectra. And, "0.4", "8", "15" and "40" are the set confidence index thresholds.

[0154] Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

[0155] In one embodiment of the present invention, filtering out useless heartbeat data and breathing data parsed from environmental interference signals includes:

[0156] The maximum signal strength value is used to determine whether a human target exists, and data with a signal strength value less than the maximum signal strength value is filtered out; the breathing waveform energy is then used to determine the breathing state of the human target, and data of the human target that is not within the monitoring range of the millimeter-wave radar is filtered out.

[0157] Filter out abnormal mutation data caused by random interference, including:

[0158] Set a sliding window to store the estimated heart rate and respiration values; compare adjacent values ​​in the sliding window. When the difference is greater than the mutation threshold, keep the previous estimate. After T consecutive comparisons, if the difference is still greater than the mutation threshold, trust the current estimate.

[0159] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0160] The above-described embodiments merely represent implementation methods of the invention. The protection scope of the present invention is not limited to the above-described embodiments. For those skilled in the art, several modifications and improvements may be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A random interference elimination method applied to human physiological information monitoring radar, characterized in that: include: S10, using a millimeter wave radar to transmit a frequency modulated continuous wave signal toward a human target to obtain an echo signal; S20, performing fast Fourier transform on the echo signal to determine the distance range of the human target, and searching within the distance range to obtain the maximum signal strength value; The signal after fast Fourier transformation is filtered and separated by a bandpass filter to obtain a respiratory waveform, and the respiratory waveform is subjected to exponential smoothing to obtain the respiratory waveform energy; S30, obtaining an intermediate frequency digital signal according to the echo signal, and obtaining an estimated respiration value and an estimated heartbeat value by spectrum estimation of the intermediate frequency digital signal; S40, calculating the confidence of the estimated respiration value and the estimated heartbeat value, setting a confidence index threshold, and comparing the confidence of the estimated respiration value and the confidence of the estimated heartbeat value with the confidence index threshold, and using the comparison result as the preprocessed heartbeat value and respiration value; S50, based on prior knowledge of human physiology, using the maximum signal strength value and the energy of the respiratory waveform, filtering the heartbeat value and the respiratory value obtained after the preprocessing, and filtering out useless heartbeat data and respiratory data analyzed from the environmental interference signal; S60, setting a mutation threshold according to prior knowledge of human physiology, filtering out abnormal mutation data caused by random interference from the heartbeat data and respiratory data after filtering out environmental interference signals; S70, taking the data filtered out in step S60 as input, filtering the data using a Savitzky-Golay filter to obtain final heart rate and respiration values.

2. The random interference elimination method applied to human physiological information monitoring radar according to claim 1 is characterized in that: The spectrum estimation includes spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points; wherein the confidence of the spectrum estimation based on fast Fourier transform is calculated, and the formula for obtaining the confidence is: In the formula, CM represents the confidence level, Q k It is represented by the spectrum energy of the kth peak point, and n is the window for calculating the confidence level, which takes the value of [0, N]. The confidence formula is used to obtain the confidence of the heartbeat value and the confidence of the breathing value estimated based on the spectrum of the fast Fourier transform.

3. The random interference elimination method applied to human physiological information monitoring radar according to claim 2 is characterized in that: The following methods are used to obtain the preprocessed heartbeat value: When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected; When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected; When V peakH When <40, the heart rate value estimated by the spectrum based on fast Fourier transform is selected; In other cases, the heartbeat value estimated based on the spectrum of the number of peak points is selected; Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the estimated heartbeat values ​​of the two spectra; and "0.4", "8", "15" and "40" are the set confidence index thresholds; Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

4. The random interference elimination method applied to human physiological information monitoring radar according to claim 1 is characterized in that: In S50, useless heartbeat data and breathing data parsed from the environmental interference signal are filtered out, including: The maximum signal strength value is used to determine whether a human target exists, and data with a signal strength value less than the maximum signal strength value is filtered out; the breathing waveform energy is then used to determine the breathing state of the human target, and data of the human target that is not within the monitoring range of the millimeter-wave radar is filtered out.

5. The random interference elimination method applied to human physiological information monitoring radar according to claim 1 is characterized in that: In S60, abnormal mutation data caused by random interference is filtered out, including: Set up a sliding window to store the estimated heart rate and respiration values; compare adjacent values ​​in the sliding window. When the difference is greater than the mutation threshold, keep the previous estimate. After T consecutive comparisons, if the difference is still greater than the mutation threshold, trust the current estimate.

6. The random interference elimination method applied to human physiological information monitoring radar according to claim 1 is characterized in that: In S70, the final heart rate value and respiration value are obtained, including: According to the input data, set the filter window width; Continue to perform i-order polynomial fitting on the window data to establish the window fitting equation; According to the window width and the window fitting equation, a window linear equation group is formed; Convert the window linear equations into matrix expressions and solve them by the least squares method; Substitute the result of the least squares solution back into the matrix expression to obtain the predicted value within the window; and the predicted value within the window is used as the final heart rate value and respiration value.

7. A random interference elimination system applied to human physiological information monitoring radar, characterized in that: include: The echo signal module is used to transmit a frequency modulated continuous wave signal toward a human target using a millimeter wave radar to obtain an echo signal; The filtering index module is used to perform fast Fourier transform on the echo signal, determine the distance range of the human target, and search within the distance range to obtain the maximum signal strength value; then the signal after fast Fourier transform is filtered and separated through a bandpass filter to obtain the respiratory waveform, and the respiratory waveform is exponentially smoothed to obtain the respiratory waveform energy; A spectrum estimation module is used to obtain an intermediate frequency digital signal according to the echo signal, and obtain an estimated respiration value and an estimated heartbeat value by spectrum estimation of the intermediate frequency digital signal; A preprocessing module is used to calculate the confidence of the estimated respiration value and the estimated heartbeat value, set a confidence index threshold, and compare the confidence of the estimated respiration value and the confidence of the estimated heartbeat value with the confidence index threshold, and use the comparison result as the preprocessed heartbeat value and respiration value; The first filtering module is used to filter the heartbeat value and respiratory value obtained after preprocessing based on the prior knowledge of human physiology, using the maximum signal strength value and the respiratory waveform energy, and filter out the useless heartbeat data and respiratory data analyzed in the environmental interference signal; The second filtering module is used to set a mutation threshold according to prior knowledge of human physiology, and filter out abnormal mutation data caused by random interference from the heartbeat data and respiratory data after filtering out environmental interference signals; The filtering module is used to take the data filtered by the second filtering module as input, perform filtering processing using a Savitzky-Golay filter, and obtain the final heartbeat value and respiration value.

8. The random interference elimination system applied to human physiological information monitoring radar according to claim 7 is characterized in that: The spectrum estimation includes spectrum estimation based on fast Fourier transform and spectrum estimation based on the number of peak points; wherein the confidence of the spectrum estimation based on fast Fourier transform is calculated, and the formula for obtaining the confidence is: In the formula, CM represents the confidence level, Q k It is represented by the spectrum energy of the kth peak point, and n is the window for calculating the confidence level, which takes the value of [0, N]. The confidence formula is used to obtain the confidence of the heartbeat value and the confidence of the breathing value estimated based on the spectrum of the fast Fourier transform.

9. The random interference elimination system applied to human physiological information monitoring radar according to claim 8, characterized in that: The following methods are used to obtain the preprocessed heartbeat value: When V FFTH <V peakH When V CM >0.4 and V d When <8, the heart rate value estimated based on the spectrum of fast Fourier transform is selected; When V FFTH >V peakH When V CM >0.4 and V d When <15, the heart rate value estimated based on the spectrum of fast Fourier transform is selected; When V peakH When <40, the heart rate value estimated by the spectrum based on fast Fourier transform is selected; In other cases, the heartbeat value estimated based on the spectrum of the number of peak points is selected; Among them, V FFTH Expressed as the heartbeat value estimated based on the fast Fourier transform spectrum, V peakH Expressed as the heart rate value estimated based on the number of peak points, V CM Expressed as the confidence of the heartbeat value estimated based on the fast Fourier transform spectrum, V d It is represented as the difference between the estimated heartbeat values ​​of the two spectra; and "0.4", "8", "15" and "40" are the set confidence index thresholds; Furthermore, the steps for obtaining the preprocessed respiration value are the same as those for obtaining the preprocessed heartbeat value, and the same confidence index threshold is applied.

10. The random interference elimination system applied to human physiological information monitoring radar according to claim 7, characterized in that: Filter out useless heartbeat and breathing data from environmental interference signals, including: The maximum signal strength value is used to determine whether a human target exists, and data with a signal strength value less than the maximum signal strength value is filtered out; the breathing waveform energy is then used to determine the breathing state of the human target, and data of the human target that is not within the monitoring range of the millimeter wave radar is filtered out; Filter out abnormal mutation data caused by random interference, including: Set a sliding window to store the estimated heart rate and respiration values; compare adjacent values ​​in the sliding window. When the difference is greater than the mutation threshold, keep the previous estimate. After T consecutive comparisons, if the difference is still greater than the mutation threshold, trust the current estimate.

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