A robust millimeter-wave radar heart rate estimation method for sleeping scenes

Abstract

The present invention discloses a robust millimeter-wave radar heart rate estimation method for sleeping scenes, comprising the following steps: collecting data of the detected object through radar, processing the pulses collected by the radar to obtain a phase sequence; bandpass filtering the phase sequence to obtain a heart rate signal; Fourier transforming the heart rate signal to obtain a heart rate spectrum; evaluating the confidence of the heart rate spectrum to obtain the confidence level; and estimating the target heart rate based on the confidence level. The method of the present invention collects raw data through radar, performs distance-dimensional imaging, target phase extraction, spectrum confidence judgment, and heart rate estimation, and adopts different heart rate estimates according to different signal spectrum qualities. The method of the present invention can effectively obtain the signal at the target true value position, and avoid the influence of poor signal quality on the output result, thereby obtaining a robust heart rate measurement result.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and specifically provides a robust millimeter-wave radar heart rate estimation method for sleeping scenes. Background Art

[0002] Cardiogenic disease is one of the main chronic diseases faced by the elderly. Continuous monitoring of the elderly's heart rate is an important way to manage the health of the elderly with chronic cardiogenic diseases.

[0003] Among the existing heart rate monitoring devices, millimeter-wave radar has gained increasing attention due to its advantages such as large detection range, strong privacy, and contactless nature. For example, the technical solution described in the publication number "CN115813363A" is as follows: "Use millimeter-wave radar to obtain an intermediate frequency signal containing displacement information of the human chest cavity; pre-process the intermediate frequency signal to obtain a chest wall displacement signal; decompose the chest wall displacement signal and extract the second harmonic frequency band; perform weighted reconstruction on the extracted second harmonic signal; and obtain the heart rate value through power spectrum estimation." The publication number "CN112674738A" proposes: "Obtain an echo signal when the electromagnetic wave signal emitted by the radar is reflected by an obstacle; extract a target echo signal from the echo signal, wherein the target echo signal is a signal reflected by the electromagnetic wave signal from the human chest cavity; extract the human breathing signal and heartbeat signal from the target echo signal; determine the human breathing frequency based on the breathing signal, and determine the human heartbeat frequency based on the heartbeat signal."

[0004] However, existing public methods generally use a target detection algorithm to select a resolution unit as the target position after clutter suppression, obtain the heart rate spectrum through Fourier transform, and then obtain the heart rate by extracting the maximum value of the heart rate spectrum. This type of method has two main problems: First, during the heart rate measurement process, human breathing causes the body position to fluctuate, and the position of the strongest point of the radar echo will also change. Extracting only a single distance unit may deviate from the chest position, resulting in the absence of a heart rate signal in the extracted signal; second, during the measurement process, due to non-ideal factors such as human micro-movement, environmental interference, and posture changes, the heart rate signal is extremely weak. At this time, the extracted heart rate signal spectrum may be of poor quality. The heart rate value estimated using the peak point method has a large error, and direct output may affect the assessment of the health status of the elderly. Therefore, there are still unresolved issues in the stable estimation of heart rate. Summary of the Invention

[0005] The purpose of the present invention is to provide a robust millimeter-wave radar heart rate estimation method for sleeping scenes, so as to solve the problems of inaccurate measurement and large errors in heart rate monitoring of the elderly in the existing technology proposed in the background art.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A robust millimeter-wave radar heart rate estimation method for sleeping scenes includes the following steps:

[0008] Step S1, collecting data of the detected object through radar, the data includes M pulses, and each pulse includes N sampling points;

[0009] Step S2: Process the pulses collected by the radar to obtain a phase sequence ;

[0010] Step S3, phase sequence Perform bandpass filtering to obtain the heart rate signal ;

[0011] Step S4: heart rate signal Transform to obtain the heart rate spectrum SF;

[0012] Step S5, evaluating the confidence of the heart rate spectrum SF to obtain a confidence level SV;

[0013] Step S6, estimating the target heart rate based on the confidence level SV, includes:

[0014] Step S601: If ,in If the confidence threshold is high, the spectrum at this time is defined as high confidence. The heart rate at this time can be estimated and output by the position of the maximum peak point:

[0015] Step S602: If ,in is the high confidence threshold, The confidence level is low, and the spectrum at this time is defined as medium confidence. The heart rate at this time is estimated by selecting K peaks.

[0016] Step S603: If in, The confidence low threshold is defined as the spectrum at this moment is low confidence, and the heart rate output value at this moment directly adopts the output value of the previous moment.

[0017] According to the above technical solution, in step S2, the pulses collected by the radar are processed to obtain a phase sequence as follows:

[0018] Step S201: Perform range-dimensional imaging on each pulse collected by the radar to obtain a one-dimensional range image of the observed scene. ;

[0019] Step S202: for each , extract the location of its maximum point , and position As the center, take the first r units and the last r units to get the signal ;

[0020] Step S203, calculate The phase of M pulses can be obtained as ;

[0021] Step S204: perform differential processing on the phase sequence to suppress the influence of low-frequency motion and obtain a new phase sequence. .

[0022] According to the above technical solution, in step S3, the phase sequence Perform bandpass filtering to obtain the heart rate signal Specifically:

[0023]

[0024] in, is the filter coefficient of the bandpass filter; Represents the convolution operation.

[0025] According to the above technical solution, in step S5, the confidence level is defined as:

[0026]

[0027] in: Indicates the maximum peak amplitude of the extracted signal, Indicates the second largest peak amplitude of the extracted signal, Indicates the starting position in the heart rate spectrum, Indicates the end position in the heart rate spectrum SF.

[0028] According to the above technical solution, in step S601, the position of the maximum peak point is used to calculate and output:

[0029]

[0030] in: Indicates the calculated heart rate, L indicates the total number of sampling points of the spectrum, Indicates the location of the maximum peak point; is the interval between adjacent pulses.

[0031] According to the above technical solution, in step S602, the estimation is performed by selecting a peak value, and the specific process is as follows:

[0032] Step S6021: Extract K peaks and record their position numbers ;

[0033] Step S6022: Calculate the frequencies corresponding to the K peaks :

[0034]

[0035] Step S6023: Select the value closest to the heart rate of the previous frame:

[0036]

[0037] in: Indicates the heart rate value selected at the current moment. Indicates the corresponding frequency value, Indicates the heart rate output value at the previous moment;

[0038] Step S6024, calculate the heart rate output value at the current moment:

[0039]

[0040] in: is the filter coefficient.

[0041] According to the above technical solution, in step S603, the heart rate output value adopts the output value at the previous moment, specifically:

[0042]

[0043] in, Indicates the heart rate output value at the previous moment.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The method described in this paper uses radar to collect raw data and then performs range-dimensional imaging, target phase extraction, spectrum confidence assessment, and heart rate estimation. Different heart rate estimates are used based on the quality of the signal spectrum. This method effectively captures the signal at the target's true value location while minimizing the impact of poor signal quality on the output, resulting in robust heart rate measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the method of the present invention;

[0047] Figure 2 It is a typical one-dimensional range image;

[0048] Figure 3 is a typical target phase sequence;

[0049] Figure 4 is a typical heart rate signal;

[0050] Figure 5 is a typical heart rate spectrum;

[0051] Figure 6 This is a typical heart rate estimation result obtained using the method of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Example 1

[0054] like Figure 1 As shown, a robust millimeter-wave radar heart rate estimation method for sleeping scenes includes the following steps:

[0055] Step S1, collecting data of the detected object through radar, the data includes M pulses, and each pulse includes N sampling points;

[0056] Step S2: Process the pulses collected by the radar to obtain a phase sequence ;

[0057] Step S3, phase sequence Perform bandpass filtering to obtain the heart rate signal ;

[0058] Step S4, performing a Fourier transform of length φ on the heart rate signal to obtain a heart rate spectrum SF;

[0059] Step S5, evaluating the confidence of the heart rate spectrum to obtain a confidence level SV;

[0060] Step S6, estimating the target heart rate based on the confidence level SV, includes:

[0061] Step S601: If ,in If the confidence threshold is high, the spectrum at this time is defined as high confidence. The heart rate at this time can be estimated and output by the position of the maximum peak point:

[0062] Step S602: If ,in is the high confidence threshold, The confidence level is low, and the spectrum at this time is defined as medium confidence. The heart rate at this time is estimated by selecting K peaks.

[0063] Step S603: If ,in, The confidence low threshold is defined as the spectrum at this moment is low confidence, and the heart rate output value at this moment directly adopts the output value of the previous moment.

[0064] The method described in this paper uses radar to collect raw data and then performs range-dimensional imaging, target phase extraction, spectrum confidence assessment, and heart rate estimation. Different heart rate estimates are used based on the quality of the signal spectrum. This method effectively captures the signal at the target's true value location while minimizing the impact of poor signal quality on the output, resulting in robust heart rate measurement results.

[0065] Example 2

[0066] This embodiment provides a specific implementation method:

[0067] A robust millimeter-wave radar heart rate estimation method for sleeping scenes includes the following steps:

[0068] Step S1: collect data of the detected object through radar. Assume that the data contains M pulses, each pulse contains N sampling points, where the typical value is .

[0069] Step S2: Perform range-dimensional imaging on each pulse collected by the radar to obtain a one-dimensional range image of the observed scene. ,like Figure 2 shown.

[0070] Step S3, for each , extract the location of its maximum point , and accumulate the surrounding Units receive signals , where the typical value ;

[0071] Step S4, calculate The phase of M pulses can be obtained as the target phase sequence ,like Figure 3 As shown;

[0072] Step S5: Perform differential processing on the phase sequence to suppress the influence of low-frequency motion and obtain a new phase sequence. ;

[0073] Step S6, as Figure 4 As shown, for the phase sequence Perform bandpass filtering to obtain the heart rate signal ,in is the filter coefficient of the bandpass filter, by setting its order , the first cutoff frequency and the second cutoff frequency , you can get it. The normal heart rate is generally 50~100 beats / minute, so and Typical values ​​can be taken as 0.8Hz and 1.7Hz; Represents the convolution operation.

[0074] Step S7, as Figure 5 As shown, the heart rate signal is subjected to a Fourier transform of length L to obtain a heart rate spectrum SF, where a typical value of L is 1024.

[0075] Step S8: Evaluate the confidence of the heart rate spectrum. The confidence is defined as:

[0076]

[0077] in: Indicates the maximum peak amplitude of the extracted signal, Indicates the second largest peak amplitude of the extracted signal, represents the starting position in the heart rate spectrum SF (typical value 5), Indicates the end position in the heart rate spectrum SF (typical value 60).

[0078] Step S9, as Figure 6 As shown in Figure 2, different strategies are adopted to estimate the target heart rate based on the confidence SV:

[0079] Step S901: If ,in If the confidence threshold is high, the spectrum at this time is defined as high confidence. The heart rate at this time can be calculated and output by the position of the maximum peak point:

[0080]

[0081] in: Indicates the calculated heart rate, L indicates the total number of sampling points of the spectrum, Indicates the location of the maximum peak point, is the interval between adjacent pulses.

[0082] Step S902: If ,in is the high confidence threshold, The confidence level is defined as the low threshold, and the spectrum at this time is defined as medium confidence. The heart rate at this time is estimated by selecting K (typical value 10) peaks. The specific process is as follows:

[0083] Step S902-a, extract K peaks and record their position numbers ;

[0084] Step S902-b, calculate the frequencies corresponding to the K peaks:

[0085]

[0086] Step S902-c: Select the value closest to the heart rate of the previous frame:

[0087]

[0088] in: Indicates the heart rate value selected at the current moment. Indicates the corresponding frequency value, Indicates the heart rate output value at the previous moment.

[0089] Step S902-d, calculate the heart rate output value at the current moment:

[0090]

[0091] in: is the filter coefficient, and its typical value is 0.9.

[0092] Step S903: If ,in, The confidence low threshold defines the spectrum at this time as low confidence, and the heart rate output value at this time directly adopts the output value of the previous moment:

[0093]

[0094] in, Indicates the heart rate output value at the previous moment.

[0095] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0096] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A robust millimeter-wave radar heart rate estimation method for sleeping scenes, characterized by: The following steps are involved: Step S1, collecting data of the detected object through radar, the data includes M pulses, and each pulse includes N sampling points; Step S2: Process the pulses collected by the radar to obtain a phase sequence ; Step S3, phase sequence Perform bandpass filtering to obtain the heart rate signal ; Step S4: heart rate signal Perform the transformation to obtain the heart rate spectrum SF; Step S5: Evaluate the confidence level of the heart rate spectrum SF to obtain the confidence level SV; the confidence level is defined as: in: Indicates the maximum peak amplitude of the extracted signal, Indicates the second largest peak amplitude of the extracted signal, Indicates the starting position in the heart rate spectrum, Indicates the end position in the heart rate spectrum SF; Step S6, estimating the target heart rate based on the confidence level SV, includes: Step S601: If ,in If the confidence threshold is high, the spectrum at this time is defined as high confidence. The heart rate at this time can be estimated and output by the position of the maximum peak point: Step S602: If ,in is the high confidence threshold, The confidence level is low, and the spectrum at this time is defined as medium confidence. The heart rate at this time is estimated by selecting K peaks. The specific process of estimating by selecting K peaks is as follows: Step S6021: Extract K peaks and record their position numbers ; Step S6022: Calculate the frequencies corresponding to the K peaks : Step S6023: Select the value closest to the heart rate of the previous frame: in: Indicates the heart rate value selected at the current moment. Indicates the corresponding frequency value, Indicates the heart rate output value at the previous moment; Step S6024, calculate the heart rate output value at the current moment: in: is the filter coefficient Step S603: If ,in, The confidence low threshold is defined as the spectrum at this moment is low confidence, and the heart rate output value at this moment directly adopts the output value of the previous moment.

2. The method for robust millimeter-wave radar heart rate estimation for sleeping scenes according to claim 1, characterized in that: In step S2, the pulses collected by the radar are processed to obtain a phase sequence as follows: Step S201: Perform range-dimensional imaging on each pulse collected by the radar to obtain a one-dimensional range image of the observed scene. ; Step S202: for each , extract the location of its maximum point , and position As the center, take the first r units and the last r units to get the signal ; Step S203, calculate The phase of M pulses can be obtained as ; Step S204: for the phase sequence , perform differential processing to suppress the influence of low-frequency motion and obtain a new phase sequence .

3. The method for robust millimeter-wave radar heart rate estimation for sleeping scenes according to claim 2, characterized in that: In step S3, the phase sequence is band-pass filtered to obtain the heart rate signal Specifically: Where h is the filter coefficient of the bandpass filter; Represents the convolution operation.

4. The method for robust millimeter-wave radar heart rate estimation for sleeping scenes according to claim 1, characterized in that: In step S601, the position of the maximum peak point is used to calculate and output: in: Indicates the calculated heart rate, L indicates the total number of sampling points of the spectrum, Indicates the location of the maximum peak point; is the interval between adjacent pulses.

5. The method for robust millimeter-wave radar heart rate estimation for sleeping scenes according to claim 1, characterized in that: Step S603: The heart rate output value adopts the output value of the previous moment, specifically: in, Indicates the heart rate output value at the previous moment.

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