Sleep scene-oriented robust millimeter wave radar heart rate estimation method

Abstract

The invention discloses a sleep scene-oriented robust millimeter wave radar heart rate estimation method, which comprises the following steps of: acquiring data of a detected object through a radar, and processing pulses acquired by the radar to obtain a phase sequence; performing band-pass filtering on the phase sequence to obtain a heart rate signal; performing Fourier transform on the heart rate signal to obtain a heart rate spectrum; evaluating the confidence coefficient of the heart rate spectrum to obtain the confidence coefficient; and estimating the target heart rate based on the confidence coefficient. According to the method, original data are collected through a radar, distance dimension imaging, target phase extraction, spectrum confidence judgment and heart rate estimation are carried out, and different heart rate estimations are adopted according to different signal spectrum qualities. By means of the method, the signal at the target true value position can be effectively obtained, the influence of poor signal quality on the output result is avoided, and therefore the robust heart rate measurement result is obtained.

Description

Technical Field

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

[0002] Cardiogenic diseases are one of the main chronic diseases faced by the elderly. Continuously monitoring the heart rate of the elderly is an important way to manage the health of elderly cardiogenic chronic diseases.

[0003] Among the existing heart rate monitoring devices, millimeter-wave radars have received increasing attention due to their advantages such as large detection range, strong privacy, and non-contact. For example, the technical solution described in the publication number: "CN115813363A": "Obtain an intermediate-frequency signal containing the displacement information of the human chest cavity using a millimeter-wave radar; perform preprocessing on 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; estimate the heart rate value through power spectrum estimation." The publication number "CN112674738A" proposes: "Obtain the echo signal reflected by the electromagnetic wave signal emitted by the radar when encountering an obstacle; extract the target echo signal from the echo signal, where the target echo signal is the signal reflected by the electromagnetic wave signal when encountering the human chest cavity; extract the breathing signal and heartbeat signal of the human body from the target echo signal; determine the breathing frequency of the human body based on the breathing signal, and determine the heartbeat frequency of the human body based on the heartbeat signal." However, in the existing public methods, generally after clutter suppression, a target detection algorithm is used to select a certain resolution cell as the target position, and the heart rate spectrum is obtained through Fourier transform, and then the maximum value of the heart rate spectrum is extracted to obtain the heart rate. There are mainly two problems with such methods: First, during the heart rate measurement process, the breathing of the human body will cause the body position to fluctuate, and thus the position of the strongest point of the radar echo will also change. Extracting only a single range cell 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, and the heart rate signal is extremely weak, the spectrum of the extracted heart rate signal may have poor quality at this time, and there is a large error in the heart rate value estimated using the peak point method, and direct output may affect the assessment of the health status of the elderly. Therefore, there are still unsolved problems for the stable estimation of the heart rate. Summary of the Invention

[0004] The purpose of the present invention is to provide a robust millimeter-wave radar heart rate estimation method for sleep scenarios to solve the problems of inaccurate measurement and large errors in the existing technology for monitoring the heart rate of the elderly in the background art.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: A robust millimeter-wave radar heart rate estimation method for sleep scenarios, comprising the following steps: Step S1, collecting data of the detected object through the radar, where the data includes M pulses, and each pulse includes N sampling points; Step S2, processing the pulses collected by the radar to obtain a phase sequence ; Step S3, performing band-pass filtering on the phase sequence to obtain a heart rate signal ; Step S4, transforming the heart rate signal to obtain a heart rate spectrum SF; Step S5, evaluating the confidence level of the heart rate spectrum SF to obtain a confidence level SV; Step S6, estimating the target heart rate based on the confidence level SV, including: Step S601, if , where is the high confidence threshold, then define the spectrum at this time as high confidence, and the heart rate at this time can be estimated and output by the position of the maximum peak point: Step S602, if , where is the high confidence threshold, is the low confidence threshold, define the spectrum at this time as medium confidence, and the heart rate at this time is estimated by selecting K peaks; Step S603, if where, is the low confidence threshold, define the spectrum at this time as low confidence, and the heart rate output value directly adopts the output value of the previous moment.

[0006] According to the above technical solution, in step S2, the specific process of processing the pulses collected by the radar to obtain a phase sequence is as follows: Step S201, performing range dimension 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 position of its maximum value point , and take the first r units and the last r units centered on the position to obtain a signal ; Step S203, calculate of the phase, then for M pulses, a phase sequence can be obtained; Step S204, for the phase sequence, perform differential processing to suppress the influence of low-frequency motion and obtain a new phase sequence 。

[0007] According to the above technical solution, in step S3, the phase sequence is band-pass filtered to obtain the heart rate signal Specifically: Among them, are the filter coefficients of the band-pass filter; represents the convolution operation.

[0008] According to the above technical solution, in step S5, the confidence level is defined as: Among them: represents the maximum peak amplitude of the extracted signal, represents the second largest peak amplitude of the extracted signal, represents the starting position in the heart rate spectrum, represents the ending position in the heart rate spectrum SF.

[0009] According to the above technical solution, in step S601, it is calculated and output through the position of the maximum peak point, specifically: Among them: represents the calculated heart rate, L represents the total number of sampling points of the spectrum, represents the position of the maximum peak point; is the interval time between adjacent pulses.

[0010] According to the above technical solution, in step S602, it is estimated by selecting K peaks, and the specific process is as follows: Step S6021, extract K peaks and record their position serial 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: Among them: represents the heart rate value selected at the current moment, represents the corresponding frequency value, represents the heart rate output value of the previous moment; Step S6024, calculate the heart rate output value at the current moment: Among them: is the filtering coefficient.

[0011] According to the above technical solution, in step S603, the heart rate output value adopts the output value of the previous moment, specifically: where represents the heart rate output value of the previous moment.

[0012] Compared with the prior art, the present invention has the following beneficial effects: In the method of the present invention, raw data is collected by radar, distance dimension imaging, target phase extraction, spectral confidence judgment and heart rate estimation are performed, and different heart rate estimations are adopted according to different signal spectral qualities. Through the method of the present invention, the signal at the true position of the target can be effectively obtained, and the influence of poor signal quality on the output result can be avoided, so as to obtain a robust heart rate measurement result. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flowchart of the method of the present invention; Figure 2 is a typical one-dimensional range profile; Figure 3 is a typical target phase sequence; Figure 4 is a typical heart rate signal; Figure 5 is a typical heart rate spectrum; Figure 6 is a typical heart rate estimation result obtained by using the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0015] Embodiment 1 As Figure 1 shown, a robust millimeter-wave radar heart rate estimation method for sleep scenarios includes the following steps: Step S1, collecting data of the detected object through radar, where the data includes M pulses, and each pulse includes N sampling points; Step S2, processing the pulses collected by the radar to obtain a phase sequence ; Step S3, performing band-pass filtering on the phase sequence to obtain a heart rate signal ; Step S4: Perform Fourier transform on the heart rate signal with a length of to obtain the heart rate spectrum SF; Step S5: Evaluate the confidence of the heart rate spectrum to obtain the confidence SV; Step S6: Estimate the target heart rate based on the confidence SV, including: Step S601: If , where is the high confidence threshold, then define the spectrum at this time as high confidence, and the heart rate at this time can be estimated and output by the position of the maximum peak point: Step S602: If , where is the high confidence threshold, is the low confidence threshold, define the spectrum at this time as medium confidence, and the heart rate at this time is estimated by selecting K peaks; Step S603: If , where, is the low confidence threshold, define the spectrum at this time as low confidence, and the heart rate output value directly adopts the output value of the previous moment.

[0016] In the method of the present invention, the original data is collected by radar, distance dimension imaging, target phase extraction, spectrum confidence judgment and heart rate estimation are carried out, and different heart rate estimations are adopted according to different signal spectrum qualities. Through the method of the present invention, the signal at the true position of the target can be effectively obtained, and the influence of poor signal quality on the output result can be avoided, so as to obtain a robust heart rate measurement result.

[0017] Embodiment 2 This embodiment provides a specific implementation manner: A robust millimeter-wave radar heart rate estimation method for sleep scenarios, including the following steps: Step S1: Collect data of the detected object through radar. Assume that the data contains M pulses, and each pulse contains N sampling points, where the typical value .

[0018] Step S2: Perform distance dimension imaging on each pulse collected by the radar to obtain the one-dimensional range image of the observed scene , as Figure 2 shown.

[0019] Step S3: For each , extract the position of its maximum value point, and accumulate the surrounding cells to obtain the signal , where the typical value ; Step S4, calculate phase, then for M pulses, the target phase sequence can be obtained, as shown in Figure 3 ; Step S5, perform differential processing on the phase sequence to suppress the influence of low-frequency motion and obtain a new phase sequence ; Step S6, as shown in Figure 4 , perform band-pass filtering on the phase sequence to obtain the heart rate signal , where are the filter coefficients of the band-pass filter. By setting its order , the first cut-off frequency and the second cut-off frequency , it can be obtained. The normal heart rate is generally between 50 and 100 beats per minute. Therefore, and can typically be taken as 0.8 Hz and 1.7 Hz; represents the convolution operation.

[0020] Step S7, as shown in Figure 5 , perform Fourier transform of length L on the heart rate signal to obtain the heart rate spectrum SF. The typical value of L is 1024.

[0021] Step S8, evaluate the confidence of the heart rate spectrum, and this confidence is defined as: where: represents the maximum peak amplitude of the extracted signal, represents the second largest peak amplitude of the extracted signal, represents the starting position in the heart rate spectrum SF (typical value 5), represents the ending position in the heart rate spectrum SF (typical value 60).

[0022] Step S9, as shown in Figure 6 , based on the confidence SV, adopt different strategies to estimate the target heart rate: Step S901, if , where is the high confidence threshold, then define the spectrum at this time as high confidence. At this time, the heart rate can be calculated and output through the position of the maximum peak point: where: represents the calculated heart rate, L represents the total number of sampling points of the spectrum, represents the position of the maximum peak point, is the time interval between adjacent pulses.

[0023] Step S902, if , where is the high confidence threshold, is the low confidence 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: Step S902-a, extract K peaks and record their position numbers ; Step S902-b, calculate the frequencies corresponding to the K peaks: Step S902-c, select the value closest to the heart rate of the previous frame: Where: represents the heart rate value selected at the current moment, represents the corresponding frequency value, represents the heart rate output value of the previous moment.

[0024] Step S902-d, calculate the heart rate output value at the current moment: Where: is the filtering coefficient, and the typical value is taken as 0.9.

[0025] Step S903, if , where, is the low confidence threshold, and the spectrum at this time is defined as low confidence. The heart rate output value at this time directly adopts the output value of the previous moment: Where, represents the heart rate output value of the previous moment.

[0026] It should be noted that in this article, relational terms such as first and second are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0027] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope 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 pulses, and each pulse includes 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 Transform to obtain the heart rate spectrum; Step S5, evaluating the confidence of the heart rate spectrum to obtain a confidence SV; Step S6, estimating the target heart rate based on the confidence level SV, includes: Step S601: If ,in is the high confidence threshold, the spectrum at this time is defined as high confidence, and 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, is the low confidence threshold, and the spectrum at this time is defined as medium confidence. The heart rate at this time is estimated by selecting peaks; Step S603: If ,in, is the low confidence threshold, defining 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.

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 take the position q as the center, take the first r units and the last r units to get the signal ; Step S203, calculate The phase of the pulse can be obtained as ; Step S204: perform differential processing on the phase sequence 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 Perform bandpass filtering to obtain the heart rate signal Specifically: Where, is the filter coefficient of the bandpass filter; Represents a convolution operation.

4. The method for robust millimeter wave radar heart rate estimation for sleeping scenes according to claim 3, characterized in that: In step S5, the confidence is defined as: 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, Indicates the end position in the heart rate spectrum SF.

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

6. The method for robust millimeter wave radar heart rate estimation for sleeping scenes according to claim 5, characterized in that: In step S602, estimation is performed by selecting a peak value, and the specific process is as follows: Step S6021, extract peak values ​​and record their position numbers ; Step S6022, calculate the frequency corresponding to the peak value : 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 last moment; Step S6024, calculate the heart rate output value at the current moment: in: is the filter coefficient.

7. The method for robust millimeter wave radar heart rate estimation for sleeping scenes according to claim 6, 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 last moment.

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