Heart rate detection method and device based on multi-channel linear frequency modulation continuous wave radar

Abstract

The present application discloses a heart rate detection method and device based on a multi-channel linear frequency modulation continuous wave radar. The method comprises: performing a Fourier transform of the distance dimension and the antenna dimension on the echo signal of the target position in sequence to generate a target range azimuth map; extracting the phase information of the point with the highest amplitude in the target range azimuth map and its adjacent distance points and adjacent azimuth points at the current moment to generate multi-channel data, and filtering the multi-channel data to obtain a multi-channel heartbeat signal; obtaining the heartbeat data of the heartbeat signal of each channel within a fixed time domain window at the current moment, calculating the correlation coefficient between the heartbeat data within the fixed time domain window at the current moment and the heartbeat data within the fixed time domain window after a preset time interval, and converting the heart rate result at the current moment using the time interval corresponding to the maximum value of the correlation coefficient. In this way, the interference of breathing on the heartbeat signal is eliminated, while ensuring the real-time and accuracy of heart rate detection.

Description

Technical Field

[0001] The present application relates to the field of heart rate detection technology, and in particular to a heart rate detection method and device based on a multi-channel linear frequency modulation continuous wave radar. Background Art

[0002] In the heart rate detection method of the related art, after obtaining the distance and azimuth map, the target position is determined by calculating the center of mass within the target range. After extracting the phase information, the target heart rate is estimated using bandpass filtering and FFT. However, the center of mass position may still be subject to significant respiratory interference, which may introduce a large amount of noise in the subsequent filtering process. Moreover, due to the real-time changes in heartbeat activity, estimating the heart rate using frequency domain methods such as FFT requires long-term signal estimation, which means that real-time performance cannot be guaranteed and the accuracy of the estimation cannot be guaranteed when the signal is severely distorted. If the time domain detection method of the autocorrelation peak gap is used to estimate the heart rate, and the quality of the heart rate estimation is measured by calculating the signal-to-noise ratio, although the real-time performance of the heart rate detection can be guaranteed, the accuracy is greatly affected by noise, and in many cases, it is impossible to accurately determine the peak position of the next heartbeat. Summary of the Invention

[0003] The present application provides a heart rate detection method based on a multi-channel linear frequency modulation continuous wave radar, which can simultaneously ensure the real-time performance and accuracy of heart rate detection, and is suitable for accurate non-contact heart rate estimation and real-time human vital signs monitoring.

[0004] The first aspect of the present application provides a heart rate detection method based on a multi-channel linear frequency modulation continuous wave radar, comprising the following steps: obtaining an echo signal returned after the linear frequency modulation signal hits the target position, performing Fourier transform in the distance dimension and Fourier transform in the antenna dimension on the echo signal in sequence to generate a target distance azimuth map; extracting the phase information of the point with the highest amplitude in the target distance azimuth map and its adjacent distance points and adjacent azimuth points at the current moment to generate multi-channel data, and filtering the multi-channel data to obtain a multi-channel heartbeat signal; obtaining the heartbeat data of the heartbeat signal of each channel in a fixed time domain window at the current moment, and calculating the correlation coefficient between the heartbeat data in the fixed time domain window at the current moment and the heartbeat data in the fixed time domain window after a preset time interval, and using the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment.

[0005] Optionally, in one embodiment of the present application, an echo signal returned after the linear frequency modulation signal hits the target position is obtained, and the echo signal is sequentially subjected to Fourier transform in the distance dimension and Fourier transform in the antenna dimension to generate a target distance and azimuth map, including: sending a linear frequency modulation signal, and receiving the echo signal generated at the target position through a receiving antenna; mixing the echo signal and the linear frequency modulation signal to obtain an intermediate frequency signal after matched filtering; performing Fourier transform in the distance dimension and antenna dimension on the intermediate frequency signal to obtain target distance information and target azimuth information, and generating a target distance and azimuth map based on the target distance information and the target azimuth information.

[0006] Optionally, in one embodiment of the present application, after generating the target distance and azimuth map, it also includes: accumulating the target distance and azimuth map within a first preset time, calculating the mean of each point in the target distance and azimuth map, and subtracting the accumulated mean of the first preset time from the target distance and azimuth map obtained every second preset time to obtain a target distance and azimuth map with a clear target range.

[0007] Optionally, in one embodiment of the present application, the multi-channel data is filtered to obtain a multi-channel heartbeat signal, including: filtering the multi-channel data in sequence using an IIR filter with a passband within a preset range to filter out low-frequency respiratory signals and high-frequency clutter signals to obtain the multi-channel heartbeat signal.

[0008] Optionally, in one embodiment of the present application, before using the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment, it also includes: judging whether the maximum value of the correlation coefficient is greater than the correlation coefficient threshold; if it is greater than or equal to the correlation coefficient threshold, obtaining the time interval corresponding to the maximum value of the correlation coefficient; if it is less than the correlation coefficient threshold, the heart rate result at the current moment is the heart rate result at the previous moment.

[0009] Optionally, in one embodiment of the present application, the conversion formula for converting the time interval to obtain the heart rate result at the current moment is:

[0010] bpm=60 / Δt

[0011] Where bpm is the heart rate at the current moment, and Δt is the time interval corresponding to the maximum value of the correlation coefficient.

[0012] The second aspect of the present application provides a heart rate detection device based on a multi-channel linear frequency modulation continuous wave radar, including: a first generation module, used to obtain an echo signal returned after the linear frequency modulation signal hits the target position, and perform Fourier transform in the distance dimension and Fourier transform in the antenna dimension on the echo signal in sequence to generate a target distance azimuth map; a second generation module, used to extract the phase information of the point with the highest amplitude in the target distance azimuth map and its adjacent distance points and adjacent azimuth points at the current moment, generate multi-channel data, and filter the multi-channel data to obtain a multi-channel heartbeat signal; a detection module, used to obtain the heartbeat data of the heartbeat signal of each channel in a fixed time domain window at the current moment, and calculate the correlation coefficient between the heartbeat data in the fixed time domain window at the current moment and the heartbeat data in the fixed time domain window after a preset time interval, and use the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment.

[0013] Optionally, in one embodiment of the present application, the first generating module includes: a sending unit for sending a linear frequency modulation signal and receiving the echo signal generated at the target position through a receiving antenna; a mixing unit for mixing the echo signal and the linear frequency modulation signal to obtain an intermediate frequency signal after matched filtering; a transformation unit for performing Fourier transform on the intermediate frequency signal in the distance dimension and the antenna dimension respectively to obtain target distance information and target azimuth information, and generating a target distance and azimuth map based on the target distance information and the target azimuth information.

[0014] Optionally, in one embodiment of the present application, it also includes: a correction module, which is used to accumulate the target distance and azimuth map within a first preset time after generating the target distance and azimuth map, calculate the mean of each point in the target distance and azimuth map, and subtract the accumulated mean of the first preset time from the target distance and azimuth map obtained every second preset time to obtain a target distance and azimuth map with a clear target range.

[0015] Optionally, in one embodiment of the present application, when filtering the multi-channel data, the multi-channel data is filtered in sequence using an IIR filter with a passband within a preset range to filter out the respiratory low-frequency signal and the high-frequency clutter signal.

[0016] The heart rate detection method and device based on multi-channel linear frequency modulation continuous wave radar in the embodiments of the present application simultaneously estimate multiple possible target signals to eliminate the interference of breathing on the heartbeat signal, which has better robustness than the signal at a single location. To more accurately estimate the heart rate, starting from the heartbeat interval, the correlation coefficient between the heartbeat signal in the current time window and the time domain signal after a certain time interval is calculated. The time interval with the highest correlation coefficient is found as the time interval between the two heartbeat activities, and the current heart rate is converted accordingly. This ensures the real-time and accuracy of the heart rate detection.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 This is a flowchart of a heart rate detection method based on a multi-channel linear frequency modulation continuous wave radar according to an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the effect of a receiving angle on received echo signals of multiple antennas according to an embodiment of the present application;

[0021] Figure 3 Execution steps of a heart rate detection method based on a multi-channel linear frequency modulation continuous wave radar provided in an embodiment of the present application;

[0022] Figure 4 A range-azimuth map obtained by Fourier transforming the distance dimension and the antenna dimension according to an embodiment of the present application;

[0023] Figure 5 A range-azimuth map obtained after mean cancellation according to an embodiment of the present application;

[0024] Figure 6 An original signal obtained after phase extraction according to an embodiment of the present application;

[0025] Figure 7 A heartbeat signal obtained by filtering an original signal according to an embodiment of the present application;

[0026] Figure 8 A comparison of the heart rate estimation result obtained by the method within 1 hour and the real-time electrocardiogram and PPG signal provided in an embodiment of the present application;

[0027] Figure 9 2 is an example diagram of a heart rate detection device based on a multi-channel linear frequency modulation continuous wave radar according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] Figure 1 The present invention provides a flowchart of a heart rate detection method based on a multi-channel linear frequency modulation continuous wave radar according to an embodiment of the present application.

[0030] like Figure 1 As shown, the heart rate detection method based on multi-channel linear frequency modulation continuous wave radar includes the following steps:

[0031] In step S101, an echo signal returned after the linear frequency modulation signal hits the target position is obtained, and the echo signal is sequentially subjected to Fourier transform in the distance dimension and Fourier transform in the antenna dimension to generate a target range and azimuth map.

[0032] A specific method for generating a target range and azimuth map includes: sending a linear frequency modulation signal and receiving an echo signal generated at a target position through a receiving antenna; mixing the echo signal and the linear frequency modulation signal to obtain a matched filtered intermediate frequency signal; performing Fourier transform on the intermediate frequency signal in the distance dimension and the antenna dimension respectively to obtain target range information and target azimuth information, and generating a target range and azimuth map based on the target distance information and target azimuth information.

[0033] LFMCW (Linear Frequency Modulated Continuous Wave) radar sends a linear frequency modulated chirp signal with a starting frequency of f0 and a duration of T c , in T c The signal frequency increases linearly from f0 to f0+ during the time period, so the chirp signal can be described as:

[0034]

[0035] The chirp signal is received. Since the target echo at distance R is received with a delay of 2R / c, the chirp signal can be expressed as:

[0036]

[0037] Where I represents the interference caused by other electromagnetic signals, and N represents the noise caused by echoes from other objects besides the body surface echo. The echo signal is mixed with the original signal, and the high-frequency signal is filtered out to obtain the intermediate frequency signal after matched filtering:

[0038]

[0039] Each chirp lasts about 1ms. In such a short time, assuming that the distance R of the target does not change, M samples are taken for each chirp, and each sampling interval is T s , then the first half of the intermediate frequency signal remains unchanged, and the second half is transformed by Fourier transform to obtain:

[0040]

[0041] Where T c =T s , Therefore, the target distance information can be obtained by using Fourier transform in the distance dimension, and then the target distance azimuth map can be obtained by performing Fourier transform in the antenna dimension.

[0042] Since the multi-channel LFMCW radar has M transmitting antennas and N receiving antennas, the multi-channel LFMCW radar has M×N equivalent channels. Figure 2 As shown in the figure, due to the different antenna positions, the time intervals for the echo to be received by different antennas after hitting the target and being reflected back are slightly different. The echo data of different receiving channels can be expressed as:

[0043] y(t)=(t)exp(jw**inθ*n)

[0044] Where n represents the nth receiving channel, and I(t) represents the echo signal of the first receiving channel.

[0045] Therefore, the Fourier transform of the signal in the antenna dimension is performed as follows:

[0046] y(t)=(t)exp(jw**inθ*n)=I()(jΩm)

[0047] The azimuth information of the received signal can be obtained. m represents the frequency dimension after Fourier transform. After performing two Fourier transforms, the target range and azimuth map is obtained.

[0048] Optionally, in one embodiment of the present application, after generating the target distance and azimuth map, it also includes: accumulating the target distance and azimuth maps within the first preset time, calculating the mean of each point in the target distance and azimuth map, and subtracting the accumulated mean of the first preset time from the target distance and azimuth map obtained every second preset time to obtain a target distance and azimuth map with a clear target range.

[0049] Specifically, the range-azimuth map of 1 second is accumulated, and the mean value of each point is calculated. In order to filter out the clutter information of stationary objects, the range-azimuth map obtained every 0.01 second is subtracted from the mean signal accumulated in the previous second to obtain a range-azimuth map with a clear target range.

[0050] In step S102, the phase information of the point with the highest amplitude in the target range and azimuth map and its adjacent range points and adjacent azimuth points at the current moment is extracted to generate multi-channel data, and the multi-channel data is filtered to obtain a multi-channel heartbeat signal.

[0051] The heartbeat activity range is determined by selecting the distance before and after the location with the strongest signal and the two adjacent directions. The data for all points within this range in the distance-and-azimuth map are divided into multiple channels. The arctan(I(t) / Q(t)) function is used to extract the phase information of each channel at the current moment, where I(t) represents the real part of the complex data at the current moment, and Q(t) represents the imaginary part of the complex data.

[0052] In one embodiment of the present application, multi-channel data is filtered to obtain a multi-channel heartbeat signal, including: filtering the multi-channel data in sequence using an IIR filter with a passband within a preset range to filter out low-frequency respiratory signals and high-frequency clutter signals to obtain a multi-channel heartbeat signal.

[0053] The data of multiple channels are sequentially filtered using an IIR filter with a passband of 0.8 Hz to 2.0 Hz to remove the low-frequency respiratory signal and high-frequency clutter signal to obtain the heartbeat signal.

[0054] In step S103, the heartbeat data of the heartbeat signal of each channel in the fixed time domain window at the current moment is obtained, and the correlation coefficient between the heartbeat data in the fixed time domain window at the current moment and the heartbeat data in the fixed time domain window after the preset time interval is calculated, and the time interval corresponding to the maximum value of the correlation coefficient is used for conversion to obtain the heart rate result at the current moment.

[0055] Specifically, a 2s time domain window is used to obtain the correlation coefficient between the current heartbeat activity and the time series of heartbeat activity 0.4s to 1.2s later. The heartbeat time domain signal within the 2s window of the current channel is recorded. The window is slid to the next position every 0.01s from 0.4s to 1.2s to calculate the correlation coefficient between the time series in the window and the current window data, and the time interval with the largest correlation coefficient is recorded.

[0056] Repeat the above process for each channel, compare the maximum correlation coefficients in each channel, and take the time interval Δt with the highest correlation coefficient as the time interval between two heartbeat activities at the current moment, which is converted into beats per minute (bpm=60 / Δt).

[0057] Optionally, in one embodiment of the present application, before using the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment, it also includes: judging whether the maximum value of the correlation coefficient is greater than the correlation coefficient threshold; if it is greater than or equal to the correlation coefficient threshold, obtaining the time interval corresponding to the maximum value of the correlation coefficient; if it is less than the correlation coefficient threshold, the heart rate result at the current moment is the heart rate result at the previous moment.

[0058] When the correlation coefficient is less than the threshold, it means that the signal is greatly affected by noise, and the heart rate at the previous moment is taken as the heart rate estimate at the current moment.

[0059] Because the position of the chest cavity is affected by respiratory activity to varying degrees, this method estimates multiple possible target signals simultaneously. Therefore, compared to signals at a single location, it has better robustness and can select the location with the least interference to calculate the heart rate. At the same time, this method directly estimates the time interval between two heartbeats, using a time domain method to search for each possible time interval between 0.4s and 1.2s, and then selects the time interval with the largest correlation coefficient between the two window time domain signals to convert it into the current heart rate. When the correlation coefficient is low, it is judged that the signal is distorted, and the estimated value at the previous moment is used to minimize interference from strong noise. As shown in simulation experiments, the degree of matching with the real-time electrocardiogram can reach over 90%.

[0060] The heart rate detection method based on multi-channel linear frequency modulation continuous wave radar of the present application is described below through a specific embodiment.

[0061] Figure 3 Table 1 shows the execution process steps of the heart rate detection method based on a multi-channel linear frequency modulation continuous wave radar. The radar parameters are shown in Table 1.

[0062] Table 1 Radar parameters

[0063] Physical meaning Numerical Physical meaning Numerical Radar bandwidth 4G Hz Frame rate 100 frames / s Starting frequency 77G Hz Sampling rate 51200 / s

[0064] During the measurement, the tester lies still with the radar 3m above his head, and wears PPG and ECG detection equipment as a reference for the heart rate estimation results.

[0065] Step 1: Set the LFMCW radar parameters according to Table 1, send and receive 100 frames of radar echo data per second, and perform Fourier transform in the range dimension and antenna dimension to obtain the target range and azimuth map. Figure 4 The target portion of the range-azimuth diagram is shown. The target is approximately at (5,11), but a large amount of clutter affects the judgment.

[0066] Step 2: Accumulate the range and azimuth maps of the previous 100 frames in total, calculate the mean of each point, and subtract the mean of the previous 100 frames from the range and azimuth map at the current moment to reduce the interference of stationary clutter, such as Figure 5 As shown in the figure, a clearer distance and azimuth map is obtained. The point with the strongest signal (5, 11) is selected as the target chest position, and nine points (5, 10), (5, 12), (4, 10), (4, 11), (4, 12), (6, 10), (6, 11), and (6, 12) are circled around the target.

[0067] Step 3: Use arctan(I(t) / Q(t)) function to extract phase information from 9 points to form 9 channels, where I(t) represents the real part of the complex data at the current moment, and Q(t) represents the imaginary part of the complex data. Figure 6 Shown is the phase information corresponding to the strongest signal point (5, 11).

[0068] Step 4: Use a 4th order elliptic filter with a passband of 0.6 to 2.0 Hz to filter out breathing and noise interference on each of the 9 channels. The stopband attenuation of the elliptic filter is 30 dB, and the passband ripple is 1 dB. Figure 7 Shown is the heartbeat signal obtained after bandpass filtering at the strongest signal point (5, 11).

[0069] Step 5: Use a 2s window to record the time domain signal of the current channel, from 0.4s to 1.2s, and use the correlation coefficient every 0.01s Calculate the correlation between the time domain signal in the current window and the window after a period of time. Record the maximum correlation coefficient and the corresponding time interval.

[0070] Step 6: Perform step 5 on all nine channels in turn, selecting the largest correlation coefficient and corresponding time interval in each channel. If the largest correlation coefficient is less than 0.7, it is determined that the signal noise in this segment is too large and causes distortion, and the heart rate in the previous 0.1 seconds is used as the current heart rate.

[0071] Step 7: When the correlation coefficient is greater than or equal to 0.7, the two signals are determined to be matched successfully. The time interval Δt is converted into beats per minute (bpm=60 / Δt) as the current heart rate estimate.

[0072] Step 8: Repeat steps 1 to 7 every 0.1 seconds to obtain a real-time estimate of the heart rate.

[0073] like Figure 8As shown in the figure, during a 30-minute test period, of the 18,000 heart rate estimates obtained by this method, 16,450 estimates were within 1 beat per minute compared to the ECG estimate, with an accuracy rate of 91.39%. Compared to the PPG estimate, 15,981 estimates were within 1 beat per minute, with an accuracy rate of 88.78%.

[0074] According to the heart rate detection method based on multi-channel linear frequency modulation continuous wave radar proposed in the embodiment of the present application, in order to eliminate the interference of breathing on the heartbeat signal, multiple possible target signals are estimated at the same time, which has better robustness than the signal at a single position. Starting from the heartbeat interval, the correlation coefficient of the heartbeat signal in the current time window and the time domain signal after a certain time interval is calculated, and the time interval with the highest correlation coefficient is found as the time interval between the two heartbeat activities. Based on this, it is converted into the current heart rate, which can more accurately estimate the heart rate. In this way, the real-time and accuracy of heart rate detection are guaranteed at the same time.

[0075] Figure 9 2 is an example diagram of a heart rate detection device based on a multi-channel linear frequency modulation continuous wave radar according to an embodiment of the present application.

[0076] like Figure 9 As shown, the heart rate detection device 10 based on the multi-channel linear frequency modulation continuous wave radar includes: a first generation module 100, a second generation module 200 and a detection module 300.

[0077] Among them, the first generation module 100 is used to obtain the echo signal returned after the linear frequency modulation signal hits the target position, and performs Fourier transform in the distance dimension and Fourier transform in the antenna dimension on the echo signal in sequence to generate a target range and azimuth map. The second generation module 200 is used to extract the phase information of the point with the highest amplitude and its adjacent distance points and adjacent azimuth points in the target range and azimuth map at the current moment, generate multi-channel data, and filter the multi-channel data to obtain a multi-channel heartbeat signal. The detection module 300 is used to obtain the heartbeat data of the heartbeat signal of each channel in a fixed time domain window at the current moment, and calculate the correlation coefficient between the heartbeat data in the fixed time domain window at the current moment and the heartbeat data in the fixed time domain window after a preset time interval, and use the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment.

[0078] Optionally, in one embodiment of the present application, the first generation module 100 includes: a sending unit for sending a linear frequency modulation signal and receiving an echo signal generated at the target position through a receiving antenna; a mixing unit for mixing the echo signal and the linear frequency modulation signal to obtain an intermediate frequency signal after matched filtering; a transformation unit for performing Fourier transform on the intermediate frequency signal in the distance dimension and the antenna dimension respectively to obtain target distance information and target azimuth information, and generating a target distance and azimuth map based on the target distance information and the target azimuth information.

[0079] Optionally, in one embodiment of the present application, the heart rate detection device 10 based on the multi-channel linear frequency modulation continuous wave radar further includes: a correction module, which is used to accumulate the target distance and azimuth maps within the first preset time after generating the target distance and azimuth map, calculate the mean of each point in the target distance and azimuth map, and subtract the accumulated mean of the first preset time from the target distance and azimuth map obtained every second preset time to obtain a target distance and azimuth map with a clear target range.

[0080] Optionally, in one embodiment of the present application, when filtering multi-channel data, the multi-channel data is filtered sequentially using an IIR filter with a passband within a preset range to filter out low-frequency respiratory signals and high-frequency clutter signals.

[0081] It should be noted that the above explanation of the embodiment of the heart rate detection method based on multi-channel linear frequency modulation continuous wave radar is also applicable to the heart rate detection device based on multi-channel linear frequency modulation continuous wave radar in this embodiment, and will not be repeated here.

[0082] According to the heart rate detection device based on multi-channel linear frequency modulation continuous wave radar proposed in the embodiment of the present application, in order to eliminate the interference of breathing on the heartbeat signal, multiple possible target signals are estimated at the same time, which has better robustness than the signal at a single position. Starting from the heartbeat interval, the correlation coefficient of the heartbeat signal in the current time window and the time domain signal after a certain time interval is calculated, and the time interval with the highest correlation coefficient is found as the time interval between the two heartbeat activities. Based on this, it is converted into the current heart rate, which can more accurately estimate the heart rate. In this way, the real-time and accuracy of heart rate detection are guaranteed at the same time.

[0083] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0084] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0085] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

Claims

1. A heart rate detection method based on multi-channel linear frequency modulation continuous wave radar, characterized in that: The following steps are involved: Acquire the echo signal returned after the linear frequency modulation signal hits the target position, perform Fourier transform of the distance dimension 5 degrees and the antenna dimension on the echo signal in sequence, and generate a target range and azimuth map; Extracting the phase information of the point with the highest amplitude in the target range and azimuth map and its adjacent range points and adjacent azimuth points at the current moment, generating multi-channel data, and filtering the multi-channel data to obtain a multi-channel heartbeat signal; Obtain the heartbeat data of the heartbeat signal of each channel in a fixed time domain window at the current moment, and calculate the correlation coefficient between the heartbeat data in the fixed time domain window at the current moment and the heartbeat data in the fixed time domain window after a preset time interval, and use the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment.

2. The method according to claim 1, characterized in that Acquire the echo signal returned after the linear frequency modulation signal hits the target position, perform Fourier transform of the distance dimension and the antenna dimension on the echo signal in sequence, and generate a target range and azimuth map, including: sending a linear frequency modulation signal and receiving an echo signal generated at the target position through a receiving antenna; 5. Mixing the echo signal and the linear frequency modulation signal to obtain a matched filtered intermediate frequency signal; Fourier transform is performed on the intermediate frequency signal in the distance dimension and the antenna dimension respectively to obtain target distance information and target azimuth information, and a target distance and azimuth map is generated according to the target distance information and the target azimuth information.

3. The method according to claim 1 or 2, characterized in that After generating the target range and azimuth map, the method further includes: 0 Accumulate the target range and azimuth map within the first preset time, calculate the mean of each point in the target range and azimuth map, and subtract the mean accumulated during the first preset time from the target range and azimuth map obtained every second preset time to obtain a target range and azimuth map with a clear target range.

4. The method according to claim 1, wherein Filtering the multi-channel data to obtain a multi-channel heartbeat signal includes:

5. The multi-channel data are filtered in sequence using an IIR filter with a passband within a preset range to filter out the respiratory low-frequency signal and the high-frequency clutter signal, thereby obtaining the multi-channel heartbeat signal.

5. The method according to claim 1, wherein Before converting the time interval corresponding to the maximum correlation coefficient to obtain the heart rate result at the current moment, the following steps are also included: Determine whether the maximum value of the correlation coefficient is greater than the correlation coefficient threshold. If it is greater than or equal to the correlation coefficient threshold, obtain the time interval corresponding to the maximum value of the correlation coefficient. If it is less than the correlation coefficient threshold, the heart rate result at the current moment is the heart rate result at the previous moment.

6. The method according to claim 1, characterized in that The conversion formula for converting the time interval to obtain the heart rate result at the current moment is: bpm=60 / Δt Where bpm is the heart rate at the current moment, and Δt is the time interval corresponding to the maximum value of the correlation coefficient.

7. A heart rate detection device based on multi-channel linear frequency modulation continuous wave radar, characterized in that: include: A first generating module is configured to obtain an echo signal returned after the linear frequency modulation signal hits the target position, and sequentially perform a Fourier transform of the distance dimension and the antenna dimension on the echo signal to generate a target range and azimuth map; The second generation module is used to extract the phase information of the point with the highest amplitude in the target range and azimuth map and its adjacent distance points and adjacent azimuth points at the current moment, generate multi-channel data, and filter the multi-channel data to obtain a multi-channel heartbeat signal; The detection module is used to obtain the heartbeat data of the heartbeat signal of each channel in a fixed time domain window at the current moment, and calculate the correlation coefficient between the heartbeat data in the fixed time domain window at the current moment and the heartbeat data in the fixed time domain window after a preset time interval, and use the time interval corresponding to the maximum value of the correlation coefficient to convert to obtain the heart rate result at the current moment.

8. The device according to claim 7, characterized in that The first generating module includes: a transmitting unit, configured to transmit a linear frequency modulation signal and receive an echo signal generated at the target position through a receiving antenna; A frequency mixing unit, configured to mix the echo signal and the linear frequency modulation signal to obtain a matched filtered intermediate frequency signal; The transformation unit is used to perform Fourier transform on the intermediate frequency signal in the distance dimension and the antenna dimension respectively to obtain target distance information and target azimuth information, and generate a target distance and azimuth map according to the target distance information and the target azimuth information.

9. The device according to claim 7 or 8, characterized in that Also includes: The correction module is used to accumulate the target distance and azimuth map within a first preset time after generating the target distance and azimuth map, calculate the mean of each point in the target distance and azimuth map, and subtract the mean accumulated during the first preset time from the target distance and azimuth map obtained every second preset time to obtain a target distance and azimuth map with a clear target range.

10. The device according to claim 7, characterized in that When filtering the multi-channel data, the multi-channel data is filtered in sequence using an IIR filter with a passband within a preset range to filter out the respiratory low-frequency signal and the high-frequency clutter signal.

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