A human health data measurement method and system based on frequency-modulated continuous wave radar

Abstract

The application discloses a kind of based on frequency modulation continuous wave radar human health data measurement method and system, the method includes the following steps: S1: the transmitting end array and receiving end array of FMCW radar are constructed, and configuration transmitting signal, echo signal is mixed with transmitting signal, and the intermediate frequency signal is obtained;S2: the intermediate frequency signal is sampled and rearranged, and the signal data after direct current bias filtering is obtained Data matrix;S3: according to signal data, determine the position of human target;S4: according to the position of human target, carry out vital sign phase signal extraction;S5: according to phase signal, carry out wavelet decomposition and reconstruction, and heart rate estimation.The application can effectively improve the accuracy of heart rate estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, and particularly relates to a human health data measurement method and system based on a frequency modulated continuous wave radar. BACKGROUND

[0002] In recent years, with the serious population aging, the demand for real-time monitoring of respiratory rate and heart rate is increasingly strong. The FMCW (Frequency Modulated Continuous Wave) radar has a distance resolution, and the physiological signal detection is not easily disturbed by the reflection signals of other objects or human bodies in the environment, and has the potential to simultaneously monitor multiple physiological signals. The FMCW radar can have the advantages of frequency modulated continuous wave and millimeter wave integration technology, which further reduces the size of the FMCW radar and reduces the cost of the medical health monitoring system based on the radar.

[0003] Because the respiratory harmonic wave and the heartbeat spectrum overlap, and the heartbeat signal is weaker than the respiratory signal and the random jitter of the human body, how to effectively filter out the respiratory harmonic wave and weaken the influence of the random jitter of the human body, and then enhance the heartbeat signal becomes the key of heart rate detection and estimation. SUMMARY

[0004] The purpose of the present application is to provide a human health data measurement method and system based on a frequency modulated continuous wave radar, to solve the technical problem of how to effectively filter out the respiratory harmonic wave and weaken the influence of the random jitter of the human body, and then enhance the heartbeat signal for heart rate detection and estimation.

[0005] The present application is implemented by adopting the following technical scheme: a human health data measurement method based on a frequency modulated continuous wave radar, comprising the following steps:

[0006] S1: constructing a transmitting end array and a receiving end array of the FMCW radar, and configuring a transmitting signal, mixing the echo signal with the transmitting signal to obtain an intermediate frequency signal;

[0007] S2: sampling and rearranging the intermediate frequency signal to obtain a data matrix and signal data after filtering out the direct current bias;

[0008] S3: determining the position of the human target according to the signal data;

[0009] S4: extracting vital sign phase signals according to the position of the human target;

[0010] S5: performing wavelet decomposition and reconstruction and heart rate estimation according to the phase signals.

[0011] Further, step S1 comprises the following sub-steps:

[0012] S11: configure the transmitting antenna, receiving antenna and transmitting signal of the FMCW radar, the transmitting signal is:

[0013]

[0014] wherein, A T is the amplitude, f c is the start frequency of sweep, B is the bandwidth of sweep, T c is the sweep period, is the phase noise;

[0015] S12: obtain the echo signal in the detection range according to the transmitting signal:

[0016]

[0017] wherein, r i is the distance between the reflecting surface in the ith distance unit and the radar, c is the speed of light, a i is the reflection coefficient of the ith target;

[0018] S13: obtain the intermediate frequency signal after mixing and low-pass filtering the transmitting signal and the echo signal:

[0019]

[0020] Further, step S2 includes the following sub-steps:

[0021] S21: after ADC sampling the intermediate frequency signal, rearrange it into a three-dimensional data array: dimension N Rx , N r , N d respectively represent the number of receiving antennas, the number of ADC sampling points and the number of Chirp;

[0022] S22: remove the direct current bias according to the three-dimensional data array by using the mean cancellation method:

[0023]

[0024] wherein, X l,n,m is the signal data with coordinates (l, n, m) in the three-dimensional data array.

[0025] Further, step S3 includes the following sub-steps:

[0026] S31: do Fourier transform on the signal data in the fast time dimension:

[0027]

[0028] wherein, S IF,1D (k) is the result of the kth transmitting signal after FFT.

[0029] S32: Static clutter filtering is performed by mean cancellation method, and peak search is performed to determine the distance of the human target in each transmitting signal:

[0030] dis x = argmax |FFT[S' IF,1D (k)]|;

[0031] wherein, wherein dis x is the distance estimation result of the xth transmitting signal, S' IF,1D (k) is the signal data after static clutter filtering, FFT[] is fast Fourier transform, and | | is modulus;

[0032] S33: Incoherent accumulation is performed between different transmitting signals to determine the distance where the human target is located:

[0033]

[0034] wherein, dis represents the final distance estimation result of the human target.

[0035] Further, step S4 includes the following sub-steps:

[0036] S41: According to the distance where the human target is located, the vital sign signal is extracted from the signal data of the distance gate:

[0037]

[0038] wherein, is the preliminary extracted phase signal sequence, S I (n) and S Q (n) are the imaginary part and the real part of the signal data of the distance gate where the human target is located, respectively;

[0039] S42: Phase unwrapping is performed on the extracted data to obtain the vital sign phase signal containing breathing and heartbeat, and first-order difference, incoherent accumulation and direct current bias removal processing are performed;

[0040] S43: Hampel filtering is used to filter the phase outliers caused by small-range random jitter under human rest:

[0041]

[0042] wherein, is the sample median in the Hampel filter window, and the vital sign phase signal is finally obtained.

[0043] Further, step S42 includes the following sub-steps:

[0044] S421: phase unwrapping according to the signal in step S41, to obtain a vital sign phase signal containing respiration and heartbeat:

[0045]

[0046] S422: first-order difference to the vital sign phase signal:

[0047]

[0048] S423: incoherent accumulation to the phase signal under adjacent distance windows:

[0049]

[0050] S424: direct current bias removal:

[0051]

[0052] wherein, N frame is the frame number of the transmitted signal.

[0053] Further, step S5 includes the following sub-steps:

[0054] S51: decomposing according to the vital sign phase signal by using the Mallet algorithm;

[0055]

[0056]

[0057] wherein, is the phase signal, j is the number of layers of phase signal decomposition, h(n) and g(n) are low-pass and high-pass filters respectively;

[0058] S52: reconstructing according to the decomposition result to obtain a heartbeat signal:

[0059]

[0060] S53: frequency estimation by using FFT spectrum peak search according to the heartbeat signal:

[0061]

[0062] wherein, HR est is the heart rate estimation result.

[0063] A human health data measurement system based on frequency-modulated continuous wave radar, to realize the human health data measurement method based on frequency-modulated continuous wave radar described above, comprising a construction module, a rearrangement module, a position determination module and a heart rate estimation module, wherein,

[0064] A construction module is configured to construct a transmitting end array and a receiving end array of the FMCW radar, and configure a transmitting signal, mix the echo signal with the transmitting signal, and obtain an intermediate frequency signal;

[0065] A rearrangement module is configured to sample and rearrange the intermediate frequency signal, and obtain a data matrix and signal data after removing a direct current bias;

[0066] A position determination module is configured to determine a position of a human target according to the signal data, and extract a vital sign phase signal according to the position of the human target;

[0067] A heart rate estimation module is configured to perform wavelet decomposition and reconstruction, and heart rate estimation according to the phase signal.

[0068] A computer program product comprises a computer program, which, when executed by a processor, implements the human health data measurement method based on the FMCW radar.

[0069] A computer readable storage medium stores a computer program, which, when executed by a processor, implements the human health data measurement method based on the FMCW radar.

[0070] The present application has the advantages that the accuracy of heart rate estimation is effectively improved, the respiratory harmonic wave in the vital sign signal is filtered by using the discrete wavelet transform, and the influence of random body shaking is reduced by Hampel filtering. BRIEF DESCRIPTION OF DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings shown.

[0072] Figure 1 The flowchart of the present application;

[0073] Figure 2 The main structure diagram of the FMCW radar acquisition device;

[0074] Figure 3 The signal data matrix diagram;

[0075] Figure 4 The static clutter filtering effect diagram;

[0076] Figure 5 The Hampel filtering effect diagram;

[0077] Figure 6 Wavelet decomposition and reconstruction schematic diagram;

[0078] Figure 7 FMCW radar human heart rate estimation result and reference value comparison diagram. DETAILED DESCRIPTION

[0079] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0080] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0081] Some embodiments of the present application will be described in detail below with reference to the drawings. The following embodiments and features in the embodiments can be combined with each other without conflict.

[0082] Embodiment 1

[0083] Referring to Figure 1 A human health data measurement method based on a frequency-modulated continuous wave radar, comprising the following steps:

[0084] S1, constructing a transmitting end array and a receiving end array of the FMCW radar, setting a transmitting FMCW signal, and obtaining an intermediate frequency signal after mixing the echo and the transmitting signal;

[0085] S2, obtaining a data matrix after sampling and rearranging the intermediate frequency signal in S1, and obtaining signal data after filtering out a direct current bias;

[0086] S3, determining the distance of a human target according to the signal data in S2;

[0087] S4, performing vital sign phase signal extraction according to the distance window obtained in S3;

[0088] S5, performing wavelet decomposition and reconstruction and heart rate estimation according to the phase signal in S4.

[0089] In this embodiment, step S1 comprises the following sub-steps:

[0090] S11, configuring the transmitting and receiving antennas of the FMCW radar and the transmitting signal, and the transmitting signal model is:

[0091]

[0092] called Chirp, where A T is the amplitude of Chirp, f c represents the start frequency of the sweep of Chirp, B represents the bandwidth of the sweep of Chirp, T c represents the period of the sweep of Chirp, is the phase noise introduced on the phase signal due to the non-linear characteristics of the local oscillator and frequency multiplier.

[0093] S12, according to the transmitted signal in S11, the echo signal in the detection range is obtained, assuming that the distance between the reflecting surface in the ith distance unit and the radar is r i , then the received signal is:

[0094]

[0095] is the superposition of multiple target reflection signals, where c is the speed of light, a i is the reflection coefficient of the ith target.

[0096] S13, according to the transmitted signal and the received signal in S11 and S12, after mixing and low-pass filtering, an intermediate frequency (IF) signal is obtained:

[0097]

[0098] In practice, a TI IWR1642 acquisition board is used for data acquisition, where the structure of the FMCW radar is as shown in Figure 2 , and the parameters of the transmitted signal are as shown in Table 1.

[0099] Table 1 Chirp signal parameters

[0100]

[0101] In this embodiment, step S2 includes the following sub-steps:

[0102] S21, according to the signal in S1 after ADC sampling, rearrange it into a 3-dimensional data matrix, with dimensions N Rx , N r , N d respectively represent the number of receiving antennas, the number of ADC sampling points, and the number of Chirps.

[0103] S22, according to the data matrix obtained in S21, use the mean cancellation method to remove the DC bias of the Chirp signal.

[0104]

[0105] wherein X l,n,m represents the signal data of the coordinates (l, n, m) in the data matrix.

[0106] In practice, N Rx , N r , N d are respectively 4, 256, 16384, and the schematic diagram of the data matrix is shown in Figure 3 .

[0107] In the embodiment, step S3 comprises the following sub-steps:

[0108] S31, Fourier transform is performed on the signal in step S2 in the fast time dimension:

[0109]

[0110] wherein S IF,1D (k) is the result of the kth Chirp after FFT.

[0111] S32, according to the signal data in step S31, static clutter filtering is performed by mean cancellation method;

[0112] S33, according to the static clutter filtering result in step S32, peak search is performed to determine the distance of the human target in each Chirp:

[0113] dis x = argmax |FFT[S' IF,1D (k)]|;

[0114] wherein dis x represents the distance estimation result of the xth Chirp, S' IF,1D (k) represents the signal data in S32, FFT[] is fast Fourier transform, and | | is modulus.

[0115] S34, according to the result in S33, non-coherent accumulation is performed between different Chirps to determine the distance where the human target is located:

[0116]

[0117] wherein dis represents the final distance estimation result of the human target. The static clutter filtering effect is shown in Figure 4 , the distance resolution is 4.29 cm, and the actual distance of the human target is the product of dis and the distance resolution.

[0118] In the embodiment, step S4 comprises the following sub-steps:

[0119] S41, according to the human target distance information obtained in S3, the vital sign signal is extracted from the signal data of the distance gate:

[0120]

[0121] wherein, is the preliminary extracted phase signal sequence, S I (n), and S Q (n) are the imaginary part and the real part of the signal data of the distance gate where the human target is located, respectively.

[0122] S42: phase unwrapping is performed on the extracted data to obtain the vital sign phase signal containing respiration and heartbeat, and first-order difference, incoherent accumulation and direct current bias removal processing are performed;

[0123] S43: using Hampel filter to filter out the phase outliers caused by small-range random jitter under human rest:

[0124]

[0125] wherein, is the median of the samples in the Hampel filter window, and the vital sign phase signal is finally obtained.

[0126] In this embodiment, step S42 includes the following sub-steps:

[0127] S421: phase unwrapping is performed according to the signal in step S41 to obtain the vital sign phase signal containing respiration and heartbeat:

[0128]

[0129] S422: first-order difference is performed on the vital sign phase signal:

[0130]

[0131] S423: incoherent accumulation is performed on the phase signals under adjacent distance windows:

[0132]

[0133] S424: direct current bias removal is performed:

[0134]

[0135] wherein, N frame is the frame number of the transmitted signal. In this embodiment, the frame period is 50 ms, that is, the slow time sampling rate is 20 Hz, the total frame number is 2048, the data acquisition time is 102.4 s, and the Hampel filtering effect is as shown in Figure 5 .

[0136] In the embodiment, the step S5 comprises the following sub-steps:

[0137] S51, decomposing the vital sign phase signal obtained in the step S4 by using the Mallet algorithm;

[0138]

[0139]

[0140] wherein, φ (n) represents the phase signal, j represents the number of layers of the phase signal decomposition, h (n) and g (n) represent low-pass and high-pass filters respectively.

[0141] S52, reconstructing according to the separation result of S51 to obtain a heartbeat signal.

[0142]

[0143] S53, performing frequency estimation on the reconstructed heartbeat signal in the step S52 by using FFT spectrum peak search:

[0144]

[0145] wherein, HR est is the heart rate estimation result. In the embodiment, the sampling frequency of the phase signal is 20 Hz, the schematic diagram of wavelet decomposition and reconstruction is shown in Figure 6 , and the observation window is 12.8 s, that is, the sampling point number of the heartbeat phase signal in single estimation is 256.

[0146] According to the Chirp signal parameters shown in Table 1, an experiment is performed. In the experiment, the size of the experimental environment room is 4 m*5 m, and other objects exist to interfere. The radar height is 90 cm, the human target distance is 1.5 m, the subject is in a resting sitting state, and there are actions such as speaking and yawning. The heart rate data recorded by the LEP three-lead electrocardiograph PC-80D is used as a reference value, and 90 s of heartbeat data is measured each time. The results of the method and the reference value are shown in Figure 7 . Through statistics, the average error of the estimation is 1.62 bpm, the average error rate is 1.66%, and the mean square error is 3.89 bpm 2 .

[0147] The application further provides a human health data measurement system based on a frequency-modulated continuous wave radar, which is used to implement the human health data measurement method based on the frequency-modulated continuous wave radar. The system comprises a construction module, a rearrangement module, a position determination module and a heart rate estimation module.

[0148] ​The construction module is configured to construct a transmitting end array and a receiving end array of the FMCW radar, and configure a transmitting signal, mix the echo signal with the transmitting signal, and obtain an intermediate frequency signal;

[0149] The rearrangement module is configured to sample and rearrange the intermediate frequency signal to obtain a data matrix and signal data after filtering of a direct current bias;

[0150] The position determination module is configured to determine a position of the human target according to the signal data, and extract a vital sign phase signal according to the position of the human target.

[0151] The heart rate estimation module is configured to perform wavelet decomposition and reconstruction, and heart rate estimation according to the phase signal.

[0152] The application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the human health data measurement method based on the FMCW radar.

[0153] The application further provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the human health data measurement method based on the FMCW radar.

[0154] Based on the above-mentioned embodiments, the application has at least the following technical effects: the application effectively improves the accuracy of heart rate estimation, filters the respiratory harmonic wave in the vital sign signal by using the discrete wavelet transform, and reduces the influence of random body shaking by using the Hampel filter.

[0155] For the foregoing embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the application is not limited by the action sequence described, because according to the application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification belong to preferred embodiments, and the actions involved are not necessarily necessary for the application.

[0156] In the above-mentioned embodiments, the basic principles and main features of the application and the advantages of the application are described. Those skilled in the art should know that the application is not limited by the above-mentioned embodiments, and the above-mentioned embodiments and the description in the specification only illustrate the principles of the application, and the changes and modifications made by those skilled in the art without departing from the spirit and scope of the application should be within the protection scope of the claims of the application.

Claims

1. A method for calculating human health data based on frequency-modulated continuous wave radar, characterized in that, Includes the following steps: S1: Construct the transmitter and receiver arrays of the FMCW radar, configure the transmit signal, and mix the echo signal with the transmit signal to obtain the intermediate frequency signal; S2: The intermediate frequency signal is sampled and rearranged to obtain the data matrix and the signal data after DC bias filtering; S3: Determine the location of the human target based on signal data; S4: Extract vital sign phase signals based on the location of the human target; step S4 includes the following sub-steps: S41: Extract vital sign signals from the signal data of the distance gate based on the distance to the human target: ； in, This is the initial extracted phase signal sequence. , These are the imaginary and real parts of the signal data for the distance between the human target and the gate, respectively. S42: Perform phase unwrapping on the extracted data to obtain vital sign phase signals containing respiration and heartbeat, and then perform first-order difference, incoherent accumulation, and DC bias removal processing; step S42 includes the following sub-steps: S421: Perform phase unwrapping based on the signal from step S41 to obtain the vital signs phase signal containing respiration and heartbeat: ； S422: Perform first-order difference on the phase signals of vital signs: ； S423: Perform incoherent accumulation of phase signals within adjacent distance windows: ； S424: Perform DC bias removal: ； in, The number of frames in the transmitted signal; S43: Using Hampel filtering to remove phase anomalies caused by small-range random jitter in a resting human body: ， in, The median value of the samples within the Hampel filter window is used to obtain the vital signs phase signal. S5: Based on the phase signal, perform wavelet decomposition and reconstruction, and estimate the heart rate; step S5 includes the following sub-steps: S51: Decompose based on vital sign phase signals using the Mallet algorithm; ； ； in, It is a phase signal. This represents the number of layers in the phase signal decomposition. and These are low-pass and high-pass filters, respectively; S52: Reconstruct the heartbeat signal based on the decomposition results: ； S53: Frequency estimation is performed using FFT spectral peak search based on the heartbeat signal. ； in, This is the result of heart rate estimation.

2. The method for calculating human health data based on frequency-modulated continuous wave radar as described in claim 1, characterized in that, Step S1 includes the following sub-steps: S11: Configure the FMCW radar's transmitting antenna, receiving antenna, and transmitting signal. The transmitting signal is: ； in, For amplitude, This is the start frequency for the frequency sweep. For the sweep bandwidth, For the frequency sweep period, This is phase noise; S12: Obtain the echo signal within the detection range based on the transmitted signal: ； in, For the first The distance between the reflector and the radar within each range cell. At the speed of light, For the first The reflection coefficient of the target; S13: After mixing and low-pass filtering the transmitted and echo signals, the intermediate frequency signal is obtained. 。 3. The method for calculating human health data based on frequency-modulated continuous wave radar as described in claim 2, characterized in that, Step S2 includes the following sub-steps: S21: After sampling the intermediate frequency signal using an ADC, it is rearranged into a three-dimensional data array: dimension , , These represent the number of receiving antennas, the number of ADC sampling points, and the number of chirps, respectively. S22: Based on the three-dimensional data array, the DC bias is removed using the mean cancellation method. ； in, For the coordinates in the three-dimensional data array The signal data.

4. The method for calculating human health data based on frequency-modulated continuous wave radar as described in claim 3, characterized in that, Step S3 includes the following sub-steps: S31: Perform a Fourier transform on the signal data in the fast time dimension: ； in, For the first The result of FFT on the transmitted signal; S32: Static clutter is filtered out using the mean cancellation method, and peak search is performed to determine the distance to the human target within each transmitted signal. ； Among them, For the first The distance estimation results for each transmitted signal, The signal data after static clutter filtering is given, FFT[ ] is Fast Fourier Transform, and || is modulus taking; S33: Determine the distance to a human target by incoherently accumulating signals between different transmitted signals. ； in, This represents the final distance estimation result for the human target.

5. A human health data calculation system based on frequency-modulated continuous wave radar, used to implement the human health data calculation method based on frequency-modulated continuous wave radar as described in any one of claims 1 to 4, characterized in that, It includes a construction module, a rearrangement module, a location determination module, and a heart rate estimation module, among which, The module is used to build the transmitter and receiver arrays of the FMCW radar, configure the transmit signal, and mix the echo signal with the transmit signal to obtain the intermediate frequency signal. The rearrangement module is used to sample and rearrange the intermediate frequency signal to obtain a data matrix and signal data after DC bias filtering. The location determination module determines the location of the human target based on signal data, and extracts vital sign phase signals based on the location of the human target. The heart rate estimation module performs wavelet decomposition and reconstruction, as well as heart rate estimation, based on the phase signal.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the human health data calculation method based on frequency modulated continuous wave radar as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the human health data calculation method based on frequency modulated continuous wave radar as described in any one of claims 1 to 4.

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