A non-contact real-time physiological sign monitoring system based on millimeter-wave radar

Abstract

The present invention discloses a non-contact real-time physiological sign monitoring system based on millimeter wave radar, comprising a millimeter wave radar and a plurality of modules for processing radar signals, wherein the millimeter wave radar is configured to continuously transmit electromagnetic wave signals, and simultaneously receive echo signals and perform frequency mixing processing on the echo signals to obtain intermediate frequency signals, and the intermediate frequency signals are processed to obtain a radar four-dimensional data matrix; the human body's physiological signs are monitored by analyzing the human body's chest micro-motion information in the signals through each module; a constant false alarm rate detection algorithm is used to process the target echo and filter out invalid signals; an adaptive distance unit selection algorithm based on the short-term stability of a respiratory signal is used to accurately capture the radar echo reflecting the human body's physiological movement; a variational mode decomposition algorithm is used to process the mixed human body physiological sign signal obtained by the processing, and a gray wolf optimization algorithm is used to optimize the key parameters in the variational mode decomposition to obtain a more accurate estimation of physiological sign parameters.

Description

Technical Field

[0001] The present invention relates to the field of non-contact physiological sign monitoring, and in particular to a non-contact real-time physiological sign monitoring system based on millimeter wave radar. Background Art

[0002] With the continuous development of society and economy, people pay more and more attention to their personal health. This trend reflects people's pursuit of high-quality life and concern for health. And with the acceleration of modern life, the increase of work pressure and the increasingly serious problem of population aging, people of different ages have more urgent needs for scientific disease prevention and daily health monitoring. In the medical field, breathing, body temperature, pulse and blood pressure are called the four major physiological signs. They are the pillars for maintaining normal activities of the body and can directly reflect the state of human life activities.

[0003] Most common physiological sign monitoring methods are contact-based, such as electrocardiograms and blood oximeters. Such contact-based devices usually require professional medical staff to operate and are not friendly to some special patients, such as burn patients, patients with mental problems, and infants. Therefore, exploring non-contact physiological sign monitoring methods is of great significance in solving the problems that patients suffer from instrument constraints and achieving long-term monitoring and remote sensing.

[0004] Among them, millimeter-wave radar has significant advantages and potential in the field of non-contact physiological signs detection. Millimeter-wave radar refers to a radar that works in the millimeter-wave band (30-300GHz frequency band, with a wavelength of generally 1-10mm). Human tissues such as skin, muscles, and bones have a certain reflectivity to millimeter waves, which means that when millimeter waves come into contact with or pass through the human body, they will be reflected and generate reflected signals. The advantage of millimeter-wave radar is that it can penetrate materials and clothing, and is less affected by environmental factors such as smoke, temperature and humidity. This means that it can achieve all-weather, all-day physiological sign monitoring, and can effectively avoid the risk of privacy leakage, with a high degree of reliability and privacy.

[0005] Although millimeter-wave radar has many advantages in monitoring human physiological signs, its application still faces some challenges. The main challenges include: (1) The vibration caused by the heartbeat is significantly weaker than the vibration caused by breathing, which causes the modulation of the heartbeat signal to the radar to be submerged by the latter, making it much more difficult to estimate the heartbeat signal than the breathing signal. (2) The signal received by the millimeter-wave radar may be affected by many factors, such as changes in human posture, irregular changes in breathing and heartbeat frequencies, and environmental noise. Therefore, accurate and reliable signal processing of the received signal is a huge challenge. (3) When separating breathing and heartbeat signals, it must be able to adapt to changes in signal strength, eliminate noise interference, and have sufficient adaptability to handle signal changes under different human structures and physiological conditions. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a non-contact real-time physiological sign monitoring system based on millimeter wave radar to achieve non-contact human physiological sign monitoring.

[0007] In order to achieve the above object, the present invention provides a non-contact real-time physiological sign monitoring system based on millimeter wave radar, and the technical solution adopted is as follows:

[0008] A non-contact real-time physiological sign monitoring system based on millimeter wave radar, the system comprising:

[0009] Millimeter wave radar, the millimeter wave radar is configured to continuously transmit electromagnetic wave signals, simultaneously receive echo signals and perform mixing processing on the echo signals to obtain intermediate frequency signals, and process the intermediate frequency signals to obtain a radar four-dimensional data matrix Among them, M is the number of acquisition frames; N is the number of pulses per frame; L is the number of channels; K is the number of sampling points; Represents the complex number field.

[0010] A clutter suppression module is configured to perform static clutter suppression after performing fast Fourier transform processing on the radar four-dimensional data matrix;

[0011] A target identification module is configured to perform non-coherent pulse accumulation on the radar signal after static clutter suppression to obtain range spectrum data, and identify the target according to the range spectrum data;

[0012] a distance unit determination module configured to adaptively select an optimal distance unit based on the short-term stability of the respiratory signal when the target recognition module recognizes the target;

[0013] A phase signal extraction module, configured to extract the phase information of the optimal distance unit to obtain a phase signal;

[0014] A signal separation module is configured to process the phase signal using a variational mode decomposition algorithm, decompose the phase signal into a linear combination of multiple frequency bands, and determine a weight and a center frequency of each frequency band by optimizing a problem;

[0015] The physiological sign signal estimation module is configured to perform Fourier transform on the linear combination of the multiple frequency bands, search for spectrum peaks to perform frequency estimation, and screen components that meet the characteristics of physiological signs; when there are multiple components in the interval, the component with the smallest envelope entropy is used as the estimate of the target physiological sign signal.

[0016] Furthermore, the electromagnetic wave signal continuously emitted by the millimeter wave radar is expressed as:

[0017]

[0018] Among them, S T (t) is the electromagnetic wave signal continuously emitted by the millimeter wave radar, f c is the starting frequency, B is the FM bandwidth, T c is the frequency modulation period, cos is the cosine function, π is the circumference of a circle, A T is the amplitude, t is the time, is the initial phase.

[0019] Further, the intermediate frequency signal is expressed as:

[0020]

[0021] Among them, S IF (t) is the intermediate frequency signal, f c is the starting frequency, B is the FM bandwidth, T c is the frequency modulation period, e is a natural constant, π is the circumference of a circle, R(t) is the function of distance changing with time, A T is the amplitude of the signal, t is the time, and c is the speed of light.

[0022] Furthermore, the clutter suppression module is further configured to estimate the degree of static clutter interference in the environment by calculating the average value of the signal on each distance unit over a period of time, and the expression is:

[0023]

[0024] Where C[n] is the average clutter estimate in the time window, m is the range unit, and R[n,m] represents the radar echo signal strength received at time point n and range unit m.

[0025] Furthermore, the target recognition module is further configured to:

[0026] The range spectrum data is divided into several windows, each of which contains the data to be processed. A unit is selected from each window as the center, and N units are selected before and after. r / 2 units, excluding the intermediate protection unit, form the reference window W i ;

[0027] To W i Sort the values ​​in ascending order;

[0028] Calculate the threshold multiplier T based on the false alarm probability P FA and window size N r , select W i The kth os Large values ​​are used as local estimates of the background noise level;

[0029] A detection threshold is calculated. If the test unit value is greater than the detection threshold, it is marked as a target; if the test unit value is less than or equal to the detection threshold, it is marked as a non-target.

[0030] Furthermore, the distance unit determination module is further configured to:

[0031] Determine the distance unit m1 with the largest reflected power through the range spectrum accumulated by the non-coherent pulse;

[0032] On the left and right sides of the distance unit m1, two distance units are selected to form a candidate set R = {m1-2, m1-1, m1, m1+1, m1+2};

[0033] Extract phase information φ1 and φ2 of adjacent pulse signals x1 and x2 in the same frame respectively according to the distance unit in the candidate set;

[0034] Extract the respiratory signal components B1 and B2 in φ1 and φ2 through a [0.1Hz, 1Hz] bandpass filter;

[0035] Calculate the Pearson correlation coefficient of B1 and B2;

[0036] Select the distance unit R with the largest Pearson correlation coefficient of the respiratory signal * :

[0037]

[0038] Among them, argmax is the maximum value operation, PCC is the Pearson correlation coefficient, is the first pulse respiratory component, is the second pulse breathing component, r is an element in the distance unit candidate set, and R is the distance unit candidate set.

[0039] Furthermore, the distance unit determination module is further configured to calculate the Pearson correlation coefficient of B1 and B2 by the following formula:

[0040]

[0041] Among them, i is the index variable, X i and Y i are the values ​​of the two variables X and Y corresponding to the i-th observation, len is the number of observations, and represent the means of variables X and Y respectively.

[0042] Furthermore, the phase signal extraction module is further configured as follows:

[0043] The initial phase of the target is calculated by the following formula

[0044]

[0045] Phase unwrapping: Process each sample one by one to determine the adjacent samples with the best distance unit Whether the phase difference exceeds the threshold value π, if the phase difference is greater than π, subtract 2π, if the phase difference is not greater than π, add 2π;

[0046] Performing first-order difference processing on the unwrapped signal to extract the relative phase changes between adjacent samples;

[0047] Perform smoothing filtering on the signal after first-order difference processing.

[0048] Furthermore, the signal separation module is further configured to optimize the number of modes and penalty coefficients of the variational mode decomposition algorithm using a grey wolf optimization algorithm:

[0049] The number of modes and penalty factor are used as optimization variables, and the minimization of envelope entropy is used as the fitness function;

[0050] According to the position of the gray wolf, the corresponding VMD parameters are used to decompose the signal, and the minimum envelope entropy of each group of modes is calculated as the fitness value;

[0051] Update the position of the gray wolf according to the fitness value and update rules;

[0052] When the termination condition is reached, the current parameters are output as the optimal solution.

[0053] Furthermore, the signal separation module is further configured as follows:

[0054] The modal function is constructed through Hilbert transform to analyze the signal and calculate the one-sided spectrum, move the spectrum to the baseband, and calculate the square of the gradient of the demodulated signal L2 The signal bandwidth is estimated by using the norm, and a constrained variational problem is constructed to minimize the sum of the spectrum widths of all modal functions. The constrained variational problem is expressed as:

[0055]

[0056] in, is the optimization goal, that is, to find a solution that minimizes the sum of the spectrum widths of all modal functions; μ k is a set of modal functions; ω k is a set of frequency parameters used to describe the frequency characteristics of the modal function; k is the modal function number, K is the total number of modal functions; t is the time variable used to describe the change of the signal over time; δ(t) is the unit pulse function; j is the imaginary unit; μ k (t) is the specific form of the modal function; f(t) is the input signal, i.e., the signal to be decomposed;

[0057] The constrained variational problem is transformed into an unconstrained optimization problem by introducing the penalty factor α and the Lagrange multiplier λ:

[0058]

[0059] in, is the objective function for optimization, which includes the penalty factor and the Lagrange multiplier; λ(t) is the Lagrange multiplier, which is used to ensure that the sum of the input signal and the modal function is equal to the original signal; is the residual of the sum of the input signal and the modal function, indicating whether the combination of the input signal minus all modal functions is equal to zero; is the inner product of the Lagrange multiplier and the residual, which is used to ensure that the constraints are satisfied;

[0060] The unconstrained optimization problem is divided into two sub-optimization problems through iterative solution by the cross direction multiplier method to obtain the overall optimal solution. When the convergence condition is met or the maximum number of iterations is reached, the iteration is stopped and the signal decomposition is completed.

[0061] The present invention has at least the following beneficial effects:

[0062] The present invention utilizes millimeter wave radar technology to realize real-time monitoring of human physiological signs indoors. Through the combination of multiple modules, physiological signals such as chest micro-movements can be effectively extracted, and then physiological sign parameters can be accurately estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0064] Figure 1 A structural schematic diagram of a non-contact real-time physiological sign monitoring system based on millimeter wave radar according to an embodiment of the present invention is shown.

[0065] Figure 2 A working principle diagram of a non-contact real-time physiological sign monitoring system based on millimeter wave radar according to an embodiment of the present invention is shown.

[0066] Figure 3 A workflow diagram of a non-contact real-time physiological sign monitoring system based on millimeter-wave radar according to an embodiment of the present invention is shown.

[0067] Figure 4 A range spectrum diagram after static clutter suppression according to an embodiment of the present invention is shown.

[0068] Figure 5 A target detection effect diagram according to an embodiment of the present invention is shown.

[0069] Figure 6 A diagram showing the effect of smoothing filtering according to an embodiment of the present invention is shown.

[0070] Figure 7 A variational mode decomposition result diagram according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0071] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0072] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0073] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0074] The specific implementation of the present invention is further described in detail below in conjunction with the drawings and examples.

[0075] An embodiment of the present invention provides an application scenario, in which the target sits quietly at 0.75m away from the millimeter-wave radar device. The hardware platform used in the experiment is the commercial millimeter-wave radar sensor IWR6843 produced by Texas Instruments, which has a sweep bandwidth of 4GHZ and a corresponding distance resolution of 3.75cm. A single transmitting antenna is used in the waveform configuration to continuously send electromagnetic waves at a frame rate of 20Hz. According to the Nyquist sampling theorem, the sampling frequency must be higher than twice the highest heart rate in the physiological signal to meet the extraction requirements of physiological vital signs. Each frame signal contains two Chirps, and the frame period is 50 milliseconds. In each Chirp, the radar performs 256 ADC samplings, and the Chirp duration is 50 microseconds. During the experiment, the subjects wore a finger-clip pulse oximeter to record heartbeat data as a reference.

[0076] Based on the above application scenarios, an embodiment of the present invention provides a non-contact real-time physiological sign monitoring system based on millimeter wave radar, such as Figure 1 As shown, the system includes a millimeter wave radar 100, a clutter suppression module 200, a target recognition module 300, a distance unit determination module 400, a phase signal extraction module 500, a signal separation module 600 and a physiological sign signal estimation module 700 which are sequentially connected.

[0077] The following will be combined Figure 2 The working principle shown and Figure 3The illustrated workflow is used to introduce the millimeter wave radar 100 included in the system and the working principle of each module.

[0078] Millimeter wave radar continuously transmits electromagnetic wave signals and receives echo signals for mixing to obtain intermediate frequency signals. After processing, the radar four-dimensional data matrix is ​​obtained. Among them, M is the number of acquisition frames; N is the number of pulses per frame; L is the number of channels; K is the number of sampling points; Represents the complex number field.

[0079] In some embodiments, the millimeter wave radar continuously transmits electromagnetic wave signals which can be expressed as:

[0080]

[0081] Among them, f c is the starting frequency; B is the FM bandwidth; T c is the frequency modulation period, A T is the amplitude, t is the time, is the initial phase.

[0082] After the millimeter wave radar 100 receives the echo signal, it is processed with the local oscillator signal in the mixer to obtain an intermediate frequency signal. The essence of mixing is spectrum shifting, which uses nonlinear elements to multiply two signals to achieve frequency conversion and processing. Through the mixing process, the high-frequency signal can be converted into an intermediate frequency signal, which is convenient for subsequent signal processing and demodulation. The intermediate frequency signal can be expressed as:

[0083]

[0084] Among them, S IF (t) is the intermediate frequency signal, R(t) is the function of distance changing with time, A T is the amplitude of the signal, t is the time, and c is the speed of light.

[0085] As the main manifestation of physiological activities, breathing and heartbeat are quasi-periodic motions. In order to simplify the processing, the motion law of human breathing and heartbeat is approximated as a sinusoidal signal in this embodiment. Therefore, the chest displacement is expressed as follows:

[0086] R(t)=A b sin(2πf b t)+A h (2πf h t)+η t

[0087] Among them, A b , A h Represent the amplitude of breathing and heartbeat signals respectively; f b 、f h Represents breathing and heart rate, ηt represents random noise.

[0088] The clutter suppression module 200 is configured to perform fast Fourier transform processing on the radar original signal along the fast time dimension and perform static clutter suppression by using mean cancellation technology.

[0089] Specifically, the clutter suppression module 200 uses mean cancellation technology to suppress static clutter and eliminate the interference of DC components and static clutter in the signal. The degree of static clutter interference in the environment is estimated by calculating the average value of the signal on each distance unit over a period of time, and the expression is:

[0090]

[0091] The target identification module 300 is configured to perform non-coherent pulse accumulation on the radar signal after the static clutter suppression of the clutter suppression module 200. The range spectrum diagram after processing is as follows: Figure 4 Then the OS-CFAR detection algorithm is used to process the range spectrum data after pulse accumulation.

[0092] In some embodiments, after the radar signal is subjected to static clutter suppression, non-coherent pulse accumulation is performed using the following formula:

[0093]

[0094] Among them, R acc [n] represents the accumulated radar echo signal strength at time point n, k represents the distance unit, n represents the time point, and R[n,k] represents the radar echo signal strength received at time point n and distance unit k.

[0095] In some embodiments, the target identification module 300 is further configured to process the pulse accumulated range spectrum data by the following steps:

[0096] Step 3.1: Divide the range spectrum data into several windows, each containing the data to be processed. Select a unit as the center one by one, and r / 2 units, excluding the intermediate protection unit, form the reference window W i .

[0097] Step 3.2: For W i Sort the values ​​in ascending order.

[0098] Step 3.3: Calculate the threshold multiplier T based on the false alarm probability P FA and window size N r , select W i The kth os Large values ​​are used as local estimates of the background noise level.

[0099] Step 3.4: Calculate the detection threshold. If the test cell value is greater than the threshold, it is marked as a target; otherwise, it is marked as a non-target.

[0100] The target detection result finally achieved by the target recognition module 300 is as follows: Figure 5 shown.

[0101] The distance unit determination module 400 is configured to adaptively select the best distance unit based on the short-term stability of the respiratory signal, so that the phase information extracted can better reflect the physiological sign information of the human body.

[0102] In some embodiments, the distance unit determination module 400 is further configured to adaptively select the best distance unit by the following steps:

[0103] Step 4.1: perform the above echo signal preprocessing, and determine the distance unit m1 with the largest reflection power through the range spectrum after the non-coherent pulse accumulation;

[0104] Step 4.2: Select two distance units on the left and right sides of the distance unit m1 to form a candidate set R = {m1-2, m1-1, m1, m1+1, m1+2};

[0105] Step 4.3: Extract phase information φ1 and φ2 of adjacent pulse signals x1 and x2 in the same frame according to the distance unit in the candidate set;

[0106] Step 4.4: Extract the respiratory signal components B1 and B2 in φ1 and φ2 through a [0.1Hz, 1Hz] bandpass filter;

[0107] Step 4.5: Calculate the Pearson correlation coefficient of B1 and B2:

[0108]

[0109] Among them, i is the index variable, X i and Y i are the values ​​of the two variables X and Y corresponding to the i-th observation, len is the number of observations, and represent the means of variables X and Y respectively.

[0110] Step 4.6: Select the distance unit R with the largest Pearson correlation coefficient of the respiratory signal * :

[0111]

[0112] Among them, argmax is the maximum value operation, PCC is the Pearson correlation coefficient, is the first pulse respiratory component extracted above, is the second pulse breathing component extracted above, and r is an element in the distance unit candidate set R.

[0113] The phase signal extraction module 500 is configured to extract the phase information of the distance unit for analysis after determining the distance unit of the human target.

[0114] In some embodiments, the phase signal extraction module 500 is further configured to extract the phase information of the distance unit for analysis by the following steps:

[0115] Step 5.1: Phase extraction. After the range dimension FFT transformation, the inverse tangent method is applied to the real and imaginary parts of the result to calculate the initial phase of the target. That is:

[0116]

[0117] Step 5.2: Phase unwrapping. The inverse tangent algorithm can effectively recover the phase of the target signal in the linear range as long as the phase remains in the interval (-π / 2,π / 2). However, when the phase exceeds this range, a phase wrapping problem occurs. The phase wrapping problem is caused by the properties of the tangent function, which leads to a one-to-many mapping relationship. The tangent function is a surjective function only in the range of (-π / 2,π / 2). Beyond this range, the tangent function will map multiple different independent variables to the same value. Therefore, phase unwrapping processing is required, and the specific process is as follows:

[0118] Process each sample one by one and judge the adjacent samples Whether the phase difference exceeds the threshold value π, if it exceeds, it indicates that a jump occurs at this time and compensation is required; if the phase difference is greater than π, subtract 2π; otherwise add 2π.

[0119] Step 5.3: First-order difference processing. Perform first-order difference processing on the unwrapped signal. The relative phase change between adjacent sampling points can be extracted through phase difference without being affected by the absolute phase value. In this way, even if the signal drifts, the phase difference can still accurately reflect the relative change of the target movement, reduce the impact of noise interference, and make the system more stable and reliable.

[0120] Step 5.4: Smoothing filter. Further eliminate the interference of environmental noise and random mutation signals, and perform smoothing filter processing on the data. It is necessary to retain the overall characteristics and trends of the signal as much as possible while suppressing the noise. Therefore, the Savitzky-Golay (SG) filtering algorithm is used. Through the local polynomial fitting method, the window is moved in the signal, and the data in the window is fitted with a polynomial, so as to achieve signal smoothing. The effect diagram is as follows Figure 6shown.

[0121] The signal separation module 600 is configured to process the above phase signal using a variational mode decomposition algorithm, decompose the signal into a linear combination of multiple frequency bands, and determine the weight and center frequency of each frequency band through an optimization problem to minimize signal energy leakage.

[0122] In some embodiments, the signal separation module 600 is further configured to use Grey Wolf Optimization (GWO) to optimize two key parameters of the variational mode decomposition algorithm, namely, the number of modes and the penalty coefficient, by the following steps:

[0123] Step 6.1: Initialize the relevant parameters of the GWO algorithm, use the number of modes and penalty factor in the VMD algorithm as optimization variables, and minimize the envelope entropy as the fitness function;

[0124] Step 6.2: Decompose the signal using the corresponding VMD parameters according to the position of the gray wolf, and calculate the minimum envelope entropy of each group of modes as the fitness value;

[0125] Step 6.3: Update the position of the gray wolf according to the fitness value and GWO update rule, i.e., the optimal solution, suboptimal solution, and general solution;

[0126] Step 6.4: If the termination condition is met, output the current parameters as the optimal solution; otherwise continue the iterative search.

[0127] In some embodiments, the signal separation module 600 is further configured to:

[0128] First, construct the modal function through Hilbert transform to analyze the signal and calculate the one-sided spectrum, and move the spectrum to the baseband. Then calculate the square of the demodulated signal gradient L 2 The signal bandwidth is estimated by using the norm and a constrained variational problem is constructed to minimize the sum of the spectral widths of all mode functions.

[0129]

[0130] in, is the optimization goal, that is, to find a solution that minimizes the sum of the spectrum widths of all modal functions; μ k is a set of modal functions; ω k is a set of frequency parameters used to describe the frequency characteristics of the modal function; k is the modal function number, representing different modal functions; t is the time variable, used to describe the change of the signal over time; δ(t) is the unit pulse function; j is the imaginary unit; μ k (t) is the specific form of the modal function; f(t) is the input signal, that is, the signal to be decomposed.

[0131] This problem is transformed into an unconstrained optimization problem by introducing the penalty factor α and the Lagrange multiplier λ:

[0132]

[0133] in, is the objective function for optimization, which includes the penalty factor and the Lagrange multiplier; λ(t) is the Lagrange multiplier, which is used to ensure that the sum of the input signal and the modal function is equal to the original signal; is the residual of the sum of the input signal and the modal function, indicating whether the combination of the input signal minus all modal functions is equal to zero; is the inner product of the Lagrange multiplier and the residual, which is used to ensure that the constraints are satisfied.

[0134] The iterative solution is solved by the Alternating Direction Method of Multipliers (ADMM), which is divided into two sub-optimization problems to obtain the overall optimal solution. When the convergence condition is met or the maximum number of iterations is reached, the iteration is stopped and the signal decomposition is completed. The decomposition result is as follows: Figure 7 shown.

[0135] The physiological sign signal estimation module 700 is configured to perform Fourier transform on each decomposed signal component, search for spectrum peaks to perform frequency estimation, and screen components that meet the characteristics of human physiological signs; if there are multiple components in the interval, the component with the smallest envelope entropy is used as the estimate of the human physiological sign signal.

[0136] The non-contact real-time physiological sign monitoring system based on millimeter-wave radar is applied to the application scenario mentioned in this embodiment. The reference value of the subject's heart rate measured by a pulse oximeter is 68 to 72 times / minute. The heart rate estimation result obtained by the physiological sign monitoring system based on millimeter-wave radar proposed in this embodiment is 70.2 times / minute, which is in line with the reference value range.

[0137] The above implementation modes are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The patent protection scope of the present invention should be defined by the claims.

Claims

1. A non-contact real-time physiological sign monitoring system based on millimeter wave radar, characterized in that: The system comprises: Millimeter wave radar, the millimeter wave radar is configured to continuously transmit electromagnetic wave signals, simultaneously receive echo signals and perform mixing processing on the echo signals to obtain intermediate frequency signals, and process the intermediate frequency signals to obtain a radar four-dimensional data matrix Among them, M is the number of acquisition frames; N is the number of pulses per frame; L is the number of channels; K is the number of sampling points; represents a complex domain; A clutter suppression module is configured to perform static clutter suppression after performing fast Fourier transform processing on the radar four-dimensional data matrix; A target identification module is configured to perform non-coherent pulse accumulation on the radar signal after static clutter suppression to obtain range spectrum data, and identify the target according to the range spectrum data; a distance unit determination module configured to adaptively select an optimal distance unit based on the short-term stability of the respiratory signal when the target recognition module recognizes the target; A phase signal extraction module, configured to extract the phase information of the optimal distance unit to obtain a phase signal; A signal separation module is configured to process the phase signal using a variational mode decomposition algorithm, decompose the phase signal into a linear combination of multiple frequency bands, and determine a weight and a center frequency of each frequency band by optimizing a problem; A physiological sign signal estimation module is configured to perform Fourier transform on the linear combination of the multiple frequency bands, search for spectrum peaks to perform frequency estimation, and screen components that meet the physiological sign characteristics; when there are multiple components in the interval, the component with the smallest envelope entropy is used as the estimation of the target physiological sign signal; The signal separation module is further configured to optimize the number of modes and penalty coefficients of the variational mode decomposition algorithm using a grey wolf optimization algorithm: The number of modes and penalty factor are used as optimization variables, and the minimization of envelope entropy is used as the fitness function; According to the position of the gray wolf, the corresponding VMD parameters are used to decompose the signal, and the minimum envelope entropy of each group of modes is calculated as the fitness value; Update the position of the gray wolf according to the fitness value and update rules; When the termination condition is reached, the current parameters are output as the optimal solution; The signal separation module is further configured as follows: The modal function is constructed through Hilbert transform to analyze the signal and calculate the one-sided spectrum, move the spectrum to the baseband, and calculate the square of the gradient of the demodulated signal L 2 The signal bandwidth is estimated by using the norm, and a constrained variational problem is constructed to minimize the sum of the spectrum widths of all modal functions. The constrained variational problem is expressed as: in, is the optimization goal, that is, to find a solution that minimizes the sum of the spectrum widths of all modal functions; μ k is a set of modal functions; ω k is a set of frequency parameters used to describe the frequency characteristics of the modal function; k is the modal function number, K is the total number of modal functions; t is the time variable used to describe the change of the signal over time; δ(t) is the unit pulse function; j is the imaginary unit; μ k (t) is the specific form of the modal function; f(t) is the input signal, i.e., the signal to be decomposed; The constrained variational problem is transformed into an unconstrained optimization problem by introducing the penalty factor α and the Lagrange multiplier λ: Wherein, e is the objective function of optimization, which includes the penalty factor and the Lagrange multiplier; λ(t) is the Lagrange multiplier, which is used to ensure that the sum of the input signal and the modal function is equal to the original signal; is the residual of the sum of the input signal and the modal function, indicating whether the combination of the input signal minus all modal functions is equal to zero; λ(t), is the inner product of the Lagrange multiplier and the residual, which is used to ensure that the constraints are satisfied; The unconstrained optimization problem is divided into two sub-optimization problems through iterative solution by the cross direction multiplier method to obtain the overall optimal solution. When the convergence condition is met or the maximum number of iterations is reached, the iteration is stopped and the signal decomposition is completed.

2. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 1, characterized in that: The electromagnetic wave signal continuously emitted by the millimeter wave radar is expressed as: Among them, S T (t) is the electromagnetic wave signal continuously emitted by the millimeter wave radar, f c is the starting frequency, B is the FM bandwidth, T c is the frequency modulation period, cos is the cosine function, π is the circumference of a circle, A T is the amplitude, t is the time, is the initial phase.

3. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 1, characterized in that: The intermediate frequency signal is expressed as: Among them, S IF (t) is the intermediate frequency signal, f c is the starting frequency, B is the FM bandwidth, T c is the frequency modulation period, e is a natural constant, π is the circumference of a circle, R(t) is the function of distance changing with time, A T is the amplitude of the signal, t is the time, and c is the speed of light.

4. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 1, characterized in that: The clutter suppression module is further configured to estimate the degree of static clutter interference in the environment by calculating the average value of the signal on each range unit over a period of time, and the expression is: Where C[n] is the average clutter estimate in the time window, m is the range unit, and R[n,m] represents the radar echo signal strength received at time point n and range unit m.

5. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 1, characterized in that: The target recognition module is further configured to: The range spectrum data is divided into several windows, each of which contains the data to be processed. A unit is selected from each window as the center, and N units are selected before and after. r / 2 units, excluding the intermediate protection unit, form the reference window W i ; To W i Sort the values ​​in ascending order; Calculate the threshold multiplier T based on the false alarm probability P FA and window size N r , select W i The kth os Large values ​​are used as local estimates of the background noise level; Calculate a detection threshold, and if the test unit value is greater than the detection threshold, mark it as a target; If the test cell value is less than or equal to the detection threshold, it is marked as a non-target.

6. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 1, characterized in that: The distance unit determination module is further configured to: Determine the distance unit m1 with the largest reflected power through the range spectrum accumulated by the non-coherent pulse; On the left and right sides of the distance unit m1, two distance units are selected to form a candidate set R = {m1-2, m1-1, m1, m1+1, m1+2}; Extract phase information φ1 and φ2 of adjacent pulse signals x1 and x2 in the same frame respectively according to the distance unit in the candidate set; Extract the respiratory signal components B1 and B2 in φ1 and φ2 through a [0.1Hz, 1Hz] bandpass filter; Calculate the Pearson correlation coefficient of B1 and B2; Select the distance unit R with the largest Pearson correlation coefficient of the respiratory signal * : Among them, argmax is the maximum value operation, PCC is the Pearson correlation coefficient, is the first pulse respiratory component, is the second pulse breathing component, r is an element in the distance unit candidate set, and R is the distance unit candidate set.

7. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 6, characterized in that: The distance unit determination module is further configured to calculate the Pearson correlation coefficient of B1 and B2 by the following formula: Among them, i is the index variable, X i and Y i are the values ​​of the two variables X and Y corresponding to the i-th observation, len is the number of observations, and represent the means of variables X and Y respectively.

8. The non-contact real-time physiological sign monitoring system based on millimeter wave radar as claimed in claim 1, characterized in that: The phase signal extraction module is further configured as follows: The initial phase of the target is calculated by the following formula Phase unwrapping: Process each sample one by one to determine the adjacent samples with the best distance unit Whether the phase difference exceeds the threshold value π, if the phase difference is greater than π, subtract 2π, if the phase difference is not greater than π, add 2π; Performing first-order difference processing on the unwrapped signal to extract the relative phase changes between adjacent samples; Perform smoothing filtering on the signal after first-order difference processing.

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