Method and system for non-contact microwave monitoring of physiological parameters

Abstract

This invention provides a method and system for non-contact microwave monitoring of physiological parameters. The method involves acquiring the reflected signal, separating its real and imaginary data, and performing structured data partitioning based on the number of receiving antennas, frame count, and chirp signal pulse count. The partitioned data is then passed through a static clutter suppression filter to obtain a preprocessed signal. This preprocessed signal is then subjected to range-dimensional FFT processing to obtain a range-angle heatmap. Based on the range-angle heatmap, the preprocessed signal is phase-demodulated to obtain a phase signal. This phase signal is then filtered and denoised to obtain a target signal. Finally, the target signal is extracted to obtain the corresponding physiological parameters. Specifically, non-contact, precise monitoring of respiration, heart rate, and blood pressure is achieved using microwave radar technology.

Description

Technical Field

[0001] This invention belongs to the field of medical monitoring technology, and specifically relates to a method and system for non-contact microwave monitoring of physiological parameters. Background Technology

[0002] Traditionally, the monitoring of physiological parameters such as respiratory rate, heart rate and blood pressure has relied on contact sensors, such as electrocardiogram (ECG) electrodes for heart rate monitoring, breathing belts or spirometers for respiratory rate monitoring, and blood pressure cuffs for blood pressure measurement.

[0003] However, these methods have many limitations, such as requiring direct contact with the patient, which may cause discomfort, especially in long-term monitoring; in addition, contact sensors may interfere with the patient's natural behavior or sleep, affecting the accuracy of monitoring.

[0004] In recent years, non-contact monitoring technologies have gradually attracted attention, among which microwave radar technology has become a research hotspot due to its advantages such as being non-invasive, radiation-free, and protecting privacy. Microwave radar transmits microwave signals and receives reflected signals, utilizing the Doppler effect to detect frequency changes caused by movements of the chest and other body parts, thereby enabling the monitoring of physiological signals.

[0005] However, existing microwave monitoring technologies face challenges when simultaneously monitoring multiple physiological parameters, such as difficulties in signal separation, environmental noise interference, and the accuracy of parameter extraction. These challenges limit the accuracy and reliability of microwave monitoring technologies in clinical applications. Therefore, there is an urgent need for a new non-contact microwave monitoring method that can effectively solve these problems and achieve accurate, real-time monitoring of physiological parameters such as respiration, heart rate, and blood pressure. Summary of the Invention

[0006] Based on this, the present invention provides a method and system for non-contact microwave monitoring of physiological parameters, aiming to ensure the accuracy of detection when monitoring multiple physiological parameters in real time.

[0007] A first aspect of this invention provides a method for non-contact microwave monitoring of physiological parameters, applied in a scenario with a microwave radar device, the microwave radar device being used to transmit microwave signals and receive reflected signals, the method comprising:

[0008] The reflected signal is acquired, the real part and the imaginary part of the reflected signal are separated, and the structured data is divided according to the number of receiving antennas, the number of frames, and the number of chirp signal pulses.

[0009] The segmented data is passed through a static clutter suppression filter to obtain a preprocessed signal, and the preprocessed signal is then subjected to range-dimensional FFT processing to obtain a range-angle heatmap.

[0010] Based on the distance-angle heatmap, the preprocessed signal is phase demodulated to obtain a phase signal, and the phase signal is then filtered and denoised to obtain the target signal.

[0011] When determining blood pressure parameters, the target signal is feature extracted, the pulse wave transit time is calculated, and based on the correlation between the pulse wave transit time and blood pressure, a regression model is used to convert the pulse wave transit time into continuous estimates of systolic and diastolic blood pressure.

[0012] When determining respiratory and heart rate parameters, the target signal is adaptively decomposed into different modal components, and the respiratory and heart rate signals are extracted through corresponding bandpass filtering paths, and finally the respiratory waveform and heart rate waveform are output.

[0013] The steps preceding the acquisition of the reflected signal, separation of the real and imaginary parts of the reflected signal, and structured data partitioning based on the number of receiving antennas, frame number, and chirp signal pulse number include:

[0014] The raw radar signal was acquired, and a high-resolution point cloud was generated using range-azimuth joint FFT.

[0015] Clustering algorithms are used to locate the user's chest cavity or carotid artery region, and adaptive beamforming is used to form a beam at a preset angle in the user's chest cavity or carotid artery region. At the same time, nulls of a preset depth are inserted in the adjacent bed direction.

[0016] Within the chest cavity or carotid artery region, the user's body movement trajectory is predicted using Kalman filtering, and the beam direction is adjusted in real time.

[0017] Furthermore, in the step of performing phase demodulation on the preprocessed signal based on the distance-angle heatmap to obtain the phase signal, the arctangent phase value of the complex signal is calculated by phase demodulation. At the same time, the phase of the multi-cycle Chirp signal is superimposed using incoherent accumulation technology to improve the signal-to-noise ratio before phase unwinding processing is performed to eliminate the 2π jump phenomenon.

[0018] Furthermore, in the step of filtering and denoising the phase signal to obtain the target signal, high-frequency noise is eliminated by using a moving average filter;

[0019] The pulse band was extracted using a fourth-order Butterworth zero-phase-shift filter, with a cutoff frequency of 0.5Hz to 6Hz.

[0020] Residual interference is suppressed by wavelet threshold denoising to obtain the pulse wave signal within a preset time period, which is used to determine blood pressure parameters.

[0021] Furthermore, the relationship between the pulse wave conduction time and blood pressure is expressed as follows:

[0022] ;

[0023] Where SBP is systolic blood pressure, DBP is diastolic blood pressure, PTT is pulse wave transit time, and a, b, and A are individual-specific parameters obtained through regression fitting. This is the reference calibration value for systolic blood pressure. This is the reference calibration value for diastolic blood pressure. This is the reference calibration value for pulse wave conduction time.

[0024] Furthermore, in the step of performing phase demodulation on the preprocessed signal based on the distance-angle heatmap to obtain a phase signal, and then filtering and denoising the phase signal to obtain the target signal, phase demodulation is used to separate phase changes caused by breathing and heartbeat. Subsequently, sudden interference is eliminated by an impulse noise removal algorithm, and the signal frequency band is initially separated by a bandpass filter.

[0025] Furthermore, variational mode decomposition is introduced to adaptively decompose the target signal into different mode components.

[0026] Furthermore, the step of predicting the user's body movement trajectory using Kalman filtering within the chest cavity or carotid artery region and adjusting the beam direction in real time includes:

[0027] By jointly optimizing spatiotemporal adaptive processing and polarization filtering, the interference suppression capability is improved. Specifically, the time dimension of spatiotemporal adaptive processing is extended to the respiratory cycle synchronization frame, so that the spatiotemporal adaptive processing matches the periodic characteristics of physiological signals when eliminating multipath reflections, reducing the attenuation of effective signals. At the same time, a polarization feature library of metal devices is introduced into polarization filtering. The cross-polarization reflection characteristics of the metal devices in the adjacent bed are pre-stored through machine learning. When unknown polarization interference is detected, similarity matching is performed based on the feature library to generate targeted polarization suppression parameters.

[0028] By transmitting detection signals in the idle frequency band of the radar, the distribution of objects within a preset range is scanned to establish a dynamic interference source map;

[0029] When a neighboring user moves, the system predicts the interference path of the interference source on the local bed's signal based on the changes in the interference source map, adjusts the null direction of the adaptive beamforming algorithm in advance, and automatically increases the null depth when the interference source gets close. The linkage space-time adaptive processing increases the suppression weight of multipath reflection in this direction.

[0030] A second aspect of this invention provides a system for non-contact microwave monitoring of physiological parameters, used to implement the method for non-contact microwave monitoring of physiological parameters described in the first aspect, the system comprising:

[0031] The acquisition module is used to acquire the reflected signal, separate the real part and imaginary part of the reflected signal, and perform structured data partitioning based on the number of receiving antennas, the number of frames, and the number of chirp signal pulses;

[0032] The first processing module is used to pass the segmented data through a static clutter suppression filter to obtain a preprocessed signal, and to perform range-dimensional FFT processing on the preprocessed signal to obtain a range-angle heatmap;

[0033] The second processing module is used to perform phase demodulation on the preprocessed signal according to the distance-angle heatmap to obtain a phase signal, and to filter and denoise the phase signal to obtain the target signal;

[0034] The first parameter determination module is used to extract features from the target signal, calculate the pulse wave transit time, and convert the pulse wave transit time into continuous estimates of systolic and diastolic blood pressure using a regression model based on the correlation between the pulse wave transit time and blood pressure.

[0035] The second parameter determination module is used to adaptively decompose the target signal into different modal components when determining respiratory parameters and heart rate parameters, extract the respiratory signal and heart rate signal respectively through the corresponding bandpass filter path, and finally output the respiratory waveform and heart rate waveform.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for non-contact microwave monitoring of physiological parameters provided in the first aspect.

[0037] A fourth aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the non-contact microwave monitoring of physiological parameters provided in the first aspect.

[0038] This invention provides a method and system for non-contact microwave monitoring of physiological parameters. The method involves acquiring the reflected signal, separating its real and imaginary data, and performing structured data partitioning based on the number of receiving antennas, frame count, and chirp signal pulse count. The partitioned data is then passed through a static clutter suppression filter to obtain a preprocessed signal. This preprocessed signal is then subjected to range-dimensional FFT processing to obtain a range-angle heatmap. Based on the range-angle heatmap, the preprocessed signal is phase-demodulated to obtain a phase signal. This phase signal is then filtered and denoised to obtain a target signal. Finally, the target signal is extracted to obtain the corresponding physiological parameters. Specifically, non-contact, precise monitoring of respiration, heart rate, and blood pressure is achieved using microwave radar technology. Attached Figure Description

[0039] Figure 1 The flowchart illustrates the implementation of a non-contact microwave monitoring method for physiological parameters provided in Embodiment 1 of the present invention.

[0040] Figure 2 This is a structural block diagram of a non-contact microwave monitoring system for physiological parameters provided in Embodiment 3 of the present invention;

[0041] Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0042] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0043] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0045] Example 1

[0046] According to an embodiment of the present invention, a method for non-contact microwave monitoring of physiological parameters is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0047] This first embodiment provides a non-contact microwave monitoring method for physiological parameters, which can be used in electronic devices, such as computers. Please refer to... Figure 1 , Figure 1 The flowchart of a non-contact microwave monitoring method for physiological parameters provided in Embodiment 1 of the present invention is shown, specifically including steps S01 to S05.

[0048] Step S01: Obtain the reflected signal, separate the real part data and the imaginary part data of the reflected signal, and perform structured data partitioning based on the number of receiving antennas, the number of frames, and the number of Chirp signal pulses.

[0049] Understandably, microwave radar transmits microwave signals and receives reflected signals. It detects frequency changes caused by subtle human movements (such as breathing and heartbeat) using the Doppler effect. Furthermore, it acquires the real and imaginary parts of the radar echo signal through the I and Q channels, respectively. The I channel refers to the in-phase (I) signal channel, and the Q channel refers to the quadrature (Q) signal channel, used to preserve the complete phase and amplitude information of the signal. Then, based on the number of receiving antennas, the number of frames, and the number of chirp signal pulses, structured data is partitioned, providing an ordered and easily processed data structure for subsequent signal processing. This structured partitioning facilitates subsequent signal analysis and processing, with different dimensions of data corresponding to different physical characteristics or processing stages.

[0050] Step S02: Pass the segmented data through a static clutter suppression filter to obtain a preprocessed signal, and perform range-dimensional FFT processing on the preprocessed signal to obtain a range-angle heatmap.

[0051] Specifically, after the signal is structurally segmented, interference signals generated by stationary objects in the environment can affect the extraction of human body micro-motion features. Therefore, a static clutter suppression filter is needed to eliminate these interferences, retaining only the signal features related to human body micro-motions. This step aims to improve signal quality, enabling subsequent processing to more accurately focus on human body micro-motion signals.

[0052] A preprocessed signal, also known as a chirp signal, is obtained through a static clutter suppression filter. A distance-dimensional FFT is then performed on each chirp signal to generate a distance-angle heatmap, which helps locate the target human body region. This heatmap visually displays the signal intensity distribution at different distances and angles, thus aiding in the localization of the target human body region. This step, from a signal processing perspective, transforms the signal into a form that facilitates the analysis of the target human body's location.

[0053] Step S03: Based on the distance-angle heatmap, the preprocessed signal is phase demodulated to obtain a phase signal, and the phase signal is filtered and denoised to obtain the target signal.

[0054] It should be noted that during the determination of blood pressure parameters, the arctangent phase value of the complex signal is calculated through phase demodulation. This is understandable, as changes in blood pressure affect the degree of arterial dilation, thus altering the amplitude of the phase change, i.e., the arctangent phase value, which is used in the filtering operation. Simultaneously, incoherent accumulation technology is employed to superimpose the phases of multi-cycle Chirp signals to improve the signal-to-noise ratio. Then, phase dewinding is performed to eliminate the 2π jump phenomenon, ultimately obtaining the phase signal. Specifically, let the baseband expression of a single Chirp signal be:

[0055] ;

[0056] in, T Chirp pulse width, μ For the frequency modulation slope, rect( t / T ) is a rectangular window function (1 when t∈[-T / 2,T / 2], 0 otherwise). t For time, j The imaginary unit is used for the first... k Echo signal of one cycle:

[0057] ;

[0058] in, T prf The pulse repetition period, A k For the first k The amplitude of each cycle, φ k For the first k The phase of each cycle, where τ is the delay of the reflected signal, can be understood as the phase change caused by blood pressure;

[0059] First, the echo signal is mixed with the local reference signal. Multiplying them together yields the difference frequency signal:

[0060] ;

[0061] The symbol indicates "proportional to", and the difference frequency signal is the frequency. A single-frequency signal, the phase of which includes φ k and fixed delay phase ;

[0062] Then, the discrete sequence is obtained by sampling the difference frequency signal. Perform a short-time Fourier transform (STFT) on the signal for each cycle to obtain the frequency domain amplitude. and phase Then, the squared frequency domain amplitudes of the M periods are accumulated:

[0063] ;

[0064] Finally, peak detection is performed on the accumulated signal to determine the frequency. Corresponding phase principal value Furthermore, the 2π jump is eliminated using a phase unwinding algorithm to obtain a continuous phase signal:

[0065] ;

[0066] Where n is an integer determined by the number of phase transitions, in this embodiment of the invention, the phase unwinding algorithm is a minimum discontinuity method based on path tracking. It should be noted that after enhancing the signal through incoherent accumulation, the continuous phase obtained by phase unwinding... It can be used to calculate the displacement or velocity of arterial wall movement, and then derive blood pressure parameters.

[0067] After extracting the continuous phase signal from the target location on the human body, a moving average filter is used to eliminate high-frequency noise; a fourth-order Butterworth zero-phase-shift filter is used to extract the pulse wave band, with a cutoff frequency of 0.5Hz to 6Hz; wavelet threshold denoising is used to suppress residual interference to obtain the pulse wave signal within a preset time period. This pulse wave signal is used to determine blood pressure parameters. It is understood that after obtaining an accurate phase signal, a continuous phase signal is extracted from the target location on the human body. Because high-frequency noise and residual interference still exist in the signal, a moving average filter is used sequentially to eliminate high-frequency noise, a fourth-order Butterworth zero-phase-shift filter is used to extract the pulse wave band, and wavelet threshold denoising is used to suppress residual interference. Through these filtering and denoising operations, a clean pulse wave signal is finally obtained.

[0068] During the process of obtaining respiratory and heart rate parameters, phase demodulation was also performed. The location of the human body was selected and locked using a distance-angle heatmap. Then, phase demodulation was performed on the signal in the selected area. This phase demodulation was used to separate the phase changes caused by breathing and heartbeat. In addition, denoising and filtering were also performed. That is, sudden interference was eliminated by using an impulse noise removal algorithm, and the signal frequency band was initially separated by using a bandpass filter.

[0069] Step S04: When determining blood pressure parameters, feature extraction is performed on the target signal, pulse wave transit time is calculated, and based on the correlation between pulse wave transit time and blood pressure, a regression model is used to convert the pulse wave transit time into continuous estimates of systolic and diastolic blood pressure.

[0070] In this embodiment of the invention, the relationship between pulse wave conduction time and blood pressure is expressed as follows: ;

[0071] Where SBP is systolic blood pressure, DBP is diastolic blood pressure, PTT is pulse wave transit time, and a, b, and A are individual-specific parameters obtained through regression fitting. This is the reference calibration value for systolic blood pressure. This is the reference calibration value for diastolic blood pressure. This is the reference calibration value for pulse wave conduction time.

[0072] Step S05: When determining the respiratory parameters and heart rate parameters, the target signal is adaptively decomposed into different modal components, and the respiratory signal and heart rate signal are extracted through the corresponding bandpass filter paths respectively, and finally the respiratory waveform and heart rate waveform are output.

[0073] Specifically, after initially separating the signal frequency bands using a bandpass filter, variational mode decomposition (VMD) is introduced to adaptively decompose the target signal into different mode components.

[0074] In summary, the non-contact microwave monitoring method for physiological parameters in the above embodiments of the present invention acquires the reflected signal, separates the real and imaginary data of the reflected signal, and performs structured data partitioning based on the number of receiving antennas, the number of frames, and the number of chirp signal pulses; the partitioned data is passed through a static clutter suppression filter to obtain a preprocessed signal, and the preprocessed signal is processed by range-dimensional FFT to obtain a range-angle heatmap; based on the range-angle heatmap, the preprocessed signal is phase-demodulated to obtain a phase signal, and the phase signal is filtered and denoised to obtain a target signal; finally, the target signal is extracted to obtain the corresponding physiological parameters. Specifically, non-contact precise monitoring of respiration, heart rate, and blood pressure is achieved through microwave radar technology.

[0075] Example 2

[0076] Embodiment 2 of the present invention also provides a method for non-contact microwave monitoring of physiological parameters. The difference between this method and Embodiment 1 is that it can better achieve anti-interference capability for non-contact microwave monitoring of physiological parameters. Specifically, before the steps of acquiring the reflected signal, separating the real and imaginary parts of the reflected signal, and performing structured data partitioning based on the number of receiving antennas, the number of frames, and the number of chirp signal pulses, the following steps are included:

[0077] The system acquires the original radar signal, namely the intermediate frequency signal (IF signal) generated by mixing the received reflected signal and the transmitted signal, the digital signal after analog-to-digital conversion (ADC), and generates a high-resolution point cloud using range-azimuth joint FFT.

[0078] The clustering algorithm is used to locate the user's chest cavity or carotid artery region, and adaptive beamforming (MVDR algorithm) is used to form a beam at a preset angle in the user's chest cavity or carotid artery region. At the same time, nulls of a preset depth are inserted in the adjacent bed direction. In this embodiment of the invention, adaptive beamforming (MVDR algorithm) is used to form a 10° narrow beam in the main valve direction (patient position), and nulls with a depth >20dB are inserted in the adjacent bed direction to suppress interference from the physiological signals of others.

[0079] Within the chest cavity or carotid artery region, Kalman filtering is used to predict the user's body movement trajectory. In this way, a three-dimensional motion state model of the target area (including position and velocity information) is established. Based on the distance, angle and Doppler velocity observation data acquired in real time by millimeter-wave radar, the state prediction and update are performed using the Kalman filtering algorithm. The optimal position of the target is dynamically estimated, and the beam pointing is adjusted in real time to ensure that the interference suppression ratio is maintained above -15dB even when the patient moves slightly.

[0080] In other embodiments of the present invention, space-time adaptive processing (STAP) can be combined to eliminate multipath reflection interference, and polarization filtering can be used to suppress cross-polarization reflections of adjacent bed metal devices.

[0081] It should be noted that the interference suppression capability is improved through the joint optimization of space-time adaptive processing (STAP) and polarization filtering. Specifically, the time dimension of space-time adaptive processing is extended to the respiratory cycle synchronization frame, that is, the signal processing frame length (2-5 seconds) is divided according to the user's respiratory rate (usually 0.2~0.5Hz). This allows space-time adaptive processing to match the periodic characteristics of physiological signals when eliminating multipath reflections, reducing the attenuation of effective signals. At the same time, a polarization feature library of metal devices is introduced into polarization filtering. Through machine learning, the cross-polarization reflection features (such as polarization angle and reflection coefficient) of preset adjacent bed metal devices (such as monitors and IV stands) are pre-stored. When unknown polarization interference is detected, similarity matching is performed based on the feature library to generate targeted polarization suppression parameters (such as adjusting the polarization isolation to above 30dB).

[0082] The radar transmits detection signals in its idle frequency band (frequency band not used for physiological signal detection), scans the distribution of objects within a preset range, and establishes a dynamic interference source map (marking the location, intensity, and changing trend of interference sources such as metal equipment and moving human bodies).

[0083] When a neighboring user moves, the system predicts the interference path of the interference source on the local bed's signal based on the changes in the interference source map, adjusts the null direction of the adaptive beamforming (MVDR) algorithm in advance, and automatically increases the null depth when the interference source gets close. The linkage space-time adaptive processing increases the suppression weight of multipath reflection in this direction.

[0084] More specifically, in the process of improving interference suppression capability, the respiratory rate is first extracted in real time. That is, by using range-Doppler spectral analysis of radar signals, the periodic characteristics of thoracic cavity movement are extracted, and the respiratory rate is calculated. (Unit: Hz), the calculation formula is:

[0085] ;

[0086] in, The time interval (in seconds) between two consecutive respiratory peaks is defined. The frame length is then adaptively adjusted, binding the signal processing frame length L to the respiratory cycle to satisfy... (k is an integer, usually 1 to 2, i.e., frame length = 1 to 2 breath cycles).

[0087] Furthermore, the STAP space-time matrix is ​​reconstructed; specifically, a space-time data matrix is ​​constructed based on the synchronization frames. (M is the number of antenna array elements, N is the number of snapshots within a frame), its time dimension is aligned with the respiratory cycle, allowing the STAP interference suppression weight ω to more accurately avoid physiological signal frequency bands. The formula is:

[0088] ;

[0089] in, Let be the spacetime covariance matrix, s be the target signal steering vector, and H denote the conjugate transpose. To find the ω that minimizes the following expression, Let the objective function be a quadratic form. The "subject to" constraint requires that the conjugate inner product of ω and vector s equals 1. The above formula can be understood as a quadratic form minimization problem with linear constraints. The core is to find a weighted product of ω and s that satisfies the condition that the inner product of ω and s is 1. The vector ω that minimizes the quadratic form.

[0090] In this embodiment of the invention, the metal equipment polarization feature library stores the three-dimensional polarization features of the metal equipment, including polarization angles. (Angle between the electric field vector and the horizontal direction, ranging from 0 to 180°), reflection coefficient (Ratio of reflected electric field to incident electric field amplitude, ranging from 0 to 1) and polarization phase difference (The phase difference between the reflected and incident signals, ranging from -π to π), represented by the feature library. K represents the number of device types, such as monitors, IV stands, etc.

[0091] Calculate the characteristics of unknown polarization interference. Euclidean distance to samples in the database:

[0092] ;

[0093] Select The suppression parameter corresponding to the smallest sample is used to generate the polarization filter matrix P:

[0094] ;

[0095] Where α is the polarization isolation (taken as 30-40dB based on the matching results), and cross-polarization interference is filtered out by matrix P.

[0096] Finally, the cascaded output of STAP and polarization filter is: .

[0097] In the process of establishing a dynamic interference source map by transmitting detection signals in the radar's idle frequency band and scanning the distribution of objects within a preset range, a spare frequency band outside the radar's operating frequency band is used to transmit linear frequency modulated signals. Subsequently, range-angle FFT is performed on the echo signals to calculate the three-dimensional coordinates of the interference sources. The formula is:

[0098] ;

[0099] in, The distance (calculated by time delay) is the distance. The pitch angle, The azimuth angle is calculated from the array beam synthesis.

[0100] Scan once at preset time intervals, if the location of the interference source changes If the change exceeds a preset amount, it is marked as a "moving interference source," and the motion vector of that target in the map is updated. .

[0101] To predict the interference path of the moving interference source to the local bed signal, prediction is performed based on the motion vector of the moving interference source. Location at any moment And calculate its azimuth angle relative to the radar. .

[0102] Furthermore, the null orientation angle of the MVDR algorithm Revised to:

[0103] ;

[0104] ±3° is reserved for error compensation. In this embodiment of the invention, the zero-depression depth D is determined according to the distance. Dynamic adjustment:

[0105] ;

[0106] Finally, a suppression weight is added to the predicted interference direction, and the constraints of STAP are modified as follows:

[0107] and ;

[0108] in, The steering vector is the direction of interference.

[0109] Example 3

[0110] Please see Figure 2 , Figure 2 This is a structural block diagram of a non-contact microwave monitoring system for physiological parameters provided in Embodiment 3 of the present invention. This non-contact microwave monitoring system 200 is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0111] Specifically, the non-contact microwave monitoring system 200 for physiological parameters includes: an acquisition module 21, a first processing module 22, a second processing module 23, a first parameter determination module 24, and a second parameter determination module 25, wherein:

[0112] The acquisition module 21 is used to acquire the reflected signal, separate the real part data and the imaginary part data of the reflected signal, and perform structured data division according to the number of receiving antennas, the number of frames, and the number of Chirp signal pulses;

[0113] The first processing module 22 is used to pass the divided data through a static clutter suppression filter to obtain a preprocessed signal, and to perform range-dimensional FFT processing on the preprocessed signal to obtain a range-angle heatmap;

[0114] The second processing module 23 is used to perform phase demodulation on the preprocessed signal according to the distance-angle heatmap to obtain a phase signal, and to filter and denoise the phase signal to obtain a target signal. The arctangent phase value of the complex signal is calculated by phase demodulation. At the same time, the phase of the multi-cycle Chirp signal is superimposed by incoherent accumulation technology to improve the signal-to-noise ratio and then phase unwinding is performed to eliminate the 2π jump phenomenon. In addition, high-frequency noise is eliminated by using a moving average filter.

[0115] The pulse band was extracted using a fourth-order Butterworth zero-phase-shift filter, with a cutoff frequency of 0.5Hz to 6Hz.

[0116] Residual interference is suppressed by wavelet threshold denoising to obtain the pulse wave signal within a preset time period, which is used to determine blood pressure parameters;

[0117] In addition, phase demodulation is used to separate phase changes caused by breathing and heartbeat, then burst interference is eliminated by impulse noise removal algorithm, and signal frequency bands are initially separated by bandpass filter;

[0118] The first parameter determination module 24 is used to extract features from the target signal, calculate the pulse wave transit time, and convert the pulse wave transit time into continuous estimates of systolic and diastolic blood pressure using a regression model based on the correlation between the pulse wave transit time and blood pressure. The relationship between the pulse wave transit time and blood pressure is expressed as follows: ;

[0119] Where SBP is systolic blood pressure, DBP is diastolic blood pressure, PTT is pulse wave transit time, and a, b, and A are individual-specific parameters obtained through regression fitting. This is the reference calibration value for systolic blood pressure. This is the reference calibration value for diastolic blood pressure. This is the reference calibration value for pulse wave conduction time;

[0120] The second parameter determination module 25 is used to adaptively decompose the target signal into different modal components when determining respiratory parameters and heart rate parameters, extract respiratory signals and heart rate signals respectively through corresponding bandpass filtering paths, and finally output respiratory waveforms and heart rate waveforms. Variational mode decomposition is introduced to adaptively decompose the target signal into different modal components.

[0121] Furthermore, in some optional embodiments of the present invention, the non-contact microwave monitoring physiological parameter system 200 further includes:

[0122] The generation module is used to acquire the raw radar signal and generate a high-resolution point cloud using range-azimuth joint FFT;

[0123] The clustering module is used to locate the user's chest cavity or carotid artery region through a clustering algorithm, and to form a beam at a preset angle in the user's chest cavity or carotid artery region using adaptive beamforming. At the same time, nulls of a preset depth are inserted in the adjacent bed direction.

[0124] The adjustment module is used to predict the user's body movement trajectory in the chest cavity or carotid artery region using Kalman filtering and adjust the beam direction in real time.

[0125] Furthermore, in some optional embodiments of the present invention, the non-contact microwave monitoring physiological parameter system 200 further includes:

[0126] The interference suppression module is used to improve interference suppression capability through joint optimization of spatiotemporal adaptive processing and polarization filtering. Specifically, the time dimension of spatiotemporal adaptive processing is extended to the respiratory cycle synchronization frame, so that the spatiotemporal adaptive processing matches the periodic characteristics of physiological signals when eliminating multipath reflections, reducing the attenuation of effective signals. At the same time, a polarization feature library of metal devices is introduced into polarization filtering. The cross-polarization reflection features of the metal devices in the adjacent bed are pre-stored through machine learning. When unknown polarization interference is detected, similarity matching is performed based on the feature library to generate targeted polarization suppression parameters.

[0127] The scanning module is used to transmit detection signals through the radar's idle frequency band, scan the distribution of objects within a preset range, and establish a dynamic interference source map;

[0128] The adjustment module is used to predict the interference path of the neighboring user to the signal of this bed based on the change of the location of the interference source map when the neighboring user moves, and adjust the null direction of the adaptive beamforming algorithm in advance. When the interference source gets close, the null depth is automatically increased. The linkage space-time adaptive processing increases the suppression weight of multipath reflection in this direction.

[0129] Example 4

[0130] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The image shows an electronic device according to Embodiment 4 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the non-contact microwave monitoring of physiological parameters as described above.

[0131] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0132] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, FlashCard, etc., equipped on the electronic device. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0133] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0134] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for non-contact microwave monitoring of physiological parameters as described above.

[0135] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0137] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0138] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0139] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for non-contact microwave monitoring of physiological parameters, characterized in that, In a scenario involving a microwave radar device for transmitting microwave signals and receiving reflected signals, the method includes: The reflected signal is acquired, the real part and the imaginary part of the reflected signal are separated, and the structured data is divided according to the number of receiving antennas, the number of frames, and the number of chirp signal pulses. The segmented data is passed through a static clutter suppression filter to obtain a preprocessed signal, and the preprocessed signal is then subjected to range-dimensional FFT processing to obtain a range-angle heatmap. Based on the distance-angle heatmap, the preprocessed signal is phase demodulated to obtain a phase signal, and the phase signal is then filtered and denoised to obtain the target signal. When determining blood pressure parameters, the target signal is feature extracted, the pulse wave transit time is calculated, and based on the correlation between the pulse wave transit time and blood pressure, a regression model is used to convert the pulse wave transit time into continuous estimates of systolic and diastolic blood pressure. When determining respiratory and heart rate parameters, the target signal is adaptively decomposed into different modal components, and the respiratory and heart rate signals are extracted through corresponding bandpass filtering paths, and finally the respiratory waveform and heart rate waveform are output. The steps preceding the acquisition of the reflected signal, separation of the real and imaginary parts of the reflected signal, and structured data partitioning based on the number of receiving antennas, frame number, and chirp signal pulse number include: The raw radar signal was acquired, and a high-resolution point cloud was generated using range-azimuth joint FFT. Clustering algorithms are used to locate the user's chest cavity or carotid artery region, and adaptive beamforming is used to form a beam at a preset angle in the user's chest cavity or carotid artery region. At the same time, nulls of a preset depth are inserted in the adjacent bed direction. Within the thoracic or carotid artery region, the user's body movement trajectory is predicted using Kalman filtering, and the beam direction is adjusted in real time. By jointly optimizing spatiotemporal adaptive processing and polarization filtering, the interference suppression capability is improved. Specifically, the time dimension of spatiotemporal adaptive processing is extended to the respiratory cycle synchronization frame, so that the spatiotemporal adaptive processing matches the periodic characteristics of physiological signals when eliminating multipath reflections, reducing the attenuation of effective signals. At the same time, a polarization feature library of metal devices is introduced into polarization filtering. The cross-polarization reflection characteristics of the metal devices in the adjacent bed are pre-stored through machine learning. When unknown polarization interference is detected, similarity matching is performed based on the feature library to generate targeted polarization suppression parameters. By transmitting detection signals in the idle frequency band of the radar, the distribution of objects within a preset range is scanned to establish a dynamic interference source map; When a neighboring user moves, the system predicts the interference path of the interference source on the local bed's signal based on the changes in the interference source map, adjusts the null pointing of the adaptive beamforming algorithm in advance, and automatically increases the null depth when the interference source gets close. The linkage space-time adaptive processing increases the suppression weight of multipath reflection in the direction of the interference source.

2. The method for non-contact microwave monitoring of physiological parameters according to claim 1, characterized in that, In the step of performing phase demodulation on the preprocessed signal according to the distance-angle heatmap to obtain the phase signal, the arctangent phase value of the complex signal is calculated by phase demodulation. At the same time, the phase of the multi-cycle Chirp signal is superimposed by incoherent accumulation technology to improve the signal-to-noise ratio and then phase unwinding is performed to eliminate the 2π jump phenomenon.

3. The method for non-contact microwave monitoring of physiological parameters according to claim 2, characterized in that, In the step of filtering and denoising the phase signal to obtain the target signal, high-frequency noise is eliminated by using a moving average filter. The pulse band was extracted using a fourth-order Butterworth zero-phase-shift filter, with a cutoff frequency of 0.5Hz to 6Hz. Residual interference is suppressed by wavelet threshold denoising to obtain the pulse wave signal within a preset time period, which is used to determine blood pressure parameters.

4. The method for non-contact microwave monitoring of physiological parameters according to claim 3, characterized in that, The relationship between pulse wave conduction time and blood pressure is expressed as follows: ； Where SBP is systolic blood pressure, DBP is diastolic blood pressure, PTT is pulse wave transit time, and a, b, and A are individual-specific parameters obtained through regression fitting. This is the reference calibration value for systolic blood pressure. This is the reference calibration value for diastolic blood pressure. This is the reference calibration value for pulse wave conduction time.

5. The method for non-contact microwave monitoring of physiological parameters according to claim 4, characterized in that, In the step of performing phase demodulation on the preprocessed signal based on the distance-angle heatmap to obtain a phase signal, and then filtering and denoising the phase signal to obtain the target signal, phase demodulation is used to separate phase changes caused by breathing and heartbeat. Subsequently, sudden interference is eliminated by an impulse noise removal algorithm, and the signal frequency band is initially separated by a bandpass filter.

6. The method for non-contact microwave monitoring of physiological parameters according to claim 5, characterized in that, Variational mode decomposition is introduced to adaptively decompose the target signal into different mode components.

7. A system for non-contact microwave monitoring of physiological parameters, characterized in that, For implementing the method of non-contact microwave monitoring of physiological parameters as described in any one of claims 1-6, the system comprises: The acquisition module is used to acquire the reflected signal, separate the real part and imaginary part of the reflected signal, and perform structured data partitioning based on the number of receiving antennas, the number of frames, and the number of chirp signal pulses; The first processing module is used to pass the segmented data through a static clutter suppression filter to obtain a preprocessed signal, and to perform range-dimensional FFT processing on the preprocessed signal to obtain a range-angle heatmap; The second processing module is used to perform phase demodulation on the preprocessed signal according to the distance-angle heatmap to obtain a phase signal, and to filter and denoise the phase signal to obtain the target signal; The first parameter determination module is used to extract features from the target signal, calculate the pulse wave transit time, and convert the pulse wave transit time into continuous estimates of systolic and diastolic blood pressure using a regression model based on the correlation between the pulse wave transit time and blood pressure. The second parameter determination module is used to adaptively decompose the target signal into different modal components when determining respiratory parameters and heart rate parameters, extract the respiratory signal and heart rate signal respectively through the corresponding bandpass filter path, and finally output the respiratory waveform and heart rate waveform.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method for non-contact microwave monitoring of physiological parameters as described in any one of claims 1-6.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for non-contact microwave monitoring of physiological parameters as described in any one of claims 1-6.

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