A method and system for monitoring the breathing and heartbeat of multiple people based on FMCW radar

Abstract

This invention discloses a method and system for monitoring the breathing and heartbeat of multiple individuals based on FMCW radar, relating to the fields of next-generation information technology and smart healthcare. The key technical points of this invention include: combining a constant false alarm rate (CFAR) detection method with the maximum number of extreme points within the range-angle matrix to identify the number and location of targets within the radar monitoring area, extracting their phase information, then using a sparse optimization method to extract the breathing and heartbeat signals of the targets from the phase signals, and finally displaying the target's location and breathing / heartbeat information in real time on the system interface. This invention overcomes the limitation of previous methods that could only detect a single person, effectively monitoring the breathing and heartbeat information of multiple individuals, and the system is easy to operate, demonstrating strong innovation and competitiveness.

Description

Technical Field

[0001] This invention relates to the fields of next-generation information technology and smart healthcare, specifically to a method and system for monitoring the breathing and heartbeat of multiple individuals based on FMCW radar. Background Technology

[0002] Currently, the main technologies for detecting human vital signs such as respiration and heartbeat are direct contact methods. Direct contact detection methods include electrocardiograms, finger-clip pulse oximeters, and electronic blood pressure monitors. However, these tests are expensive, most of them are medical devices, which are inconvenient for non-professionals to operate, and require contact with the human body, making the tests inconvenient.

[0003] Radar offers significant advantages in detecting respiration and heartbeat. Firstly, radar has strong signal penetration, allowing it to operate covertly in its surroundings without contact with the human body. Secondly, radar has a wider detection range and is less affected by external environmental conditions, exhibiting better stability. Furthermore, radar can perform long-term monitoring, and the accumulated data is highly helpful in assessing human health. This contactless technology allows for continuous monitoring of the user's health, enabling timely alerts in emergencies such as fainting or cardiac arrest, reducing the risk of death or disability and thus alleviating the burden on healthcare. The advantages of radar in detecting respiration and heartbeat have attracted widespread attention.

[0004] Existing research mainly focuses on detecting respiratory and heartbeat signals in single individuals, and real-time monitoring systems are rare. Detecting respiratory and heartbeat signals in multiple individuals is a significant advantage of radar monitoring, and real-time monitoring systems have broad application prospects. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for monitoring the breathing and heartbeat of multiple people based on FMCW radar, which can accurately and stably monitor the breathing and heartbeat signals of multiple people.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a multi-person breathing and heartbeat monitoring system based on FMCW radar, including a radar configuration module, a data acquisition and analysis module, a target identification and information acquisition module, a data preprocessing module, a breathing and heartbeat information extraction module, and a target location and breathing and heartbeat information display module;

[0007] The radar configuration module is configured with radar parameters and time-division multiplexing operating mode.

[0008] The data acquisition and parsing module is configured to control data upload and parse the data to form a distance-angle matrix;

[0009] The target recognition and information acquisition module is configured to combine the constant false alarm rate (CFAR) detection method and the number of maximum extrema of the distance-angle matrix to determine the number and location of targets within the monitoring area, and extract the real and imaginary data to form in-phase and orthogonal components;

[0010] The data preprocessing module is configured to remove the DC bias using the mean method, then obtain the wound radar phase using the arctangent method, and finally unwound the radar phase using the differential method.

[0011] The breathing and heartbeat information extraction module is configured to extract the breathing and heartbeat signals of the target using sparse optimization, and to estimate the respiratory rate and heart rate using the maximum frequency method.

[0012] The target location and respiratory and heart rate information display module is configured to draw respiratory and heart rate signal waveforms in real time on the interface and update the target's location, respiratory rate, and heart rate information at 6-second intervals.

[0013] A method for monitoring the breathing and heartbeat of multiple individuals based on FMCW radar includes the following steps:

[0014] Step 1: Configure radar system parameters;

[0015] Step 2: Acquire and reassemble the original radar data into a range-angle matrix;

[0016] Step 3: Determine the number and location of targets within the monitoring area using the constant false alarm rate (CFAR) detection method and the number of maximum extreme values ​​of the distance-angle matrix;

[0017] Step 4: Extract the real part of the data at the target location to form in-phase components, and the imaginary part to form quadrature components;

[0018] Step 5: Perform data preprocessing on the in-phase and quadrature components to obtain the true radar phase signal;

[0019] Step 6: Extract respiratory and heartbeat signals from the radar phase signals, and estimate the respiratory rate and heart rate;

[0020] Step 7: Display the target's location, breathing, and heart rate information on the interface in real time.

[0021] Furthermore, the specific process of step three is as follows: using the constant false alarm rate (CFAR) detection method, the presence of a target is detected. If no target is found, the in-phase component and the quadrature component are directly set to zero. Otherwise, if a target is found, the number and location of the target are determined by the number of extreme points in the distance-angle matrix.

[0022] Furthermore, in step four, the in-phase and quadrature components extracted each time are added to the corresponding vector. When 6 seconds of data extraction is completed, proceed to step five; otherwise, return to step two.

[0023] Furthermore, the specific process of step five is as follows: first, the DC bias in the in-phase component and the quadrature component is removed; then, the arctangent method is used to obtain the wound radar phase; and finally, the differential method is used to unwound the radar phase to obtain the true radar phase.

[0024] Furthermore, in step six, the extraction model for respiratory and heartbeat signals is as follows:

[0025]

[0026] In the formula w h and w r c is the sparse adjustment parameter. r c h Let D be the frequency domain sparsity coefficients for respiration and heartbeat. H These are the Fourier transform matrix and its inverse matrix, respectively; for a vector x of length N, Respiratory rate and heart rate were estimated using the maximum frequency estimation method.

[0027] The beneficial effects of this invention are as follows: The multi-person breathing and heartbeat monitoring method and system based on FMCW radar provided by this invention, combined with the constant false alarm rate (CFAR) detection method and the maximum number of extreme points within the range-angle matrix, identifies the number and location of targets within the radar monitoring area and extracts their phase information. Then, a sparse optimization method is used to extract the breathing and heartbeat signals of the targets from the phase signals. Finally, the target's location and breathing / heartbeat information are displayed in real time on the system interface. This invention uses a sparse optimization method for the extraction of breathing and heartbeat signals, resulting in low computational complexity and enabling multi-target monitoring, making this invention highly innovative and competitive. Attached Figure Description

[0028] Figure 1 This is a schematic diagram illustrating the workflow of a multi-person respiratory and heartbeat monitoring method based on FMCW radar in an embodiment of the present invention.

[0029] Figure 2 This is a schematic diagram of a multi-person respiratory and heartbeat monitoring system based on FMCW radar, as described in an embodiment of the present invention.

[0030] Figure 3 This is a schematic diagram of the monitoring interface in the absence of a target in an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the monitoring interface in the case of a single target in an embodiment of the present invention;

[0032] Figure 5 Figure 1 is a schematic diagram of the monitoring interface under dual-target conditions in an embodiment of the present invention; wherein Figure (a) is an experimental diagram of multi-person heartbeat and respiration detection; and Figure (b) is a schematic diagram of the detection results of the host computer for multi-person heartbeat and respiration detection. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of the invention.

[0034] 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 this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0035] A method for monitoring the respiration and heartbeat of multiple individuals based on FMCW radar, the workflow of which is as follows: Figure 1 As shown, it includes the following steps:

[0036] Step 1: Configure radar system parameters; specifically, configure the radar operating frequency to 77GHz, the duration of a single chirp to 50μs, the pulse repetition period to 50ms, the bandwidth to 4GHz, the number of sampling points per chirp to 200, the number of chirps per frame to 4, and the operating mode to time division multiplexing mode.

[0037] Step 2: Acquire and reassemble the original radar data into a range-angle matrix; specifically, based on the uploaded data format, reassemble the data into a complex number form, then generate an antenna number-fast time matrix, and obtain the angle-range matrix through Fourier transform.

[0038] Step 3: Determine the number and location of targets within the monitoring area using the constant false alarm rate (CFAR) detection method and the number of maximum extrema in the range-angle matrix. The specific process is as follows: Use the CFAR detection method to detect whether there are targets. If there are no targets, the in-phase and quadrature components are directly set to zero, and it is determined whether 6 seconds of data extraction has been completed. If yes, proceed to step 5; otherwise, return to step 2. Otherwise, if targets exist, determine the number and location of targets using the number of extrema in the range-angle matrix, and proceed to step 4.

[0039] Step 4: Extract the real part of the data at the target location to form in-phase components and the imaginary part to form quadrature components, and determine whether the data extraction for 6 seconds has been completed. If yes, proceed to step 5; otherwise, return to step 2.

[0040] Step 5: Perform data preprocessing on the in-phase and quadrature components to obtain the true radar phase signal;

[0041] Step 6: Extract respiratory and heartbeat signals from the radar phase signals, and estimate the respiratory rate and heart rate;

[0042] Step 7: Display the target's location, breathing, and heart rate information on the interface in real time.

[0043] Further, the specific process of step five is as follows: First, the DC bias in the in-phase component and quadrature component is estimated and removed using the mean method. Then, the tangent method is used to obtain the entangled radar phase. Finally, the differential method is used to untangle the entangled phase to obtain the true radar phase.

[0044] Furthermore, in step six, the extraction model for respiratory and heartbeat signals is as follows:

[0045]

[0046] In the formula w h and w r c is the sparse adjustment parameter. r c h Let D be the frequency domain sparsity coefficients for respiration and heartbeat. H These are the Fourier transform matrix and its inverse, respectively. For a vector x of length N, By solving the model, sparse frequency domain estimates of respiration and heartbeat can be obtained. By performing an inverse Fourier transform, the waveforms of respiration and heartbeat can be obtained. The maximum frequency estimation method is used to estimate the respiratory rate and heart rate.

[0047] Furthermore, the specific process of step seven is as follows: draw the breathing and heartbeat signal waveforms in real time on the interface, and update the target's position, breathing rate, and heart rate information at 6-second intervals;

[0048] A multi-person respiratory and heart rate monitoring system based on FMCW radar, such as Figure 2 As shown, the system includes:

[0049] The radar configuration module is configured with radar parameters and time-division multiplexing operating mode.

[0050] The data acquisition and parsing module is configured to upload data via an Ethernet interface and parse the data to form a distance-angle matrix;

[0051] The target recognition and information acquisition module is configured to combine the constant false alarm rate (CFAR) detection method and the number of maximum extrema of the distance-angle matrix to determine the number and location of targets within the monitoring area, and extract the real and imaginary data to form in-phase and orthogonal components;

[0052] The data preprocessing module is configured to remove the DC bias using the mean method, then obtain the wound radar phase using the arctangent method, and finally unwound the radar phase using the differential method.

[0053] The breathing and heartbeat information extraction module is configured to extract the breathing and heartbeat signals of the target using sparse optimization, and to estimate the respiratory rate and heart rate using the maximum frequency method.

[0054] The target location and respiratory and heart rate information display module is configured to draw respiratory and heart rate signal waveforms in real time on the interface and update the target's location, respiratory rate, and heart rate information at 6-second intervals.

[0055] The aforementioned methods for monitoring the breathing and heartbeat of multiple individuals were embedded into the system and experimentally verified. It can be seen that, in the absence of a target in the radar monitoring area, the system monitoring results are as follows: Figure 3 As shown, the system displays no target in the current area, and both respiratory and heartbeat signals are zero; when a single target exists in the radar monitoring area, the system monitoring results are as follows. Figure 4 As shown, the system displays the distance and angle of a single target from the radar within the current area, along with the corresponding respiratory and heart rate waveforms. The estimated respiratory rate is 20 breaths / minute and the heart rate is 70 beats / minute. Target 1 does not exist, and its corresponding respiratory and heart rate signals are zero. When two targets are present in the radar monitoring area, the system monitoring results are as follows: Figure 5 As shown, the system displays the positions of two targets within the current area and their breathing and heart rate waveforms. Target 1 has a breathing rate of 20 breaths / minute and a heart rate of 90 beats / minute, while target 2 has a breathing rate of 30 breaths / minute and a heart rate of 70 beats / minute. The system can effectively identify the number of targets and extract the corresponding breathing and heart rate signals in different scenarios.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A FMCW radar based multi-person breathing and heartbeat monitoring system, characterized in that, The radar configuration module, the data acquisition and analysis module, the target identification and information acquisition module, the data preprocessing module, the respiratory and heartbeat information extraction module, and the target position and respiratory and heartbeat information display module are included. The radar configuration module configures radar parameters and selects a time division multiplexing mode. The data acquisition and analysis module is configured to control data uploading and analyze data into a distance-angle matrix. The target identification and information acquisition module is configured to determine the number and position of targets in the monitoring area by combining a constant false alarm detection method and the maximum number of extreme values of the distance-angle matrix, and extract real and imaginary data to form in-phase and quadrature components. The data preprocessing module is configured to remove direct current bias using the mean method, then obtain the wrapped radar phase using the arctangent method, and finally unwrap the true radar phase using the difference method. The respiratory and heartbeat information extraction module is configured to extract the respiratory and heartbeat signals of the target using sparse optimization and estimate the respiratory rate and heart rate using the maximum frequency method. The extraction model of the respiratory and heartbeat signal is: where w h and w r are sparsity regularization parameters, c r , c h are the frequency domain sparsity coefficients of respiration and heartbeat, D, D H are the Fourier transform matrix and its inverse, respectively; For a vector x of length N, Respiration rate and heart rate estimation employs a maximum frequency estimation method; The target position and respiratory and heartbeat information display module is configured to real-time draw the respiratory and heartbeat signal waveform in the interface, update the position, respiratory rate, and heart rate information of the target at a time interval of 6 seconds.

2. A monitoring method of a multi-person breathing and heartbeat monitoring system based on FMCW radar, characterized by, The method comprises the following steps: Step one, configure the radar system parameters; Step two, acquire and reorganize the radar raw data into a distance-angle matrix; Step three, determine the number and position of targets in the monitoring area using a constant false alarm detection method and the maximum number of extreme values of the distance-angle matrix; Step four, extract the real part of the data at the target position to form an in-phase component and the imaginary part to form a quadrature component; Step five, perform data preprocessing on the in-phase and quadrature components to obtain the true radar phase signal; Step six, extract the respiratory and heartbeat signals from the radar phase signal and estimate the respiratory rate and heart rate; The extraction model of the respiratory and heartbeat signal is: where w h and w r are sparsity regularization parameters, c r , c h are the frequency domain sparsity coefficients of respiration and heartbeat, D, D H are the Fourier transform matrix and its inverse, respectively; For a vector x of length N, Respiration rate and heart rate estimation employs a maximum frequency estimation method; Step seven, real-time display the position, respiratory, and heartbeat information of the target in the interface.

3. The method of claim 2, wherein the method further comprises: The specific process of step three is to use the constant false alarm detection method to detect whether there is a target, if not, the in-phase component and the quadrature component are directly set to zero, otherwise, the number and position of the target are determined by the number of extreme points in the distance-angle matrix.

4. The method of claim 2, wherein the method further comprises: In step four, the in-phase component and the quadrature component extracted each time are added to the corresponding vector, when the data extraction of 6 seconds is completed, step five is entered, otherwise, step two is returned.

5. The method of claim 2, wherein the method further comprises: The specific process of step five is to first remove the direct current bias in the in-phase component and the quadrature component, then obtain the wrapped radar phase using the arctangent method, and finally unwrap the true radar phase using the difference method.

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