A radar-based non-contact human vital sign sensing method and system

Abstract

This invention relates to a radar-based non-contact human vital sign sensing method and system, comprising: acquiring radar echo signals including the target; processing the intermediate frequency signal obtained after low-pass filtering to obtain the signal spectrum; selecting the range domain with the highest dynamic energy as the range domain for sensing the target; determining the dynamic or static state of the target based on the signal spectrum; if dynamic, re-acquiring the signal; if static, performing range beamforming on the signal within the range domain of the target to enhance the signal-to-noise ratio of the target signal; performing a fast Fourier transform on the target signal to obtain the spectrum of a complex signal, and calculating the respiratory rate; sequentially performing a first-order difference and fast Fourier transform on the target signal to obtain the spectrum of a differential complex signal; and calculating the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate. This invention can expand the range of respiratory and heartbeat detection and improve the accuracy of heartbeat detection; it can be widely applied in the field of non-contact human vital sign sensing.

Description

Technical Field

[0001] This invention relates to the field of non-contact sensing technology, and in particular to a radar-based non-contact human vital signs sensing method and system. Background Technology

[0002] With economic and social development and the accelerating aging process, people are paying increasing attention to health issues. Breathing and heart rate, as important indicators of human vital signs, can be effectively monitored long-term to help people understand their health status and provide early warnings of some cardiopulmonary diseases. However, traditional methods of detecting breathing and heart rate are mostly contact-based, such as wristbands, chest and abdominal straps, and electrocardiographs. These devices require the wearer to wear specific sensors, which is an intrusive approach that increases the burden on the user and also presents power supply issues, making long-term monitoring of vital signs difficult in a home setting. Non-contact sensing better solves these problems. For example, radar-based breathing and heart rate sensing uses a radar transmitter to transmit signals that are reflected by the human body and reach the radar receiver. These signals carry information related to human movement and physiological state. By processing this information, information such as breathing, heart rate, and location can be obtained. This type of method does not require the wearer to carry the relevant equipment, achieving the above functions non-contactly.

[0003] The basic principle of non-contact sensing of human respiration and heartbeat is to detect the minute vibrations they cause in the chest cavity. Both respiration and heartbeat cause rises and falls and vibrations in the chest cavity. The amplitude of respiration is 5mm–12mm, while the amplitude of vibration caused by heartbeat is approximately 0.1mm–0.4mm. Some radars, such as the common 77GHz millimeter-wave radar, can achieve micrometer-level accuracy, thus allowing the detection of human respiration and heartbeat through radar. These vibrations cause phase fluctuations in the radar's received signal, and previous methods largely relied on analyzing these phase signals to sense respiration and heartbeat. Furthermore, it is recognized that respiration and heartbeat occur in different frequency bands: respiration occurs at 0.15Hz to 0.5Hz, while heartbeats occur at frequencies above 0.6Hz. Therefore, previous methods primarily used spectral analysis of the phase signals to detect respiratory and heart rates.

[0004] However, current radar-based methods for detecting breathing and heartbeats face two challenges. First, because the chest vibrations caused by a heartbeat are relatively weak, the heartbeat signal is easily drowned out by noise at long distances, resulting in a poor signal-to-noise ratio. This problem significantly limits the range of heartbeat detection and hinders the practical application of such systems. The second challenge is the difficulty in eliminating static signals, leading to spectral distortion of the phase signal. Traditional methods eliminate static signals by subtracting the mean or fitting a circle, but due to the weakness of the signal, neither of these methods can completely eliminate static signals. Therefore, the presence of static vectors still significantly reduces the accuracy of heartbeat detection. These two challenges limit the range and accuracy of heartbeat detection, hindering the practical deployment of such systems. Summary of the Invention

[0005] To address the issues of low signal-to-noise ratio (SNR) and undetectable heartbeats due to the difficulty in eliminating static signals when radar is far from the target, this invention aims to provide a non-contact radar-based method and system for sensing human vital signs. By combining range-based beamforming with angle-based beamforming to enhance the signal, the SNR is improved. Furthermore, the method of directly extracting the heartbeat from the spectrum of the complex signal eliminates the need for static elimination, thereby expanding the detection range and improving the accuracy of heartbeat detection.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a non-contact human vital sign sensing method based on radar, comprising: acquiring radar echo signals including the target, obtaining intermediate frequency signals after passing through a low-pass filter, processing the intermediate frequency signals to obtain a signal spectrum, and selecting the range domain with the largest dynamic energy as the range domain of the sensing target; determining the dynamic or static state of the target based on the signal spectrum, if it is dynamic, re-acquiring the signal, and if it is static, performing range beamforming on the signal in the range domain of the sensing target to enhance the signal-to-noise ratio of the target signal; performing a fast Fourier transform on the target signal to obtain the spectrum of a complex signal, and calculating the respiratory rate; performing a first-order difference and a fast Fourier transform on the target signal sequentially to obtain the spectrum of a differential complex signal, and calculating the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0007] Furthermore, the step of selecting the range domain with the highest dynamic energy as the range domain of the perceived target includes:

[0008] The signal spectrum is obtained by performing a fast Fourier transform on the intermediate frequency signal, with each frequency corresponding to a distance.

[0009] By concatenating the results of the slow-time chirp Fourier transforms, a distance-time graph is obtained, with the vertical axis representing distance and the horizontal axis representing time.

[0010] The distance to the breathing target is determined based on the distance-time diagram; the ratio of the energy of the signal in each distance domain changing with slow time within a preset frequency range to the total spectral energy is calculated, and the distance domain with the highest ratio is taken as the distance domain of the sensing target.

[0011] Furthermore, determining the dynamic and static state of the target under test based on the signal spectrum includes:

[0012] Perform a 2D FFT on multiple chirps within a frame of the signal spectrum to obtain a distance-Doppler map;

[0013] Peaks with non-zero velocity are detected on the distance-Doppler plot. If the energy of a peak with non-zero velocity exceeds a preset threshold, it is considered dynamic; otherwise, it is considered static.

[0014] Furthermore, the range beamforming of the signal within the range domain of the perceived target includes:

[0015] Digital beamforming is used to obtain the range spectrum of signals with different starting frequencies within the range domain of the perceived target. The distance corresponding to the highest value of the range spectrum is the specific distance of the target.

[0016] A steering vector is determined to indicate the specific distance to the target. Based on this steering vector, the multi-frequency signals are aligned to obtain the target signal with enhanced signal-to-noise ratio.

[0017] Furthermore, the calculation of the respiratory rate includes:

[0018] The change of the target signal with enhanced signal-to-noise ratio over slow time reflects the target's human vital signs parameters. Accumulating human vital signs parameters over a time window and performing FFT yields a complex spectrum.

[0019] The complex spectrum contains the corresponding spectral peaks of the respiratory rate. The maximum spectral peak is found within the preset spectral range to obtain the target respiratory rate.

[0020] Furthermore, the method for obtaining the spectrum of the differential complex signal includes:

[0021] The target signal with enhanced signal-to-noise ratio is subjected to first-order difference to obtain a complex difference signal;

[0022] The spectrum of the differential complex signal is obtained by performing a Fast Fourier Transform on the obtained complex differential signal.

[0023] Furthermore, the target's heart rate is calculated using the following method:

[0024] Define a frequency search range, find all peaks within the frequency search range, remove the peaks corresponding to the harmonics of breathing, and the remaining peaks are candidate peaks for heartbeats;

[0025] Templates are generated one by one based on the frequency of the candidate peaks, the templates are matched with the frequencies, and the degree of matching is confirmed.

[0026] The candidate peak with the highest matching degree is selected as the heartbeat frequency.

[0027] A radar-based non-contact human vital sign sensing system includes: a signal acquisition and processing module, which acquires radar echo signals including those of the target, obtains intermediate frequency signals after passing through a low-pass filter, processes the intermediate frequency signals to obtain the signal spectrum, and selects the range domain with the highest dynamic energy as the range domain of the sensing target; a signal-to-noise ratio enhancement processing module, which determines the dynamic or static state of the target based on the signal spectrum, and if it is dynamic, re-acquires the signal, and if it is static, performs range beamforming on the signal in the range domain of the sensing target to enhance the signal-to-noise ratio of the target signal; a first calculation module, which performs a fast Fourier transform on the target signal to obtain the spectrum of the complex signal and calculates the respiratory rate; and a second calculation module, which sequentially performs a first-order difference and a fast Fourier transform on the target signal to obtain the spectrum of the differential complex signal, and calculates the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0028] A computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0029] A computing device includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0030] The present invention has the following advantages due to the adoption of the above technical solutions:

[0031] 1. This invention enables long-term stable monitoring of human vital signs without requiring the human body to carry any sensors.

[0032] 2. This invention uses distance-dimensional beamforming and traditional angle beamforming techniques to expand the detection range of human vital signs.

[0033] 3. This invention does not require the use of phase signals and directly analyzes the spectrum of complex signals, thus eliminating the need for static elimination and improving the accuracy of human vital sign detection. Attached Figure Description

[0034] Figure 1 This is a flowchart of a breathing and heartbeat detection method based on 60G band millimeter-wave radar according to an embodiment of the present invention;

[0035] Figure 2This is the frequency response diagram for the differential operation. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0037] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] To address the issues of low signal-to-noise ratio (SNR) and undetectable heartbeats due to the difficulty in eliminating static signals when radar is at a distance from the target, this invention provides a non-contact radar-based method and system for sensing human vital signs. The method includes: acquiring intermediate frequency (IF) signals; calculating the target's range domain; performing motion / static detection; improving the SNR by using multi-frequency signals for range-dimensional beamforming and multi-antenna beamforming for angular-dimensional beamforming; detecting respiration using the enhanced complex signal; performing first-order differential analysis on the enhanced complex signal; and detecting heartbeats using the first-order differential complex signal. This invention can sample and superimpose signals from different frequency and spatial dimensions, thereby significantly improving the SNR and expanding the sensing range. Furthermore, it directly analyzes the complex signal rather than the phase, eliminating the need for static elimination and improving the accuracy of respiration and heartbeat detection.

[0039] In one embodiment of the present invention, a non-contact human vital sign sensing method based on radar is provided. This method expands the sensing range by combining range-dimensional beamforming with traditional angle beamforming to improve the signal-to-noise ratio. It bypasses the static signal elimination problem by directly performing spectral analysis based on complex signals instead of phase signals, thus improving the system's accuracy. In this embodiment, respiratory and heartbeat detection based on 60GHz millimeter-wave radar is taken as an example. Figure 1 As shown, the method includes the following steps:

[0040] 1) Collect radar echo signals including the target, obtain intermediate frequency signals after passing through a low-pass filter, process the intermediate frequency signals to obtain the signal spectrum, and select the range domain with the largest dynamic energy as the range domain for sensing the target.

[0041] 2) Determine the dynamic or static state of the target based on the signal spectrum. If it is dynamic, reacquire the signal. If it is static, perform range beamforming on the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal.

[0042] 3) Perform a fast Fourier transform on the target signal to obtain the spectrum of the complex signal, and calculate the respiratory rate;

[0043] 4) Perform first-order differential and fast Fourier transform on the target signal in sequence to obtain the spectrum of the differential complex signal. Calculate the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0044] In step 1) above, the radar transmits a frequency-modulated continuous wave (FMCW) signal into space. The signal is reflected back to the radar receiver after being reflected by the target human body. The received signal is then mixed with the transmitted signal and passed through a low-pass filter to obtain an intermediate frequency (IF) signal. The IF signal is then sampled to obtain the sampled signal required for subsequent processing. This sampled signal is a multi-frequency signal.

[0045] In this embodiment, to obtain multi-frequency signals, the signal parameters need to be configured during signal transmission so that adjacent chirps have different starting frequencies. The configuration method varies depending on the radar used. Assuming there is only one reflector in space, the mathematical expression for the transmitted signal of a chirp is:

[0046]

[0047] Among them, f c Let be the starting frequency of the signal, and k be the slope of the frequency change over time. The received signal is a delay of the transmitted signal, and the delay time is related to the distance to the target. By mixing the received signal with the transmitted signal and passing it through a low-pass filter, the intermediate frequency (IF) signal can be obtained. Since the signal modulation method is linear frequency modulated continuous wave, the frequency of the IF signal is the frequency difference between the transmitted and received signals. The frequency difference has a linear relationship with the time delay, and since the time delay has a linear relationship with the distance to the target, the frequency of the IF signal reflects the distance to the target.

[0048] In step 1) above, it is known that the frequency of the intermediate frequency signal reflects the distance of the target. Therefore, in order to calculate the distance of the target, it is first necessary to convert the signal from the time domain to the frequency domain.

[0049] In this embodiment, the range domain with the highest dynamic energy is selected as the range domain of the sensing target, including the following steps:

[0050] 1.1) Perform a fast Fourier transform on the intermediate frequency signal to obtain the signal spectrum, with each frequency corresponding to a distance;

[0051] 1.2) The results of the slow-time chirp Fourier transforms are concatenated to obtain a distance-time graph, with the vertical axis representing distance and the horizontal axis representing time.

[0052] 1.3) Determine the distance to the breathing target based on the distance-time diagram; calculate the ratio of the energy of the signal in each distance domain changing with slow time within the preset frequency range to the total spectral energy, and take the distance domain with the highest ratio as the distance domain of the sensing target;

[0053] Preferably, the preset frequency range is 0.1Hz to 0.5Hz.

[0054] Specifically, assuming the target is stationary (meaning no limb movement), the received signal only contains the target signal and noise. The objective of this embodiment is to determine the range domain containing the target. This range domain includes both the target signal and noise, while the remaining range domains contain only Gaussian white noise. The energy of Gaussian white noise is uniformly distributed in the frequency domain, while the energy of the breathing signal is distributed between 0.1Hz and 0.5Hz. Therefore, the ratio of the energy of the signal in each range domain that changes slowly between 0.1Hz and 0.5Hz to the total spectral energy is calculated. The range domain with the highest ratio is the target range domain.

[0055] In step 2) above, determining the dynamic and static state of the target under test based on the signal spectrum includes the following steps:

[0056] 2.1.1) Perform 2D FFT on multiple chirps within a frame of the signal spectrum to obtain the distance-Doppler map;

[0057] 2.1.2) Detect peaks with non-zero velocity on the distance-Doppler plot. If the energy of a peak with non-zero velocity exceeds a preset threshold, it is considered dynamic; otherwise, it is considered static.

[0058] In step 2) above, beamforming is performed to enhance the signal-to-noise ratio of the target signal. Since adjacent chirps with different starting frequencies are considered to be emitted simultaneously due to the short time interval relative to the time scale of breathing and heartbeats, and different frequency signals are detecting the same target, the phase difference between the signals is determined by the distance to the target, mathematically expressed as follows:

[0059]

[0060] Since the phase difference is caused by distance, the energy can be focused at a specific distance after the phase is filled in.

[0061] In this embodiment, the specific distance to the target needs to be calculated first, and then phase alignment is performed based on that distance.

[0062] Specifically, range beamforming is performed on the signal within the range domain of the perceived target, including the following steps:

[0063] 2.2.1) Digital beamforming is performed on signals with different initial frequencies within the range domain of the perceived target to obtain the range spectrum. The distance corresponding to the highest value of the range spectrum is the specific distance of the target.

[0064] The method for obtaining the distance spectrum is as follows:

[0065] Assuming n signals of different frequencies are configured, the signal vector s(t) is:

[0066] s(t)=[s1,s2,…,s n ] T

[0067] The guiding vector a(d) corresponding to the distance d is:

[0068]

[0069] Where Δf is the frequency difference between signals. Assume the search range is [d1, d2, ..., d...]. m If there are m guiding vectors, then there are m guiding vectors, which can be represented by a matrix A:

[0070] A = [a(d1), a(d2), ..., a(d...] m )] T

[0071] Finally, multiplying matrix A by the signal vector yields the distance spectrum:

[0072] spectrum = A*s(t)

[0073] 2.2.2) Determine the steering vector for the specific distance to the target, and align the multi-frequency signals according to the steering vector to obtain the target signal with enhanced signal-to-noise ratio;

[0074] Assume the specific distance to the target is d. l The corresponding guiding vector is a(d l Based on this steering vector, multi-frequency signals can be aligned, and the alignment method is as follows:

[0075] S=a(d l ) T *s(t)

[0076] Where S is the target signal with enhanced signal-to-noise ratio, which can be used for subsequent processing.

[0077] This step can be combined with traditional angle beamforming to further improve the perceived signal-to-noise ratio.

[0078] Step 3) above, the calculation of respiratory rate, includes the following steps:

[0079] 3.1) The change of the target signal with slow time after the signal-to-noise ratio enhancement reflects the target's human vital signs parameters. Accumulate the human vital signs parameters of a time window and perform FFT to obtain the complex spectrum.

[0080] Specifically, the method for obtaining the complex spectrum is as follows:

[0081] The target signal S with enhanced signal-to-noise ratio is obtained. The change of this signal over slow time reflects the target's vital signs parameters, denoted as S(t). In this embodiment, the phase is not calculated; instead, an FFT is performed on S(t) accumulated over a time window to obtain the complex spectrum. The expression for S(t) is as follows:

[0082]

[0083] Right now

[0084]

[0085] Where, d br The vibration of the chest cavity caused by breathing, f br d represents the frequency of breathing. h The vibration of the chest cavity caused by the heartbeat, f h Let S(t) be the frequency of the heartbeat, then using a Bessel function of the first kind, we can express S(t) as:

[0086]

[0087] Where, ω br ω is the angular frequency of breathing. h This is the angular frequency of the heartbeat.

[0088] As can be seen, the signal contains the frequency of breathing, the frequency of heartbeat, and the signal in which the harmonics of breathing and the harmonics of heartbeat are modulated each other.

[0089] 3.2) The complex spectrum contains the corresponding spectral peaks of the respiratory rate. The maximum spectral peak is found within a preset spectral range to obtain the target respiratory rate f. br .

[0090] Preferably, the preset frequency range is 0.1Hz to 0.5Hz.

[0091] In step 4) above, the method for obtaining the spectrum of the differential complex signal includes the following steps:

[0092] 4.1.1) Perform first-order difference on the target signal after signal-to-noise ratio enhancement to obtain a complex differential signal;

[0093] Among them, the complex difference signal S after the first-order difference diff (t) is:

[0094] S diff (t)=S(t)-S(t-1)

[0095] The purpose of this differential calculation is to suppress the energy of respiration and enhance the energy of the heartbeat, thus facilitating heartbeat detection. This is because the spectral distribution of the heartbeat is relatively higher than that of respiration, and the differential calculation acts like a high-pass filter, with a frequency response as follows: Figure 2 As shown.

[0096] 4.1.2) For the obtained complex differential signal S diff (t) is subjected to Fast Fourier Transform to obtain the spectrum of the differential complex signal.

[0097] In step 4) above, the target's heart rate is calculated using the following steps:

[0098] 4.2.1) Determine a frequency search range, find all peaks within the frequency search range, remove the peaks corresponding to the harmonics of respiration, and the remaining peaks are candidate peaks of heartbeat;

[0099] For example, in [2.5·f br 6.5·f br ], such as f br When the frequency is 16 Hz / min, the search range is [40, 104]. All peaks are found within the search range, and then the peaks corresponding to the harmonics of respiration are removed. The remaining peaks are candidate peaks for heartbeat.

[0100] 4.2.2) Generate templates one by one according to the frequency of the candidate peaks, match the templates with the frequencies, and confirm the degree of matching;

[0101] Specifically, the method for confirming the degree of matching is: template [f h ,f h -f br ,f h +f br ,f h +2·f br The sum of the energy values ​​of these frequencies in the spectrum represents the degree of matching; the larger the sum, the higher the degree of matching.

[0102] 4.2.3) Select the candidate peak with the highest matching degree as the heartbeat frequency f. h .

[0103] In one embodiment of the present invention, a radar-based non-contact human vital signs sensing system is provided, comprising:

[0104] The signal acquisition and processing module acquires radar echo signals including those of the target, obtains intermediate frequency signals after passing through a low-pass filter, processes the intermediate frequency signals to obtain the signal spectrum, and selects the range domain with the largest dynamic energy as the range domain for sensing the target.

[0105] The signal-to-noise ratio enhancement processing module determines the dynamic or static state of the target under test based on the signal spectrum. If it is dynamic, the signal is reacquired; if it is static, range beamforming is applied to the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal.

[0106] The first calculation module performs a fast Fourier transform on the target signal to obtain the spectrum of the complex signal, and calculates the respiratory rate.

[0107] The second calculation module performs first-order difference and fast Fourier transform on the target signal in sequence to obtain the spectrum of the differential complex signal, and calculates the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0108] In the above embodiments, in the signal acquisition and processing module, selecting the range domain with the highest dynamic energy as the range domain of the sensing target includes:

[0109] The signal spectrum is obtained by performing a fast Fourier transform on the intermediate frequency signal, with each frequency corresponding to a distance.

[0110] By concatenating the results of the slow-time chirp Fourier transforms, a distance-time graph is obtained, with the vertical axis representing distance and the horizontal axis representing time.

[0111] The distance to the breathing target is determined based on the distance-time diagram; the ratio of the energy of the signal in each distance domain changing with slow time within a preset frequency range to the total spectral energy is calculated, and the distance domain with the highest ratio is taken as the distance domain of the sensing target.

[0112] In the above embodiments, the signal-to-noise ratio enhancement processing module determines the dynamic and static state of the target under test based on the signal spectrum, including:

[0113] Perform a 2D FFT on multiple chirps within a frame of the signal spectrum to obtain a distance-Doppler map;

[0114] Peaks with non-zero velocity are detected on the distance-Doppler plot. If the energy of a peak with non-zero velocity exceeds a preset threshold, it is considered dynamic; otherwise, it is considered static.

[0115] In the above embodiments, the signal-to-noise ratio enhancement processing module performs range beamforming on the signal in the range domain of the perceived target, including:

[0116] Digital beamforming is used to obtain the range spectrum of signals with different starting frequencies within the range domain of the perceived target. The distance corresponding to the highest value of the range spectrum is the specific distance of the target.

[0117] A steering vector is determined to indicate the specific distance to the target. Based on this steering vector, the multi-frequency signals are aligned to obtain the target signal with enhanced signal-to-noise ratio.

[0118] In the above embodiments, the calculation of the respiratory rate in the first calculation module includes:

[0119] The change of the target signal with enhanced signal-to-noise ratio over slow time reflects the target's human vital signs parameters. Accumulating human vital signs parameters over a time window and performing FFT yields a complex spectrum.

[0120] The complex spectrum contains the corresponding spectral peaks of the respiratory rate. The maximum spectral peak is found within the preset spectral range to obtain the target respiratory rate.

[0121] In the above embodiments, the method for obtaining the spectrum of the differential complex signal in the second calculation module includes:

[0122] The target signal with enhanced signal-to-noise ratio is subjected to first-order difference to obtain a complex difference signal;

[0123] The spectrum of the differential complex signal is obtained by performing a Fast Fourier Transform on the obtained complex differential signal.

[0124] In the above embodiments, the target's heart rate is calculated in the second calculation module using the following method:

[0125] Define a frequency search range, find all peaks within the frequency search range, remove the peaks corresponding to the harmonics of breathing, and the remaining peaks are candidate peaks for heartbeats;

[0126] Templates are generated one by one based on the frequency of the candidate peaks, the templates are matched with the frequencies, and the degree of matching is confirmed.

[0127] The candidate peak with the highest matching degree is selected as the heartbeat frequency.

[0128] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0129] In one embodiment of the present invention, a computing device structure is provided. This computing device can be a terminal, which may include: a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. When executed by the processor, the computer program implements a radar-based non-contact human vital sign sensing method. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions in memory to execute the following methods: acquire radar echo signals including the target, obtain intermediate frequency signals after passing through a low-pass filter, process the intermediate frequency signals to obtain the signal spectrum, and select the range domain with the highest dynamic energy as the range domain for sensing the target; determine the dynamic or static state of the target based on the signal spectrum, if it is dynamic, reacquire the signal, if it is static, perform range beamforming on the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal; perform a fast Fourier transform on the target signal to obtain the spectrum of the complex signal, and calculate the respiratory rate; perform a first-order difference and fast Fourier transform on the target signal in sequence to obtain the spectrum of the differential complex signal, and calculate the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0130] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] Those skilled in the art will understand that the structure of the above-described computing device is only a partial structure related to the solution of this application and does not constitute a limitation on the computing device to which the solution of this application is applied. A specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0132] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided in the above-described method embodiments, for example including: acquiring radar echo signals including a target, obtaining an intermediate frequency signal after passing through a low-pass filter, processing the intermediate frequency signal to obtain a signal spectrum, selecting the range domain with the largest dynamic energy as the range domain of the target; determining the dynamic or static state of the target based on the signal spectrum, if it is dynamic, re-acquiring the signal, if it is static, performing range beamforming on the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal; performing a fast Fourier transform on the target signal to obtain the spectrum of a complex signal, and calculating the respiratory rate; performing a first-order difference and a fast Fourier transform on the target signal sequentially to obtain the spectrum of a differential complex signal, and calculating the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0133] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to execute the methods provided in the above embodiments, including, for example,: acquiring radar echo signals including a target, obtaining an intermediate frequency signal after passing through a low-pass filter, processing the intermediate frequency signal to obtain a signal spectrum, and selecting the range domain with the highest dynamic energy as the range domain of the target; determining the dynamic or static state of the target based on the signal spectrum, and if it is dynamic, re-acquiring the signal, and if it is static, performing range beamforming on the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal; performing a fast Fourier transform on the target signal to obtain the spectrum of a complex signal, and calculating the respiratory rate; performing a first-order difference and a fast Fourier transform on the target signal sequentially to obtain the spectrum of a differential complex signal, and calculating the target's heart rate based on the spectrum of the differential complex signal and the respiratory rate.

[0134] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A radar-based non-contact human vital sign perception method, characterized in that, include: The radar echo signal including the target is collected, and the intermediate frequency signal is obtained after passing through a low-pass filter. The intermediate frequency signal is processed to obtain the signal spectrum, and the range domain with the largest dynamic energy is selected as the range domain for sensing the target. The dynamic or static state of the target under test is determined based on the signal spectrum. If it is dynamic, the signal is reacquired. If it is static, range beamforming is applied to the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal. The target signal is subjected to a fast Fourier transform to obtain the spectrum of the complex signal, and the respiratory rate is calculated. The target signal is subjected to first-order difference and fast Fourier transform sequentially to obtain the spectrum of the differential complex signal. The target's heart rate is calculated based on the spectrum of the differential complex signal and the respiratory rate. The calculation method includes: determining a frequency search range, finding all peaks within the frequency search range, removing peaks corresponding to respiratory harmonics, and the remaining peaks being candidate peaks for heartbeat; generating templates one by one based on the frequency of the candidate peaks, matching the templates with the frequencies, and confirming the degree of matching. The candidate peak with the highest matching degree is selected as the heartbeat frequency.

2. The radar-based non-contact human vital sign sensing method as described in claim 1, characterized in that, The selection of the range domain with the highest dynamic energy as the range domain of the sensing target includes: The signal spectrum is obtained by performing a fast Fourier transform on the intermediate frequency signal, with each frequency corresponding to a distance. By concatenating the results of the slow-time chirp Fourier transforms, a distance-time graph is obtained, with the vertical axis representing distance and the horizontal axis representing time. The distance to the breathing target is determined based on the distance-time diagram; the ratio of the energy of the signal in each distance domain changing with slow time within a preset frequency range to the total spectral energy is calculated, and the distance domain with the highest ratio is taken as the distance domain of the sensing target.

3. The radar-based non-contact human vital sign sensing method as described in claim 1, characterized in that, The process of determining the dynamic and static state of the target under test based on the signal spectrum includes: Perform a 2D FFT on multiple chirps within a frame of the signal spectrum to obtain a distance-Doppler map; Peaks with non-zero velocity are detected on the distance-Doppler plot. If the energy of a peak with non-zero velocity exceeds a preset threshold, it is considered dynamic; otherwise, it is considered static.

4. The radar-based non-contact human vital sign sensing method as described in claim 1, characterized in that, The range beamforming of the signal within the range domain of the perceived target includes: Digital beamforming is used to obtain the range spectrum of signals with different starting frequencies within the range domain of the perceived target. The distance corresponding to the highest value of the range spectrum is the specific distance of the target. A steering vector is determined to indicate the specific distance to the target. Based on this steering vector, the multi-frequency signals are aligned to obtain the target signal with enhanced signal-to-noise ratio.

5. The radar-based non-contact human vital sign sensing method as described in claim 1, characterized in that, The calculation of the respiratory rate includes: The change of the target signal with enhanced signal-to-noise ratio over slow time reflects the target's human vital signs parameters. Accumulating human vital signs parameters over a time window and performing FFT yields a complex spectrum. The complex spectrum contains the corresponding spectral peaks of the respiratory rate. The maximum spectral peak is found within the preset spectral range to obtain the target respiratory rate.

6. The radar-based non-contact human vital sign sensing method as described in claim 1, characterized in that, The method for obtaining the spectrum of the differential complex signal includes: The target signal with enhanced signal-to-noise ratio is subjected to first-order difference to obtain a complex difference signal; The spectrum of the differential complex signal is obtained by performing a Fast Fourier Transform on the obtained complex differential signal.

7. A radar-based non-contact human vital signs sensing system, characterized in that, include: The signal acquisition and processing module acquires radar echo signals including those of the target, obtains intermediate frequency signals after passing through a low-pass filter, processes the intermediate frequency signals to obtain the signal spectrum, and selects the range domain with the largest dynamic energy as the range domain for sensing the target. The signal-to-noise ratio enhancement processing module determines the dynamic or static state of the target under test based on the signal spectrum. If it is dynamic, the signal is reacquired; if it is static, range beamforming is applied to the signal in the range domain of the target to enhance the signal-to-noise ratio of the target signal. The first calculation module performs a fast Fourier transform on the target signal to obtain the spectrum of the complex signal, and calculates the respiratory rate. The second calculation module sequentially performs first-order difference and fast Fourier transform on the target signal to obtain the spectrum of the differential complex signal. Based on the spectrum of the differential complex signal and the respiratory rate, the target's heart rate is calculated. The calculation method includes: determining a frequency search range, finding all peaks within the frequency search range, removing peaks corresponding to respiratory harmonics, and the remaining peaks being candidate peaks for heartbeats; generating templates one by one based on the frequency of the candidate peaks, matching the templates with the frequencies, and confirming the degree of matching. The candidate peak with the highest matching degree is selected as the heartbeat frequency.

8. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 6.

9. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 6.

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