Non-contact vital sign monitoring method and equipment with phase interpolation function

Abstract

The invention discloses a non-contact vital sign monitoring method and device with a phase interpolation function, and the method comprises the steps: transmitting a detection signal based on a radar system, carrying out the collection of environment data, and carrying out the shift register of the environment data in an A memory; acquiring data of which the duration is T from the memory A, and performing correlation operation preprocessing to realize target positioning and phase information sequence data Y extraction; filling a missing part in the phase information sequence data Y by using an interpolation technology based on a Kalman filtering algorithm to obtain filled data IY; and performing phase difference and moving average filtering on the filled data IY to obtain the respiratory rate and / or the heart rate. Compared with the prior art, the monitoring method provided by the invention is more efficient, and accurate monitoring of the vital signs of the human body can be ensured in a non-contact and free distribution state. The method can be widely applied to the technical field of indoor life information monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor life information monitoring, and in particular to a non-contact vital sign monitoring method and device with a phase interpolation function. Background Art

[0002] With the intensification of the global population aging, the health management and nursing needs of the elderly population are becoming increasingly urgent. Against this background, radar technology has gradually become an important tool in the field of vital sign monitoring due to its superior non-contact characteristics. Radar can not only monitor in an interference-free environment, but also enable long-term care, accurate diagnosis, and effective prevention of potential fatal diseases. These advantages have led to the wide application of radar in indoor care environments, especially in scenarios such as hospitals, nursing homes, and home care.

[0003] However, in the practical application of non-contact vital sign monitoring, radar technology also faces some challenges. First, due to the randomness and uncertainty of the target, the human target needs to be automatically located to ensure the accuracy of monitoring. Second, when radar operates in an indoor environment, it may be interfered by other objects, resulting in inaccurate monitoring results. For example, reflected waves generated by furniture, walls, or other human activities may confuse the radar signal, causing missing chest phase data in the acquisition, thereby affecting the extraction and analysis of vital signs. Therefore, improving the automatic positioning ability of radar in a dynamic and complex environment, as well as filling the missing data that appears in the vital sign monitoring process, has become an important research direction. Summary of the Invention

[0004] To solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a non-contact vital sign monitoring method, device, and medium with a phase interpolation function.

[0005] The first technical solution adopted by the present invention is:

[0006] A non-contact vital sign monitoring method with a phase interpolation function, including the following steps:

[0007] Based on a radar system to send a detection signal, environmental data is collected and shift-register stored in an A memory;

[0008] Obtain data with a duration of T from the A memory, perform correlation operation preprocessing to achieve target positioning and extraction of phase information sequence data Y;

[0009] Use an interpolation technique based on the Kalman filter algorithm to fill the missing part in the phase information sequence data Y to obtain the filled data IY;

[0010] Perform phase difference and moving average filtering on the filled data IY to obtain the respiratory rate and / or heart rate.

[0011] Further, the radar system sends a detection signal, collects environmental data, and shift registers it into the A memory, including:

[0012] Send a radar signal to the object to be measured, obtain the radar received signal, mix and sample the transmitted signal and the received signal to obtain a dataset of intermediate frequency signal segments;

[0013] Shift register the obtained dataset of intermediate frequency signal segments into the A memory.

[0014] Further, the data with a duration of T includes a first data segment and a second data segment, and the first data segment and the second data segment are a set of continuous data segments; the duration of the first data segment is T1, the duration of the second data segment is T2, and T = T1 + T2;

[0015] Obtain the data with a duration of T from the A memory, perform correlation operation preprocessing to achieve target positioning and extraction of the phase information sequence data Y, including:

[0016] Extract the first data segment from the A memory for correlation operation preprocessing to generate a distance-angle heat map;

[0017] Determine whether there is a target according to the energy magnitude of the highlighted part in the distance-angle heat map;

[0018] If there is a target, determine the position of the target distribution, and extract the phase information sequence data Y of the target from the second data segment to obtain the phase data of the target thoracic cavity change.

[0019] Further, the first data segment and the second data segment are obtained in the following manner:

[0020] Extract the signals in the first register address segment and the second register address segment from the A memory as the first data segment and the second data segment; wherein, the first register address segment is Addr~[(Addr + AT1) - 1], and the second register address segment is [(Addr + AT1) - 1]~{[(Addr + AT1) - 1] + AT2 - 1}, where Addr is the starting address, the time length corresponding to AT1 is T1, and the time length corresponding to AT2 is T2;

[0021] After extraction, perform translation with a set step size. Each time a step is translated, extract the data in the register address segment again until the translation amount reaches the upper limit;

[0022] If the target is not detected continuously, increase T1 (i.e., AT1) to expand the range of the first data segment.

[0023] Further, determining whether there is a target according to the energy magnitude of the highlighted part in the distance-angle heat map includes:

[0024] Calculate and obtain the variance threshold of each sampling point in each frame through the 2D-CFAR algorithm;

[0025] When the data of the hot spot in the distance-angle heat map exceeds the calculated variance threshold, it is determined that the hot spot is a target; when the data of the hot spot in the distance-angle heat map is lower than the calculated variance threshold, the hot spot is not recognized as a target and is translated to the next storage space of the A memory with a specific step size.

[0026] Further, filling the missing part in the phase information sequence data Y by using the interpolation technology based on the Kalman filtering algorithm to obtain the filled data IY includes:

[0027] Extract the phase information sequence data Y, that is, the observed data that changes with time in the signal, and some of the observed data are missing values;

[0028] Define a state space model to perform state prediction and update on the phase information sequence data Y, and use the Kalman filter to fill the missing data;

[0029] Output the complete observed data IY after interpolation processing.

[0030] Further, defining a state space model to perform state prediction and update on the phase information sequence data Y includes:

[0031] Determine the state transition matrix and the observation matrix to accurately characterize the dynamic evolution law and observation characteristics of the phase information sequence data Y;

[0032] Initialize the state vector and its covariance matrix as the starting conditions of the Kalman filtering algorithm;

[0033] Predict the state value at the current moment according to the state estimate at the previous moment, and calculate the corresponding predicted covariance matrix;

[0034] Combine the phase information sequence data Y at the current moment, and correct the predicted state through the Kalman gain to obtain the updated optimal state estimate value;

[0035] Iteratively execute the above process to fill the missing part of the phase information sequence data Y.

[0036] Further, the phase information sequence data Y is a composite signal of a respiration signal and a heartbeat signal, or is a separated respiration signal and heartbeat signal through appropriate processing.

[0037] Further, the process of performing phase difference and moving average filtering on the filled data IY to obtain the respiration rate and / or heart rate includes:

[0038] Performing a phase difference operation on the filled data IY, identifying and extracting the periodic characteristics in the signal by calculating the phase change values between adjacent data points;

[0039] Using a moving average filter to smooth the phase difference result;

[0040] Performing a fast Fourier transform on the smoothed signal, converting the time-domain signal into a frequency-domain signal, and extracting the respiration frequency and / or heart rate eigenvalue through frequency-domain analysis.

[0041] Further, the target positioning and radar types are diverse, specifically including multiple positioning methods such as time difference positioning, phase difference positioning, multipath positioning, and clustering positioning, as well as multiple radar types such as pulsed radar, continuous wave radar, and phased array radar.

[0042] The second technical solution adopted by the present invention is:

[0043] An electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement a non-contact vital sign monitoring method with a phase interpolation function as described above.

[0044] The third technical solution adopted by the present invention is:

[0045] A computer-readable storage medium, in which at least one instruction, at least one program, a code set, or an instruction set is stored. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement a non-contact vital sign monitoring method with a phase interpolation function as described above.

[0046] The fourth technical solution adopted by the present invention is:

[0047] A computer program product or computer program, the computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device may read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to execute the above non-contact vital sign monitoring method with a phase interpolation function.

[0048] The beneficial effects of the present invention are as follows: In the aspect of indoor vital sign monitoring and processing, the present invention adopts a memory shift register and data shift extraction technology, significantly improving the processing speed. In addition, the present invention introduces a method based on the Kalman filter algorithm to fill in the missing data in the chest cavity signal, making the phase information of the target monitoring more complete, thereby achieving precise monitoring. These improvements effectively meet the requirements of the radar vital sign monitoring method in terms of high precision and processing speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings in the following introduction are only for conveniently and clearly presenting some embodiments of the technical solutions in the present invention. For those skilled in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0050] Figure 1 is a flowchart of a non-contact vital sign monitoring method with a phase interpolation function in an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of the transmission and reception signal models corresponding to each chirp in a radar in an embodiment of the present invention;

[0052] Figure 3 is a schematic diagram showing the successive entry of the first data segment and the second data segment signals into the memory in an embodiment of the present invention;

[0053] Figure 4 is a flowchart of the dynamic target uninterrupted detection and the acquisition of phase information sequence data in an embodiment of the present invention;

[0054] Figure 5 is a flowchart of the Kalman filter algorithm for target vital sign monitoring in an embodiment of the present invention;

[0055] Figure 6 is a schematic structural diagram of a non-contact vital sign monitoring device with a phase interpolation function in an embodiment of the present invention;

[0056] Figure 7It is a schematic diagram of the application of the non-contact vital sign monitoring device with a phase interpolation function in an embodiment of the present invention. Detailed implementation manners

[0057] The embodiments of the present application are described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and should not be construed as a limitation to the present application. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0058] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application. The singular forms "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise clearly defined, words such as "set", "installed", "connected" should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0059] In the description of the present application, it should be understood that for the orientation description, such as the up, down, front, back, left, right, etc. indicating the orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0060] In the description of the present application, the meaning of "a number of" is one or more, the meaning of "a plurality of" is two or more, and understandings such as "greater than", "less than", "exceeding" do not include the recited number, and understandings such as "above", "below", "within" include the recited number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and should not be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features.

[0061] In the description of the present application, "and / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the front and rear associated objects.

[0062] Term explanation:

[0063] 2D-CFAR algorithm: 2D-CFAR is a method for adaptive target detection on two-dimensional data. By selecting training cells around the target point in a two-dimensional coordinate system (such as the XY plane) to estimate the background noise, using guard cells to avoid the influence of interfering target energy on the noise estimation, and then calculating the detection threshold according to the set false alarm probability to determine whether the signal strength of the target point exceeds the threshold, so as to achieve robust detection of the target in a complex background.

[0064] chirp: In a radar system, it is a signal whose frequency varies linearly with time. A simple linear frequency modulation chirp signal can be expressed as: s(t) = Acos(2πf0t + πSt 2 ), where A is the signal amplitude, f0 is the starting frequency, S is the frequency modulation slope, and t is the time.

[0065] Embodiment 1

[0066] As Figure 1 described, this embodiment provides a non-contact vital sign monitoring method with a phase interpolation function, including the following steps:

[0067] S1. Based on the radar system, send a detection signal, collect environmental data, and shift-register it into the A memory.

[0068] In one embodiment, step S1 includes the following steps S11-S12:

[0069] S11. Transmit a radar signal to the object to be measured, obtain the radar received signal, mix and sample the transmitted signal and the received signal to obtain a dataset of intermediate frequency signal segments.

[0070] Further as an optional implementation, as Figure 2 (a) shows, the transmitted signal and the received signal are frequency-modulated continuous signals chirp, and the frequency of chirp increases linearly with time. One chirp period is the duration of the signal from the starting frequency to the cut-off frequency. There will be idle time between chirps. One frame period contains multiple chirp periods. From the start of the first chirp to the cut-off, then followed by the start of the second chirp to the cut-off, and finally the start of the last chirp to the cut-off. One frame period is at least greater than two chirp periods.

[0071] Among them, as Figure 2 (b) shows, the intermediate frequency signal obtained by mixing is sampled, that is, the multi-antenna multi-frame multi-chirp signal dataset G1, which is a three-dimensional dataset of F*M*N, where F represents the frame, M represents the number of chirps included in each frame, and N represents the number of sampling points included in each chirp.

[0072] S12. Shift and store the obtained intermediate frequency signal segment data set into memory A, as Figure 3 shown.

[0073] S2. Obtain data with a duration of T from memory A, and perform correlation operation preprocessing to achieve target positioning and extract phase information sequence data Y.

[0074] In this embodiment, the data with a duration of T includes a first data segment and a second data segment. Each time the first data segment and the second data segment are extracted, they are a group of continuous data segments; the duration of the first data segment is T1, and the duration of the second data segment is T2, where T = T1 + T2; the first data segment and the second data segment are translated by a set step size and continue to be extracted until the maximum translation amount is reached.

[0075] Exemplarily, the specific steps of dynamic target continuous detection and phase information sequence data acquisition are as Figure 4 shown. Step S2 includes the following steps S21 - S23:

[0076] S21. Extract the first data segment from the memory A and perform correlation operation preprocessing, including processing such as vector cancellation algorithm, variance processing, Fourier transform, etc., to generate a distance - angle heat map.

[0077] S22. Determine whether there is a target according to the energy size of the highlighted part in the distance - angle heat map.

[0078] Specifically, perform 2D - CFAR calculation according to the variance data to obtain the variance threshold of each sampling point in each frame.

[0079] When the data of the hot spot in the distance - angle heat map exceeds the threshold calculated by 2D - CFAR, it is determined that the hot spot is a target; when the data of the hot spot in the distance - angle heat map is lower than the threshold calculated by 2D - CFAR, the hot spot is not recognized as a target and is translated to the next storage space in memory A with a specific step size.

[0080] S23. When there is a target, determine the position of the target distribution, extract the phase information sequence data Y of the target from the second data segment, and obtain the phase data of the target thoracic cavity change.

[0081] As an alternative embodiment, the step of extracting the first data segment and the second data segment from the memory A specifically includes:

[0082] A1. Extract the signals in the first register address segment and the second register address segment from the memory A as the first data segment and the second data segment; where, as Figure 3As shown, the first register address segment is Addr to [(Addr + AT1) - 1], and the second register address segment is [(Addr + AT1) - 1] to {[(Addr + AT1) - 1] + AT2 - 1}, where Addr is the starting address, the time length corresponding to AT1 is T1, and the time length corresponding to AT2 is T2;

[0083] A2. After extraction is completed, translation is performed in set steps. For each translation of one step, the data in the register address segment is extracted again until the translation amount reaches the upper limit;

[0084] A3. If the target is not detected continuously, increase T1 (i.e., AT1) to expand the first data segment range.

[0085] S3. Use the interpolation technique based on the Kalman filter algorithm to fill in the missing parts in the phase information sequence data Y to obtain the filled data IY.

[0086] In one embodiment, step S3 includes the following steps S31 - S33:

[0087] S31. Take out the phase information sequence data Y, that is, the observed data that changes with time in the signal, and some of the observed data are missing values.

[0088] Specifically, the phase information sequence data Y can be a composite signal of a breathing signal and a heartbeat signal, or the separate breathing signal and heartbeat signal can be separated through appropriate processing.

[0089] S32. Define the state space model, perform state prediction and update on the phase information sequence data Y, and use the Kalman filter to fill in the missing data.

[0090] As an alternative implementation, the process of defining the state space model and performing state prediction and update on the phase information sequence data Y is as Figure 5 shown, and the specific steps include:

[0091] B1. Determine the state transition matrix and the observation matrix to accurately characterize the dynamic evolution law and observation characteristics of the phase information sequence data Y;

[0092] B2. Initialize the state vector and its covariance matrix as the starting conditions of the Kalman filter algorithm, including the state covariance matrix, the system process noise covariance matrix, and the observation noise covariance matrix;

[0093] B3. According to the state estimate at the previous moment, predict the state value at the current moment and calculate the corresponding prediction covariance matrix;

[0094] B4. Combine the phase information sequence data Y at the current moment, and correct the predicted state through the Kalman gain to obtain an updated optimal state estimate value, including an optimal phase estimation matrix and an optimal covariance matrix;

[0095] B5. Iteratively execute the above steps B1 - B4 to fill in the missing part of the phase information sequence data Y.

[0096] S33. Output the complete observed data IY after interpolation processing.

[0097] S4. Perform phase difference and moving average filtering on the filled data IY to obtain the respiratory rate and / or heart rate.

[0098] In some embodiments, step S4 specifically includes the following steps:

[0099] S41. Perform a phase difference operation on the filled data IY, and identify and extract the periodic features in the signal by calculating the phase change value between adjacent data points.

[0100] S42. Use a moving average filter to smooth the phase difference result.

[0101] S43. Perform a fast Fourier transform on the smoothed signal to convert the time-domain signal into a frequency-domain signal, and extract the respiratory rate and heart rate characteristic values through frequency-domain analysis.

[0102] Compared with the prior art, the present invention has achieved the following innovative improvements in the field of vital sign monitoring: First, by introducing the memory shift register technology and data shift extraction technology, the signal processing efficiency has been significantly improved, and the system delay has been effectively reduced; Second, the distance-angle heat map technology has been innovatively adopted to realize the automatic positioning and tracking of the target, greatly improving the flexibility and adaptability of monitoring; In addition, aiming at the problem of missing data in the thoracic cavity signal, a filling method based on the Kalman filtering algorithm is proposed to ensure the integrity and continuity of the phase information, thereby realizing more accurate vital sign monitoring. The above technical improvements comprehensively improve the performance of the radar vital sign monitoring method in terms of high precision, real-time performance, and reliability, meeting the monitoring requirements in complex application scenarios.

[0103] Embodiment 2

[0104] As Figure 6 shown, this embodiment provides a non-contact vital sign monitoring device with a phase interpolation function, including:

[0105] A radar antenna acquisition module for acquiring data in front of the radar and converting the data into an intermediate frequency signal for subsequent processing;

[0106] A memory module for updating and storing a dataset of intermediate frequency signals, saving the intermediate acquired dataset, and ultimately the data for display.

[0107] An extraction module for extracting data segments from the memory.

[0108] A decision module for comparing the energy magnitude of the highlighted part in the range-angle heatmap with the threshold calculated by 2D-CFAR to determine whether there is a potential target distribution; when the data of the hotspot in the range-angle heatmap exceeds the threshold calculated by 2D-CFAR, the hotspot is determined as a target; when the data of the hotspot in the range-angle heatmap is lower than the threshold calculated by 2D-CFAR, the hotspot is not recognized as a target.

[0109] A subsequent processing module determines the position of the target distribution, extracts the sequence data Y of the phase information of the target, obtains the phase data of the target thoracic cavity change, and further obtains the respiratory rate and / or heart rate.

[0110] See Figure 7 , the non-contact vital sign monitoring device with a phase interpolation function according to the present invention is respectively connected to a radar antenna array, and the distribution and identification of the target can be displayed through a display device.

[0111] Since this device is a non-contact vital sign monitoring device with a phase interpolation function in an embodiment of the present invention, and the principle of the device to solve problems is similar to the above method, the implementation of this device can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0112] Embodiment 3

[0113] The embodiment of the present invention further provides an electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a non-contact vital sign monitoring method with a phase interpolation function as Figure 1 shown.

[0114] It can be understood that the memory may include a Random Access Memory (RAM) and may also include a Read-Only Memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function, instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store data created according to the use of the server, etc.

[0115] The processor may include one or more processing cores. The processor connects various parts within the entire server using various interfaces and lines, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by calling data stored in the memory, it performs various functions of the server and processes data. Optionally, the processor may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate a combination of one or several of a Central Processing Unit (CPU) and a modem, etc. Among them, the CPU mainly processes the operating system and application programs, etc.; the modem is used for processing wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately through a single chip.

[0116] Since this electronic device is the electronic device corresponding to a non-contact vital sign monitoring method with a phase interpolation function in an embodiment of the present invention, and the principle of how this electronic device solves problems is similar to that of this method, the implementation of this electronic device can refer to the implementation process of the above method embodiment, and repeated parts will not be elaborated.

[0117] Embodiment 4

[0118] An embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one segment of program, code set, or instruction set is stored, and the at least one instruction, the at least one segment of program, the code set, or the instruction set is loaded and executed by a processor to implement Figure 1 a non-contact vital sign monitoring method with a phase interpolation function as shown.

[0119] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium that can be used to carry or store data.

[0120] Since this storage medium is the storage medium corresponding to a non-contact vital sign monitoring method with a phase interpolation function in the embodiments of the present invention, and the principle of solving problems by this storage medium is similar to that of this method, the implementation of this storage medium can refer to the implementation process of the above method embodiments, and the repeated parts will not be elaborated.

[0121] Embodiment 5

[0122] In some possible implementation manners, various aspects of the method in the embodiments of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps of a non-contact vital sign monitoring method with a phase interpolation function according to various exemplary implementation manners described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0123] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] The above embodiments are only for illustrating the technical concept and characteristics of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A non-contact vital sign monitoring method with a phase interpolation function, characterized in that, Including the following steps: Based on the radar system, send a detection signal, collect environmental data, and shift-register it into memory A; Obtain data with a duration of T from memory A, perform correlation operation preprocessing to achieve target positioning and extract phase information sequence data Y; Use interpolation technology based on the Kalman filter algorithm to fill in the missing parts in the phase information sequence data Y to obtain the filled data IY; Perform phase difference and moving average filtering on the filled data IY to obtain the respiratory rate and / or heart rate.

2. The non-contact vital sign monitoring method with a phase interpolation function according to claim 1, wherein The step of based on the radar system sending a detection signal, collecting environmental data, and shift-registering it into memory A includes: Transmit a radar signal to the object to be measured, obtain the radar received signal, mix and sample the transmitted signal and the received signal to obtain a dataset of intermediate frequency signal segments; Shift-register the obtained dataset of intermediate frequency signal segments into memory A.

3. The non-contact vital sign monitoring method with a phase interpolation function according to claim 1, wherein The data with a duration of T includes a first data segment and a second data segment, and the first data segment and the second data segment are a set of continuous data segments; the duration of the first data segment is T1, the duration of the second data segment is T2, and T = T1 + T2; The step of obtaining data with a duration of T from memory A, performing correlation operation preprocessing to achieve target positioning and extract phase information sequence data Y includes: Extract the first data segment from the memory A for correlation operation preprocessing to generate a distance-angle heat map; Determine whether there is a target according to the energy magnitude of the highlighted part in the distance-angle heat map; If there is a target, determine the position of the target distribution, and extract the phase information sequence data Y of the target from the second data segment to obtain the phase data of the target thoracic cavity change.

4. The non-contact vital sign monitoring method with a phase interpolation function according to claim 3, characterized in that, The first data segment and the second data segment are obtained in the following manner: Extract the signals in the first register address segment and the second register address segment from memory A as the first data segment and the second data segment; wherein, the first register address segment is Addr~[(Addr + AT1) - 1], the second register address segment is [(Addr + AT1) - 1]~{[(Addr + AT1) - 1] + AT2 - 1}, where Addr is the starting address, the time length corresponding to AT1 is T1, and the time length corresponding to AT2 is T2; After the extraction is completed, perform translation with a set step size. Each time a step is translated, extract the data in the register address segment again until the translation amount reaches the upper limit; If no target is detected continuously, increase T1 to expand the range of the first data segment.

5. The non-contact vital sign monitoring method with a phase interpolation function according to claim 3, characterized in that, The step of determining whether there is a target according to the energy magnitude of the highlighted part in the distance-angle heat map includes: Calculate and obtain the variance threshold of each sampling point in each frame through the 2D-CFAR algorithm; When the data of the hot spot in the distance-angle heat map exceeds the calculated variance threshold, it is determined that the hot spot is a target; when the data of the hot spot in the distance-angle heat map is lower than the calculated variance threshold, the hot spot is not recognized as a target and is translated to the next storage space of memory A with a specific step size.

6. The non-contact vital sign monitoring method with a phase interpolation function according to claim 1, wherein, Interpolating the missing part of the phase information sequence data Y using the interpolation technique based on the Kalman filter algorithm to obtain the filled data IY, including: Taking out the phase information sequence data Y, i.e., the observed data that changes with time in the signal, where some of the observed data are missing values; Defining a state space model to perform state prediction and update on the phase information sequence data Y, and using the Kalman filter to fill in the missing data; Outputting the complete observed data IY after interpolation processing.

7. A non-contact vital sign monitoring method with a phase interpolation function according to claim 6, characterized in that, The defining a state space model to perform state prediction and update on the phase information sequence data Y includes: Determining the state transition matrix and the observation matrix to accurately characterize the dynamic evolution law and observation characteristics of the phase information sequence data Y; Initializing the state vector and its covariance matrix as the starting conditions of the Kalman filter algorithm; Predicting the state value at the current moment based on the state estimate at the previous moment, and calculating the corresponding predicted covariance matrix; Combining the phase information sequence data Y at the current moment, correcting the predicted state through the Kalman gain to obtain the updated optimal state estimate value; Iteratively executing the above process to fill in the missing part of the phase information sequence data Y.

8. A non-contact vital sign monitoring method with a phase interpolation function according to claim 1, characterized in that, The phase information sequence data Y is a composite signal of a respiration signal and a heartbeat signal, or is a separately isolated respiration signal and heartbeat signal through appropriate processing.

9. A non-contact vital sign monitoring method with a phase interpolation function according to claim 1, characterized in that, Performing phase difference and moving average filtering on the filled data IY to obtain the respiration rate and / or heart rate, including: Performing a phase difference operation on the filled data IY, and identifying and extracting the periodic characteristics in the signal by calculating the phase change values between adjacent data points; Using a moving average filter to smooth the phase difference result; Performing a fast Fourier transform on the smoothed signal to convert the time-domain signal into a frequency-domain signal, and extracting the respiration frequency and / or heart rate characteristic values through frequency-domain analysis.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 9.

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