Human body automatic focusing vital sign monitoring method

Abstract

The invention discloses a human body automatic focusing vital sign monitoring method comprising the following steps: sending radar microwave signals to a target human body, receiving three paths of radar microwave echo signals, and respectively extracting frequency domain characteristics of the three paths of radar microwave echo signals; generating human body three-dimensional point cloud data through the frequency domain characteristics of the three paths of radar microwave echo signals, and determining the lying position of a target human body according to the human body three-dimensional point cloud data; obtaining three paths of human body radar microwave echo signals according to the lying position of the target human body, and performing signal reconstruction on the three paths of human body radar microwave echo signals to obtain three paths of reconstructed human body radar microwave signals; and judging whether the target human body is in a resting state or not through the reconstructed three paths of human body radar microwave signals, if so, searching the chest of the target human body, and calculating the heart rate and respiratory rate of the target human body.

Description

Technical Field

[0001] The present invention relates to the technical field of vital sign monitoring, and in particular to a human body automatic focusing vital sign monitoring method. Background Art

[0002] Microwave radar is a non-contact sensing technology that can be used to detect human vital signs, such as breathing rate and heart rate. Generally, the radar transmitter transmits electromagnetic wave signals to the human target. When the electromagnetic wave signals touch the human body, they will be reflected back to the radar receiver. These reflected electromagnetic wave signals will produce a weak frequency shift due to human breathing and other movements. The human breathing rate and heart rate can be obtained by analyzing the phase of these frequency shifts.

[0003] On the one hand, since the amplitude of human breathing and heart rate is relatively weak, non-contact monitoring methods can easily ignore some relatively weak breathing and heart rate signals, which can easily cause inaccurate monitoring data; on the other hand, since this type of monitoring method is generally used in hospitals, nursing homes and other scenarios, the human body is usually lying in bed for non-contact monitoring. The human body will still move when lying down to rest. Vital signs such as breathing and heart rate are monitored through slight movements of the human chest. Human movement will cause the human chest to move, which will result in the collected monitoring data not coming from the human chest, resulting in inaccurate monitoring.

[0004] The purpose of the present invention is to design a human body automatic focusing vital sign monitoring method in view of the above-mentioned problems in the prior art. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a human body automatic focusing vital sign monitoring method that can solve the above-mentioned problems.

[0006] The present invention provides a human body automatic focusing vital sign monitoring method, comprising:

[0007] Send a radar microwave signal to the target human body, receive three radar microwave echo signals, and extract the frequency domain features of the three radar microwave echo signals respectively;

[0008] Generate three-dimensional point cloud data of the human body through the frequency domain characteristics of the three-way radar microwave echo signal, and determine the lying position of the target human body according to the three-dimensional point cloud data of the human body;

[0009] Reconstructing the three-way human body radar microwave echo signals according to the three-way human body radar microwave echo signals obtained at the target human body lying position to obtain the reconstructed three-way human body radar microwave signals;

[0010] The reconstructed three-way human radar microwave signal is used to determine whether the target human body is in a resting state. If so, the target human chest cavity is found and the target human heart rate and respiratory rate are calculated.

[0011] Furthermore, before respectively extracting the frequency domain features of the three-way radar microwave echo signals, the following steps are performed:

[0012] The three radar microwave echo signals are respectively sampled for intermediate frequency signals to obtain three radar microwave digital signals.

[0013] Further, the extracting frequency domain features of the three radar microwave echo signals respectively includes:

[0014] Perform one-dimensional FFT calculation on the three-way radar microwave digital signals to obtain the distance characteristics of the three-way radar microwave digital signals;

[0015] The three-way radar microwave digital signals are subjected to two-dimensional FFT calculation to obtain the velocity characteristics and angle characteristics of the three-way radar microwave digital signals.

[0016] Further, the generating of three-dimensional point cloud data of a human body by frequency domain characteristics of three-way microwave signals comprises:

[0017] Combining the distance feature, speed feature, and angle feature to obtain a three-dimensional frequency domain feature;

[0018] The three-dimensional frequency domain features are mapped to the spatial dimension to obtain the three-dimensional point cloud data of the human body.

[0019] Further, determining the target human body lying position according to the human body three-dimensional point cloud data includes:

[0020] The human body 3D point cloud data is clustered and separated by using DBSCAN algorithm to obtain human body point cloud clusters;

[0021] Analyze the distribution shape of points in the human point cloud cluster to identify human features and obtain a shape similar to the human body;

[0022] By calculating the length, width and direction of a shape similar to the human body, the lying posture and position of the human body can be obtained.

[0023] Further, the signal reconstruction of the three-path human body radar microwave signals to obtain the reconstructed three-path human body radar microwave signals includes:

[0024] Calculate the angle information of the lying position of the human body through the phase difference between the frequency domain characteristics of the first human body radar microwave signal and the frequency domain characteristics of the third human body radar microwave signal;

[0025] Performing steering vector processing on the sampling points of each frame of the frequency domain characteristics of the third radar microwave signal to obtain the steering vector of the third radar microwave signal;

[0026] The steering vector of the third radar microwave signal is multiplied by the frequency domain feature of the third radar microwave signal to obtain the reconstructed three-way human body radar microwave signal.

[0027] Further, judging whether the target human body is in a resting state by reconstructing the three-path human body radar microwave signal includes:

[0028] Perform 2D FFT calculation on the reconstructed three-way human body radar microwave signals to obtain the velocity characteristics of the reconstructed three-way human body radar microwave signals;

[0029] Determine whether the speed characteristics of the reconstructed three-way human body radar microwave signals are greater than a first threshold, if so, the human body is in a moving state, otherwise, perform a static judgment;

[0030] Performing cancellation processing on the reconstructed three-way human body radar microwave signals of the current frame and the previous two frames to obtain a differential signal;

[0031] Perform FFT calculation on the differential signal to obtain the frequency domain characteristics of the differential signal;

[0032] If the frequency domain characteristic of the differential signal is less than the second threshold, the human body is in a resting state.

[0033] Further, the searching for the target human chest cavity includes:

[0034] Perform 1D FFT and 2D FFT calculations on the reconstructed three-way human body radar microwave signals to obtain the frequency domain characteristics of the reconstructed three-way human body radar microwave signals;

[0035] Extract frequency domain features with an index of 10-15 frequency band, find the maximum value and its corresponding index in the frequency domain features with an index of 10-15 frequency band and store them, calculate the mode in the stored frequency domain data as the target frequency index position of the human chest cavity;

[0036] Applying a window function to the frequency domain characteristics of the target frequency index position of the human chest cavity and then performing FFT calculation to obtain the phase information of the target frequency position;

[0037] Unwrapping the phase at the target frequency position, calculating the time difference of the unwrapped phase, and obtaining phase difference data;

[0038] 256 frames of phase difference data are accumulated each time and used as input for calculating the breathing frequency and heart rate of the target person.

[0039] Further, the calculating target human heart rate includes:

[0040] After performing heart rate bandpass filtering on the phase difference data, the wavelet transform is used to perform 4-layer decomposition, and the detail coefficients of the third layer are extracted to reconstruct the high-frequency heart rate signal and the low-frequency heart rate signal;

[0041] Perform 1024-point FFT calculation on the reconstructed high-frequency heart rate signal and low-frequency heart rate signal to obtain the frequency domain characteristics of the reconstructed high-frequency heart rate signal and low-frequency heart rate signal;

[0042] Normalizing the peak values ​​of the frequency domain features of the high-frequency heart rate signal and the low-frequency heart rate signal, and calculating the difference between the maximum peak value and other peak values;

[0043] If the difference is less than the third threshold, the heart rate in the previous time window is output; if the difference is greater than the third threshold, the current heart rate is output through the high-frequency heart rate signal and the low-frequency heart rate signal;

[0044] Based on the historical data of 100 frames of high-frequency heart rate signals and low-frequency heart rate signals, the standard deviation of the historical data is calculated. If the standard deviation of the historical data is less than the fourth threshold, the current heart rate is output. If the standard deviation of the historical data is greater than the fourth threshold, the heart rate estimated based on the high-frequency heart rate signal is output.

[0045] Further, the calculation of the target human respiratory rate includes:

[0046] After the phase difference data is subjected to respiratory frequency bandpass filtering, it is decomposed into 4 layers using wavelet transform, and the detail coefficients of the third layer are extracted to reconstruct the high-frequency respiratory frequency signal and the low-frequency respiratory frequency signal;

[0047] Perform 1024-point FFT calculation on the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal to obtain the frequency domain characteristics of the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal;

[0048] The peak values ​​of the frequency domain features of the high-frequency respiratory frequency signal and the low-frequency respiratory frequency signal are counted to obtain the stage statistical data of the respiratory frequency;

[0049] The reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal are compared with the stage statistical data of the respiratory frequency to obtain a comparison difference. If the comparison difference is less than the fifth threshold, the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal are output; otherwise, the stage statistical data of the respiratory frequency are output.

[0050] Beneficial effects of the present invention:

[0051] First, by combining multiple frequency domain features (distance, speed, angle), it can more accurately construct and identify human features, improve the accuracy of human body recognition, and avoid misdetection of other moving human bodies; through point cloud technology, the three-dimensional shape of the human body can be reconstructed, and human features can be identified and analyzed based on this; through the DBSCAN algorithm, different point cloud clusters can be effectively identified and separated. Analyzing the point distribution shape within the clustered human point cloud cluster is a key step in identifying human features. Using statistical shape analysis, features similar to the human body can be identified from the point cloud shape, such as the position of the head, torso, and limbs, and the human body can be automatically distinguished from other objects. By calculating the length, width, and direction of the point cloud cluster similar to the human shape, the lying posture and position of the human body can be inferred.

[0052] Secondly, by comparing the phase difference of radar signals from different channels, the angle information of the human body's lying position can be accurately calculated. The information provided by the phase difference can help determine the direction of the signal source. Calculating the angle information through the phase difference solves the problem of how to more accurately determine the exact position and direction of the human body within the radar range. Applying the steering vector to the sampling point of each frame can enhance the signal from a specific angle. It focuses on a specific direction by adjusting the phase of the signal, improves the signal-to-noise ratio and signal quality, and overcomes the common noise and background interference problems in radar signal processing. By multiplying the steering vector with the frequency domain signal, the process of reconstructing the signal enhances the desired (breathing and heart rate) signal characteristics while reducing interference and noise.

[0053] Third, by setting two thresholds, it is possible to accurately determine whether the human body is in a resting state. After determining the resting state, the human chest position is accurately searched, and the respiratory frequency and heart rate are accurately calculated through bandpass filtering, wavelet transform, reconstruction and other steps. At the same time, the historically stored respiratory frequency and heart rate are combined for judgment to avoid calculation errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 It is a flow chart of the method of embodiment 1.

[0056] Figure 2 It is a detailed method flow chart of embodiment 1. DETAILED DESCRIPTION

[0057] To facilitate understanding by those skilled in the art, the structure of the present invention is now further described in detail with reference to the embodiments and the accompanying drawings. It should be understood that the steps mentioned in the present embodiment, unless otherwise specified in their order, can be adjusted in order according to actual needs, and can even be executed simultaneously or partially simultaneously.

[0058] Embodiment 1

[0059] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a human body automatic focusing vital sign monitoring method, comprising:

[0060] S1 sends a radar microwave signal to the target human body, receives three radar microwave echo signals, and extracts the frequency domain features of the three radar microwave echo signals respectively;

[0061] S101 performs intermediate frequency signal sampling on the three radar microwave echo signals respectively to obtain three radar microwave digital signals.

[0062] S102 performs one-dimensional FFT calculation on the three-way radar microwave digital signals to obtain distance characteristics of the three-way radar microwave digital signals;

[0063] S103 performs two-dimensional FFT calculation on the three-channel radar microwave digital signals to obtain speed characteristics and angle characteristics of the three-channel radar microwave digital signals.

[0064] In this step, the radar is installed and adjusted so that its beam covers the human target detection area, which ensures that the radar signal can effectively cover and return data from the area. The radar monitoring system of the present application includes: a transmitter and three receivers, which collect intermediate frequency signals from the three receiving channels and read the radar waveform data. The purpose of this step is to capture the reflection signal of the human body in the target area. Each received echo signal is subjected to one-dimensional and two-dimensional fast Fourier transform (FFT) respectively. The one-dimensional FFT is used for frequency analysis in a single dimension, while the two-dimensional FFT uses two dimensions of time and space for a more comprehensive analysis.

[0065] S2 generates three-dimensional point cloud data of the human body through the frequency domain characteristics of the three-way radar microwave echo signal, and determines the lying position of the target human body according to the three-dimensional point cloud data of the human body;

[0066] S201 combines the distance feature, speed feature, and angle feature to obtain a three-dimensional frequency domain feature;

[0067] S202 maps the three-dimensional frequency domain features to the spatial dimension to obtain three-dimensional point cloud data of the human body.

[0068] S203 clusters and separates the three-dimensional point cloud data of the human body by using a DBSCAN algorithm to obtain a human body point cloud cluster;

[0069] S204 analyzes the distribution shape of points in the human body point cloud cluster to identify human shape features and obtain a shape similar to the human body;

[0070] S205 obtains the lying posture and position of the human body by calculating the length, width and direction of the shape similar to the human body.

[0071] In this step, by combining multiple frequency domain features (distance, speed, angle), human features can be more accurately constructed and identified, the accuracy of human body recognition can be improved, and the misdetection of other moving human bodies can be avoided; the three-dimensional shape of the human body can be reconstructed through point cloud technology, and human features can be identified and analyzed based on this; DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a commonly used point cloud data clustering algorithm, which is particularly suitable for situations where these data have noise and irregular distribution. Through this algorithm, different point cloud clusters can be effectively identified and separated. Analyzing the point distribution shape within the clustered human point cloud cluster is a key step in identifying human features. Using statistical shape analysis, features similar to the human body can be identified from the point cloud shape, such as the position of the head, torso and limbs, and the human body can be automatically distinguished from other objects. By calculating the length, width and direction of the point cloud cluster similar to the human body, the lying posture and position of the human body can be inferred.

[0072] S3 reconstructs the three-way human body radar microwave echo signals according to the three-way human body radar microwave echo signals obtained at the target human body lying position to obtain the reconstructed three-way human body radar microwave signals;

[0073] S301 calculates angle information of a lying position of a human body through a phase difference between a frequency domain feature of a first human body radar microwave signal and a frequency domain feature of a third human body radar microwave signal;

[0074] S302 performs steering vector processing on the sampling points of each frame of the frequency domain characteristics of the third radar microwave signal to obtain a steering vector of the third radar microwave signal;

[0075] S303 multiplies the steering vector of the third radar microwave signal by the frequency domain feature of the third radar microwave signal to obtain reconstructed three-path human body radar microwave signals.

[0076] In this step, by comparing the phase difference of radar signals from different channels, the angle information of the human body's lying position can be accurately calculated. The information provided by the phase difference can help determine the direction of the signal source. Calculating the angle information by phase difference solves the problem of how to more accurately determine the exact position and direction of the human body within the radar range.

[0077] Since the amplitude of human breathing and heart rate is relatively weak, non-contact monitoring methods tend to ignore some relatively weak breathing and heart rate signals. Applying the steering vector to the sampling points of each frame can enhance the signal from a specific angle. It focuses on a specific direction by adjusting the phase of the signal, improves the signal-to-noise ratio and signal quality, and overcomes the common noise and background interference problems in radar signal processing. By multiplying the steering vector with the frequency domain signal, the process of reconstructing the signal enhances the required (breathing and heart rate) signal characteristics while reducing interference and noise.

[0078] S4 determines whether the target human body is in a resting state through the reconstructed three-way human radar microwave signal. If so, it searches for the target human body's chest cavity and calculates the target human body's heart rate and respiratory rate.

[0079] S401 performs 2D FFT calculation on the reconstructed three-way human body radar microwave signals to obtain velocity characteristics of the reconstructed three-way human body radar microwave signals;

[0080] S402 determines whether the speed characteristics of the reconstructed three-way human body radar microwave signals are greater than a first threshold value, if so, the human body is in a moving state, otherwise, a static state determination is performed;

[0081] S403 cancels the reconstructed three-way human body radar microwave signals of the current frame and the previous two frames to obtain a differential signal;

[0082] S404 performs FFT calculation on the differential signal to obtain frequency domain characteristics of the differential signal;

[0083] S405: If the frequency domain characteristic of the differential signal is less than the second threshold, the human body is in a resting state.

[0084] In this step, the dynamic information of the moving target can be obtained simultaneously in the space and time axes through 2D FFT calculation. By evaluating whether the speed feature exceeds the preset first threshold, it can be preliminarily judged whether the target human body is in motion. Further, the current frame is canceled with the previous two frames to generate a differential signal. This process can highlight the changing part of the signal and is used to detect small motion changes. FFT calculation of the differential signal helps to analyze the spectral characteristics of the signal, so as to identify whether there is small motion. By comparing whether the frequency domain characteristics of the differential signal are less than the second threshold, it is judged whether the human body is in a resting state. Through the judgment of the two thresholds, it is accurately judged that the current human body is in a resting state, rather than a lying body movement state.

[0085] S406 performs 1D FFT and 2D FFT calculations on the reconstructed three-way human body radar microwave signals to obtain frequency domain features of the reconstructed three-way human body radar microwave signals;

[0086] S407 extracts frequency domain features with an index of 10-15 frequency band, finds the maximum value and its corresponding index in the frequency domain features with an index of 10-15 frequency band and stores them, calculates the mode in the stored frequency domain data as the target frequency index position of the human chest cavity;

[0087] S408: applying a window function to the frequency domain characteristics of the target frequency index position of the human chest cavity and performing FFT calculation to obtain phase information of the target frequency position;

[0088] S409 unwraps the phase at the target frequency position, calculates the time difference of the unwrap phase, and obtains phase difference data;

[0089] S410 accumulates 256 frames of phase difference data each time as input for calculating the breathing frequency and heart rate of the target human body.

[0090] In this step, after determining that the human body is in a resting state, the frequency band where the chest cavity is located is searched in the radar microwave echo. Specifically, the maximum value and its index are searched in a specific frequency band (index 10-15), and the mode is calculated to determine the target frequency index of the human chest cavity, which can effectively identify the main frequency components related to breathing and heart rate; by applying a window function to the target frequency index position and performing FFT calculation, the resolution and signal-to-noise ratio of the frequency domain features can be improved, thereby more accurately extracting phase information; phase dewrapping processing is performed on the phase and time difference is calculated to obtain phase difference data, which helps to further capture subtle physiological movement changes, such as breathing and heart rate; accumulating 256 frames of phase difference data provides stable input data for the calculation of breathing frequency and heart rate, which helps to improve the accuracy and reliability of the calculation.

[0091] After determining the frequency band where the chest cavity is located, the target heart rate and respiratory rate of the human body can be calculated using the frequency band data:

[0092] Specifically, the calculation of the target human heart rate includes:

[0093] After performing heart rate bandpass filtering on the phase difference data, the wavelet transform is used to perform 4-layer decomposition, and the detail coefficients of the third layer are extracted to reconstruct the high-frequency heart rate signal and the low-frequency heart rate signal;

[0094] Perform 1024-point FFT calculation on the reconstructed high-frequency heart rate signal and low-frequency heart rate signal to obtain the frequency domain characteristics of the reconstructed high-frequency heart rate signal and low-frequency heart rate signal;

[0095] Normalizing the peak values ​​of the frequency domain features of the high-frequency heart rate signal and the low-frequency heart rate signal, and calculating the difference between the maximum peak value and other peak values;

[0096] If the difference is less than the third threshold, the heart rate in the previous time window is output; if the difference is greater than the third threshold, the current heart rate is output through the high-frequency heart rate signal and the low-frequency heart rate signal;

[0097] Based on the historical data of 100 frames of high-frequency heart rate signals and low-frequency heart rate signals, the standard deviation of the historical data is calculated. If the standard deviation of the historical data is less than the fourth threshold, the current heart rate is output. If the standard deviation of the historical data is greater than the fourth threshold, the heart rate estimated based on the high-frequency heart rate signal is output.

[0098] In this step, the phase difference data is firstly subjected to heart rate bandpass filtering, which can effectively filter out interference signals that are not within the heart rate range, thereby extracting the heart rate-related signal components. Symlets wavelet transform is used for 4-layer decomposition to extract the detail coefficients of the third layer to reconstruct the high-frequency and low-frequency heart rate signals, which can analyze the transient changes in the time series signal and help extract the heart rate features.

[0099] By performing FFT calculation on the reconstructed signal, its frequency domain characteristics can be obtained and the main components (peaks) in the frequency domain can be identified; the peak values ​​of the frequency domain characteristics of the high-frequency and low-frequency heart rate signals are compared, and the stability of the heart rate is determined by normalization and difference calculation; if the difference is less than the third threshold, it means that the current frequency characteristic change is not obvious or not stable enough. In this case, the heart rate value determined in the previous time window is selected for output. This can avoid making wrong judgments when the signal characteristics are not obvious or there are fluctuations. During the heart rate monitoring process, the system will store the heart rate calculated under the most recent one or several stable conditions for use when the newly calculated heart rate does not have enough confidence. This is equivalent to a protection mechanism to ensure the stability and credibility of the heart rate output.

[0100] By evaluating the standard deviation of 100 frames of historical data, we can determine the changing pattern of the current heart rate and decide whether to output the current heart rate or rely on the heart rate estimation of the high-frequency part. By analyzing the peak difference and the standard deviation of historical data, if the standard deviation is small, it means that the heart rate is relatively stable, then the current heart rate estimate may be more reliable and can be output directly, which can improve the detection accuracy of abnormal situations or unstable signals.

[0101] Specifically, the calculation of the target human respiratory rate includes:

[0102] After the phase difference data is subjected to respiratory frequency bandpass filtering, it is decomposed into 4 layers using wavelet transform, and the detail coefficients of the third layer are extracted to reconstruct the high-frequency respiratory frequency signal and the low-frequency respiratory frequency signal;

[0103] Perform 1024-point FFT calculation on the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal to obtain the frequency domain characteristics of the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal;

[0104] The peak values ​​of the frequency domain features of the high-frequency respiratory frequency signal and the low-frequency respiratory frequency signal are counted to obtain the stage statistical data of the respiratory frequency;

[0105] The reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal are compared with the stage statistical data of the respiratory frequency to obtain a comparison difference. If the comparison difference is less than the fifth threshold, the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal are output; otherwise, the stage statistical data of the respiratory frequency are output.

[0106] In this step, irrelevant frequency components are preliminarily removed by bandpass filtering of the respiratory frequency. The multi-layer decomposition of wavelet transform (the present application uses Symlets wavelet basis) can capture the different frequency details of the signal. The third layer of detail coefficient decomposition of high-frequency and low-frequency respiratory signals can help distinguish and analyze different respiratory frequency components. Fast Fourier transform (FFT) calculation is performed on the reconstructed high-frequency and low-frequency respiratory signals, which can provide the frequency domain characteristics of these signals and help identify the main components of the respiratory frequency. By statistically analyzing the frequency domain characteristics of high-frequency and low-frequency signals, the changing trend of the respiratory frequency is evaluated, thereby helping to identify periodic changes and possible abnormalities. The main purpose of the comparison is to verify whether the key features of the signal are accurately retained or enhanced during the signal processing (including filtering, wavelet transform, and reconstruction), rather than being lost or generating excessive distortion. The results of the comparison will guide how to output the respiratory rate in the final step. If the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal perform well in denoising and feature retention, that is, less than the fifth threshold, the final respiratory frequency can be determined based on these signals, indicating that the current respiratory frequency is relatively stable and reliable and can be directly output, otherwise the stage statistical data of the respiratory frequency is output. It can improve the detection accuracy of abnormal breathing or unstable signals.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0112] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0113] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0114] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0115] 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 conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Claims

1. A human body automatic focusing vital sign monitoring method, characterized in that: include: Send a radar microwave signal to the target human body, receive three radar microwave echo signals, and extract the frequency domain features of the three radar microwave echo signals respectively; Generate three-dimensional point cloud data of the human body through the frequency domain characteristics of the three-way radar microwave echo signal, and determine the lying position of the target human body according to the three-dimensional point cloud data of the human body; Reconstructing the three-way human body radar microwave echo signals according to the three-way human body radar microwave echo signals obtained at the target human body lying position to obtain the reconstructed three-way human body radar microwave signals; The reconstructed three-way human radar microwave signal is used to determine whether the target human body is in a resting state. If so, the target human chest cavity is found and the target human heart rate and respiratory rate are calculated.

2. The method for automatically focusing vital signs monitoring of a human body according to claim 1, characterized in that: Before extracting the frequency domain features of the three-way radar microwave echo signals respectively, the following steps are performed: The three radar microwave echo signals are respectively sampled for intermediate frequency signals to obtain three radar microwave digital signals.

3. The method for automatically focusing vital signs monitoring of a human body according to claim 2, characterized in that: The method of extracting the frequency domain features of the three radar microwave echo signals respectively includes: Perform one-dimensional FFT calculation on the three-way radar microwave digital signals to obtain the distance characteristics of the three-way radar microwave digital signals; The three-way radar microwave digital signals are subjected to two-dimensional FFT calculation to obtain the velocity characteristics and angle characteristics of the three-way radar microwave digital signals.

4. The method for automatically focusing vital signs monitoring of a human body according to claim 3, characterized in that: The method of generating three-dimensional point cloud data of a human body by using frequency domain characteristics of three-channel microwave signals includes: Combining the distance feature, speed feature, and angle feature to obtain a three-dimensional frequency domain feature; The three-dimensional frequency domain features are mapped to the spatial dimension to obtain the three-dimensional point cloud data of the human body.

5. The method for automatically focusing vital signs monitoring of a human body according to claim 4, characterized in that: Determining the target human body lying position according to the human body three-dimensional point cloud data includes: The human body 3D point cloud data is clustered and separated by using DBSCAN algorithm to obtain human body point cloud clusters; Analyze the distribution shape of points in the human point cloud cluster to identify human features and obtain a shape similar to the human body; By calculating the length, width and direction of a shape similar to the human body, the lying posture and position of the human body can be obtained.

6. The method for automatically focusing vital signs monitoring of a human body according to claim 5, characterized in that: The signal reconstruction of the three-path human body radar microwave signals to obtain the reconstructed three-path human body radar microwave signals comprises: Calculate the angle information of the lying position of the human body through the phase difference between the frequency domain characteristics of the first human body radar microwave signal and the frequency domain characteristics of the third human body radar microwave signal; Performing steering vector processing on the sampling points of each frame of the frequency domain characteristics of the third radar microwave signal to obtain the steering vector of the third radar microwave signal; The steering vector of the third radar microwave signal is multiplied by the frequency domain feature of the third radar microwave signal to obtain the reconstructed three-way human body radar microwave signal.

7. The method for automatically focusing vital signs monitoring of a human body according to claim 6, characterized in that: The determining whether the target human body is in a resting state by using the reconstructed three-way human body radar microwave signal comprises: Perform 2D FFT calculation on the reconstructed three-way human body radar microwave signals to obtain the velocity characteristics of the reconstructed three-way human body radar microwave signals; Determine whether the speed characteristics of the reconstructed three-way human body radar microwave signals are greater than a first threshold, if so, the human body is in a moving state, otherwise, perform a static judgment; Performing cancellation processing on the reconstructed three-way human body radar microwave signals of the current frame and the previous two frames to obtain a differential signal; Perform FFT calculation on the differential signal to obtain the frequency domain characteristics of the differential signal; If the frequency domain characteristic of the differential signal is less than the second threshold, the human body is in a resting state.

8. The method for automatically focusing vital signs monitoring of a human body according to claim 7, characterized in that: The searching for the target human chest cavity comprises: Perform 1D FFT and 2D FFT calculations on the reconstructed three-way human body radar microwave signals to obtain the frequency domain characteristics of the reconstructed three-way human body radar microwave signals; Extract frequency domain features with an index of 10-15 frequency band, find the maximum value and its corresponding index in the frequency domain features with an index of 10-15 frequency band and store them, calculate the mode in the stored frequency domain data as the target frequency index position of the human chest cavity; Applying a window function to the frequency domain characteristics of the target frequency index position of the human chest cavity and then performing FFT calculation to obtain the phase information of the target frequency position; Unwrapping the phase at the target frequency position, calculating the time difference of the unwrapped phase, and obtaining phase difference data; 256 frames of phase difference data are accumulated each time and used as input for calculating the breathing frequency and heart rate of the target person.

9. The method for automatically focusing vital signs monitoring of a human body according to claim 8, characterized in that: The calculation of the target human heart rate comprises: After performing heart rate bandpass filtering on the phase difference data, the wavelet transform is used to perform 4-layer decomposition, and the detail coefficients of the third layer are extracted to reconstruct the high-frequency heart rate signal and the low-frequency heart rate signal; Perform 1024-point FFT calculation on the reconstructed high-frequency heart rate signal and low-frequency heart rate signal to obtain the frequency domain characteristics of the reconstructed high-frequency heart rate signal and low-frequency heart rate signal; Normalizing the peak values ​​of the frequency domain features of the high-frequency heart rate signal and the low-frequency heart rate signal, and calculating the difference between the maximum peak value and other peak values; If the difference is less than the third threshold, the heart rate in the previous time window is output; if the difference is greater than the third threshold, the current heart rate is output through the high-frequency heart rate signal and the low-frequency heart rate signal; Based on the historical data of 100 frames of high-frequency heart rate signals and low-frequency heart rate signals, the standard deviation of the historical data is calculated. If the standard deviation of the historical data is less than the fourth threshold, the current heart rate is output. If the standard deviation of the historical data is greater than the fourth threshold, the heart rate estimated based on the high-frequency heart rate signal is output.

10. The method for automatically focusing vital signs monitoring of a human body according to claim 8, characterized in that: The calculation of the target human respiratory rate comprises: After the phase difference data is subjected to respiratory frequency bandpass filtering, it is decomposed into 4 layers using wavelet transform, and the detail coefficients of the third layer are extracted to reconstruct the high-frequency respiratory frequency signal and the low-frequency respiratory frequency signal; Perform 1024-point FFT calculation on the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal to obtain the frequency domain characteristics of the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal; The peak values ​​of the frequency domain features of the high-frequency respiratory frequency signal and the low-frequency respiratory frequency signal are counted to obtain the stage statistical data of the respiratory frequency; The reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal are compared with the stage statistical data of the respiratory frequency to obtain a comparison difference. If the comparison difference is less than the fifth threshold, the reconstructed high-frequency respiratory frequency signal and low-frequency respiratory frequency signal are output; otherwise, the stage statistical data of the respiratory frequency are output.

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