A method for long-distance vital signal monitoring based on multi-channel technology

Abstract

This invention discloses a method for long-distance vital sign monitoring based on multi-channel imaging. The method includes the following steps: S1: Acquiring and preprocessing echo signals from multiple channels of the human body, and then merging the preprocessed echo signals; S2: Detecting vital signs within distance units based on the merged echo signals to obtain a set of vital sign distance units containing multiple distance units; S3: Calculating vital sign phase signals based on the set of vital sign distance units; S4: Decomposing the vital sign phase signals into several components, analyzing the respiratory signal component and the heartbeat signal component, and calculating the human respiratory rate and heart rate. This invention achieves high-accuracy long-distance vital sign monitoring.

Description

Technical Field

[0001] This invention relates to the field of non-contact vital sign monitoring technology, specifically to a method for long-distance vital signal monitoring based on multiple channels. Background Technology

[0002] As the concept of healthy living gains popularity, people are paying increasing attention to their own health and vital sign monitoring. According to the latest data from the World Health Organization, the number of people with chronic diseases worldwide is on the rise, accounting for nearly 70% of all deaths. This not only highlights the urgent need for vital sign monitoring but also prompts people to detect and effectively prevent chronic diseases as early as possible to improve overall quality of life. At the same time, people are more proactively involved in their own health management, and more and more people are using technological means to monitor their vital signs.

[0003] Non-contact vital sign monitoring technology eliminates the need for physical contact with the user, significantly improving monitoring comfort and convenience while protecting user privacy. It meets people's daily monitoring needs and is expected to see wider application in the future. Compared to traditional infrared and optical sensors, millimeter-wave radar demonstrates superior performance in capturing minute physiological changes. This technology not only monitors basic physiological movements but also tracks complex and subtle physiological indicators such as heart rate variability, respiratory patterns, and mood changes, providing users with more comprehensive information about their health status. Notably, millimeter-wave radar is highly adaptable to the environment and less affected by external factors such as light and temperature, making it suitable for real-world, complex scenarios, including but not limited to hospital wards, offices, and bedrooms.

[0004] However, considering the attenuation characteristics of millimeter-wave signals, the signal-to-noise ratio gradually decreases with increasing propagation distance. Simultaneously, the displacement of the human chest cavity caused by respiratory movements and heartbeats is extremely weak and easily masked by noise, further reducing the accuracy of millimeter-wave radar in long-distance vital sign monitoring. These characteristics limit its application in certain scenarios, such as living rooms or conference rooms, because users may be far from the radar sensor, making high-precision vital sign monitoring impossible.

[0005] To address this issue and improve the applicability of millimeter-wave radar vital sign monitoring systems, existing technologies mainly fall into two categories: radar hardware improvements and signal processing algorithm research. By optimizing frequency selection and antenna design, radar hardware improvements enhance the system's sensitivity and resolution. These improvements strengthen the radar system's ability to acquire and analyze vital sign signals, but also increase system complexity and cost. Furthermore, some hardware improvements may lead to increased system size, weight, and power consumption, thus limiting their ease of deployment in practical scenarios. For signal processing algorithms, existing research primarily focuses on noise removal and vital sign signal enhancement. For example, adaptive wavelet transform removes impulse noise interference, and differential enhancement methods enhance heartbeat signals, improving the accuracy and robustness of vital sign monitoring without altering the system's hardware structure, facilitating deployment and debugging. However, the improvement in monitoring distance is relatively limited; at longer distances, the system's vital sign monitoring accuracy decreases with increasing monitoring distance.

[0006] Chinese invention application No. 202010413424.6 discloses "A Radar-Based Method and System for Detecting Respiratory and Heartbeat Signals," whose technical solution is as follows: A) Determine the presence of a target based on transmitted and received signals; B) Process the mixed vital signs signal of the target as follows: B1) High-pass filtering and FFT transformation of the mixed vital signs signal to obtain first spectrum data; B2) If the frequency corresponding to the maximum amplitude point is within the respiratory frequency range and there is a maximum amplitude point at and / or near the Q harmonic of that frequency, then that frequency is taken as the dominant respiratory frequency; B3) Decompose the mixed vital signs signal of the target based on empirical wavelet transform; B4) If the maximum frequency of the third or fourth component is within the preset heartbeat frequency range, its maximum frequency is taken as the dominant heartbeat frequency. This technical solution decomposes the mixed vital signs signal based on empirical wavelet transform, adaptively separating respiratory and heartbeat signals to extract respiratory and heartbeat frequencies. This technical solution improves the accuracy and robustness of vital sign monitoring to some extent. However, at longer distances, the accuracy of vital sign monitoring decreases as the monitoring distance increases. Therefore, this technical solution is not suitable for long-distance monitoring applications. Summary of the Invention

[0007] To address the technical problem of insufficient accuracy in long-distance vital sign monitoring based on millimeter-wave radar in existing technologies, this invention provides a multi-channel long-distance vital signal monitoring method. The technical solution adopted by this invention is as follows:

[0008] This invention provides a method for long-distance vital signal monitoring based on multiple channels, the method comprising the following steps:

[0009] S1: Acquire multi-channel echo signals from the human body and preprocess them, then merge the preprocessed multi-channel echo signals.

[0010] S2: Detect vital signs within the range cells based on the merged echo signals to obtain a set of vital sign range cells containing multiple range cells;

[0011] S3: Calculate the vital sign phase signal based on the set of vital sign distance units;

[0012] S4: Decompose the vital signs phase signal into several components, analyze the respiratory signal component and the heartbeat signal component, and calculate the human respiratory rate and heartbeat rate.

[0013] As a preferred embodiment, in step S1, the method for acquiring and preprocessing the multi-channel echo signals of the human body, and merging the preprocessed multi-channel echo signals includes:

[0014] The echo signals from multiple channels of the human body are acquired. After preprocessing, including removing DC bias and static object noise interference, the channel weights are calculated, and then the signals from multiple channels are merged according to the weights.

[0015] As a preferred approach, a method for acquiring multi-channel echo signals from the human body, performing preprocessing including removing DC bias and static object clutter interference, calculating channel weights, and then merging signals from multiple channels according to the weights includes:

[0016] S11: Acquire multi-channel echo signals from the human body and store them in the original data matrix. In the given information, k = 1, 2, ..., K, m = 1, 2, ..., M, n = 1, 2, ..., N; where M represents the number of frames of the transmitted signal, N represents the number of sampling points in each frame of the transmitted signal, and K represents the number of channels.

[0017] S12: After preprocessing the echo signals of multiple channels to remove DC bias and static object clutter interference, calculate the channel weights, and then merge the signals of multiple channels according to the weights, specifically as follows;

[0018] S121: Calculate the DC bias compensation data matrix ;

[0019] S122: Based on the DC bias compensation data matrix Calculate the mean matrix The formula is as follows:

[0020]

[0021] S123: According to the mean matrix Calculate peak vector For k=1,2,…,K, the formula is as follows:

[0022]

[0023]

[0024] in, express Domain transformation;

[0025] S124: Let the peak matrix ; where the peak matrix The formula for calculating the column vector is:

[0026] ;

[0027] Calculate the channel matrix The formula is as follows:

[0028]

[0029] Where H represents the conjugate transpose of the matrix;

[0030] S125: Yes The decomposition is as follows:

[0031]

[0032] Among them, eigenvalues , It is the corresponding feature vector;

[0033] S126: Set the channel weight ;

[0034] S127: Calculate the normalized weights For k=1,2,…,K, the formula is as follows:

[0035]

[0036] S128: Based on the normalized weights Calculate the channel merging matrix The formula is as follows:

[0037] .

[0038] As a preferred embodiment, in step S121, the DC bias compensation data matrix is ​​calculated. The methods include:

[0039] S1211: Extract the original data matrix real part matrix and imaginary part matrix ;

[0040] S1212: Set channel index ;

[0041] S1213: Let the distance cell index ;

[0042] S1214: Let the real part of the vector Imaginary part vector ;

[0043] in, It is a column vector of length M;

[0044] S1215: Construct the center matrix X and the radius vector y, as shown in the following formula:

[0045]

[0046]

[0047] S1216: Solving DC parameters The formula is as follows:

[0048]

[0049] in, It is a vector of length 3;

[0050] S1217: Calculation and The formula is as follows:

[0051]

[0052]

[0053] S1218: Calculation The formula is as follows:

[0054]

[0055] in, Represents the imaginary unit;

[0056] S1219: If Execute step S12110, otherwise let Execute step S1214;

[0057] S12110: If Execute step S12111, otherwise let Execute step S1213;

[0058] S12111: Obtain .

[0059] As a preferred embodiment, in step S2, the method for obtaining a set of vital sign distance cells containing multiple distance cells by detecting vital signals within the distance cells based on the merged echo signals includes:

[0060] The phase of all range cells in the echo signal after channel merging is extracted, the phase parameter of each range cell is calculated, the detection window length and parameter threshold are set, the vital signs within the range cells are detected, and a set of vital signs range cells containing multiple range cells is obtained.

[0061] As a preferred approach, the method of extracting the phase of all range cells in the echo signal after channel merging, calculating the phase parameters of each range cell, setting the detection window length and parameter threshold, detecting vital signs within the range cells, and obtaining a set of vital sign range cells containing multiple range cells includes:

[0062] S21: Extract Phase of each distance unit m = 1, 2, ..., M;

[0063] S22: Calculation Related functions and its domain transformation The formula is as follows:

[0064]

[0065]

[0066] S23: Calculation parameters and The formula is as follows:

[0067]

[0068] in, This indicates the lower limit of human respiratory rate. This indicates the upper limit of human heart rate. Indicates the sampling frequency;

[0069] S24: Calculate phase parameters For n=1,2,…,N, the formula is as follows:

[0070]

[0071] S25: Search The formula is as follows:

[0072]

[0073] S26: Calculate the mean of the phase parameters and standard deviation The formula is as follows:

[0074]

[0075]

[0076] S27: Set the human target detection window length to L, let ;

[0077] S28: Calculate the phase parameter threshold The formula is as follows:

[0078]

[0079] in, For adjustment factors;

[0080] S29, if Execute step S210, otherwise let Proceed to step S28;

[0081] S210, Order ;

[0082] S211, if Execute step S212, otherwise let Proceed to step S28;

[0083] S212, Order Vital signs distance unit set A total of There are vital signs signals in each distance unit.

[0084] As a preferred embodiment, in step S3, the method for calculating the vital sign phase signal based on the set of vital sign distance units includes:

[0085] The respiratory weight and heart rate weight of different distance units are calculated based on the set of vital sign distance units. Then, the signals in the set of vital sign distance units are merged according to the weight to obtain human respiratory signals and heart rate signals. Further merging is then performed to obtain vital sign phase signals.

[0086] As a preferred embodiment, the method of calculating the respiratory weight and heart rate weight of different distance units based on the set of vital sign distance units, and then merging the signals in the set of vital sign distance units according to the weights to obtain human respiratory signals and heart rate signals, and further merging them to obtain vital sign phase signals includes:

[0087] S31: Calculation domain transformation b = 1, 2, ..., B, the formula is as follows:

[0088]

[0089] S32: Calculate parameters The formula is as follows:

[0090]

[0091]

[0092] in, and Indicates the upper and lower limits of human respiratory rate. and Indicates the upper and lower limits of human heart rate;

[0093] S33: Calculation Components within the range of human respiratory rate and components within the heart rate range b = 1, 2, ..., B, the formula is as follows:

[0094]

[0095]

[0096] S34: Determine the respiratory reference signal and heartbeat reference signal The formula is as follows:

[0097]

[0098]

[0099] S35: Calculate the weighted respiratory value and heart rate weights The formula is as follows:

[0100]

[0101]

[0102] in, Indicates the covariance of the two. Represents the variance of sequence x;

[0103] S36: According to the aforementioned respiratory weights and the heartbeat weights By merging the signals from the vital sign distance unit set, the respiratory phase signal is obtained. and heartbeat phase signal The formula is as follows:

[0104]

[0105]

[0106] S37: Obtaining the breathing untangling signal after untangling. Heartbeat untangling signal ;

[0107] S38: Merging to obtain vital sign phase signals The formula is as follows:

[0108] .

[0109] As a preferred embodiment, in step S4, the method for decomposing the vital sign phase signal into several components, analyzing the respiratory signal component and the heartbeat signal component, and calculating the human respiratory rate and heartbeat rate includes:

[0110] S41: Calculate the vital signs phase signal differential signal The formula is as follows:

[0111] m=1,2,…,M-1;

[0112]

[0113] S42: The bandpass phase signal is obtained after passing through a bandpass filter. m = 1, 2, ..., M;

[0114] S43: Set the number of decompositions ;

[0115] S44: Will Decomposed into Each signal component ;

[0116] S45: Let the decompose index ;

[0117] S46: Calculate peak frequency The formula is as follows:

[0118]

[0119] S47: If ,make ,

[0120] If the above steps are not executed, proceed to step S46; otherwise, proceed to step S48.

[0121] S48: If ,make ,

[0122] If the above steps are not executed, proceed to step S46; otherwise, proceed to step S49.

[0123] S49: If Execute step S410, otherwise let Execute step S46;

[0124] S410: Estimating Human Respiratory Rate and heart rate The formula is as follows:

[0125]

[0126] .

[0127] As a preferred embodiment, in step S44, the... Decomposed into Each signal component The methods include:

[0128] S441: Set Embedding Dimension ;

[0129] S442: Constructing the trajectory matrix The formula is as follows:

[0130]

[0131] S443: Yes The decomposition is as follows:

[0132]

[0133] Where G is the singular value matrix, U is a left orthogonal matrix, and P is a right orthogonal matrix;

[0134] S444: Extract the signal The main components are shown in the following formula:

[0135]

[0136]

[0137]

[0138] S445: Reconstruct the index ;

[0139] S446: Calculate the signal reconstruction matrix The formula is as follows:

[0140]

[0141] S447: Reconstructing the... Each signal component is represented by the following formula:

[0142]

[0143] S448: If Execute step S449, otherwise let Execute step S446;

[0144] S449: Obtain Each signal component .

[0145] Compared with the prior art, the beneficial effects of this invention are:

[0146] This invention improves the signal-to-noise ratio of human body reflection signals by preprocessing multi-channel echo signals without modifying the radar system hardware. This not only reduces system complexity but also facilitates debugging and deployment. Furthermore, this invention locates multiple range cells containing the human target using phase parameters and processes vital sign signals reflected from different parts of the body, enhancing the radar system's ability to acquire and analyze long-range vital sign signals. Finally, by decomposing human vital signs into multiple signal components and extracting those that match human breathing and heartbeat frequencies, the influence of respiratory harmonics and noise is suppressed, improving the robustness and accuracy of long-range vital sign monitoring. Attached Figure Description

[0147] Figure 1 This embodiment provides a flowchart of a method for long-distance vital signal monitoring based on multiple channels. Detailed Implementation

[0148] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention.

[0149] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0150] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0151] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0152] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The invention will be further described below with reference to the accompanying drawings and embodiments.

[0153] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0154] Example 1

[0155] Please refer to Figure 1 This invention provides a method for long-distance vital signal monitoring based on multi-channel technology, the method comprising the following steps:

[0156] S1: Acquire multi-channel echo signals from the human body and preprocess them, then merge the preprocessed multi-channel echo signals.

[0157] S2: Based on the merged echo signal, detect the vital signs within the distance unit to obtain a set of vital sign distance units containing multiple distance units; wherein, the distance unit is obtained by Fourier transform processing of the echo signal from the human body's multi-channel signal.

[0158] S3: Calculate the vital sign phase signal based on the set of vital sign distance units;

[0159] S4: Decompose the vital signs phase signal into several components, analyze the respiratory signal component and the heartbeat signal component, and calculate the human respiratory rate and heartbeat rate.

[0160] In a specific embodiment, in step S1, the method for acquiring and preprocessing the multi-channel echo signals of the human body, and merging the preprocessed multi-channel echo signals includes:

[0161] The echo signals from multiple channels of the human body are acquired. After preprocessing, including removing DC bias and static object noise interference, the channel weights are calculated, and then the signals from multiple channels are merged according to the weights.

[0162] In one specific embodiment, a method for acquiring multi-channel echo signals of the human body, performing preprocessing including removing DC bias and static object clutter interference, calculating channel weights, and then merging signals from multiple channels according to the weights includes:

[0163] S11: Acquire multi-channel echo signals from the human body and store them in the original data matrix. In the given information, k = 1, 2, ..., K, m = 1, 2, ..., M, n = 1, 2, ..., N; where M represents the number of frames of the transmitted signal, N represents the number of sampling points in each frame of the transmitted signal, and K represents the number of channels.

[0164] S12: After preprocessing the echo signals of multiple channels to remove DC bias and static object clutter interference, calculate the channel weights, and then merge the signals of multiple channels according to the weights, specifically as follows;

[0165] S121: Calculate the DC bias compensation data matrix ;

[0166] S122: Based on the DC bias compensation data matrix Calculate the mean matrix The formula is as follows:

[0167]

[0168] S123: According to the mean matrix Calculate peak vector For k=1,2,…,K, the formula is as follows:

[0169]

[0170]

[0171] in, express Domain transformation;

[0172] S124: Let the peak matrix ; where the peak matrix The formula for calculating the column vector is:

[0173] ;

[0174] Calculate the channel matrix The formula is as follows:

[0175]

[0176] Where H represents the conjugate transpose of the matrix;

[0177] S125: Yes The decomposition is as follows:

[0178]

[0179] Among them, eigenvalues , It is the corresponding feature vector;

[0180] S126: Set the channel weight ;

[0181] S127: Calculate the normalized weights For k=1,2,…,K, the formula is as follows:

[0182]

[0183] S128: Based on the normalized weights Calculate the channel merging matrix The formula is as follows:

[0184] .

[0185] In one specific embodiment, in step S121, the DC bias compensation data matrix is ​​calculated. The methods include:

[0186] S1211: Extract the original data matrix real part matrix and imaginary part matrix ;

[0187] S1212: Set channel index ;

[0188] S1213: Let the distance cell index ;

[0189] S1214: Let the real part of the vector Imaginary part vector ;

[0190] in, It is a column vector of length M;

[0191] S1215: Construct the center matrix X and the radius vector y, as shown in the following formula:

[0192]

[0193]

[0194] S1216: Solving DC parameters The formula is as follows:

[0195]

[0196] in, It is a vector of length 3;

[0197] S1217: Calculation and The formula is as follows:

[0198]

[0199]

[0200] S1218: Calculation The formula is as follows:

[0201]

[0202] in, Represents the imaginary unit;

[0203] S1219: If Execute step S12110, otherwise let Execute step S1214;

[0204] S12110: If Execute step S12111, otherwise let Execute step S1213;

[0205] S12111: Obtain .

[0206] In a specific embodiment, the method for obtaining a set of vital sign distance cells containing multiple distance cells by detecting vital signals within the distance cells based on the merged echo signals in step S2 includes:

[0207] The phase of all range cells in the echo signal after channel merging is extracted, the phase parameter of each range cell is calculated, the detection window length and parameter threshold are set, the vital signs within the range cells are detected, and a set of vital signs range cells containing multiple range cells is obtained.

[0208] In one specific embodiment, the method for extracting the phase of all range cells of the echo signal after channel merging, calculating the phase parameter of each range cell, setting the detection window length and parameter threshold, detecting vital signs within the range cells, and obtaining a set of vital sign range cells containing multiple range cells includes:

[0209] S21: Extract Phase of each distance unit m = 1, 2, ..., M;

[0210] S22: Calculation Related functions and its domain transformation The formula is as follows:

[0211]

[0212]

[0213] S23: Calculation parameters and The formula is as follows:

[0214]

[0215] in, This indicates the lower limit of human respiratory rate. This indicates the upper limit of human heart rate. Indicates the sampling frequency;

[0216] S24: Calculate phase parameters For n=1,2,…,N, the formula is as follows:

[0217]

[0218] S25: Search The formula is as follows:

[0219]

[0220] S26: Calculate the mean of the phase parameters and standard deviation The formula is as follows:

[0221]

[0222]

[0223] S27: Set the human target detection window length to L, let ;

[0224] S28: Calculate the phase parameter threshold The formula is as follows:

[0225]

[0226] in, For adjustment factors;

[0227] S29, if Execute step S210, otherwise let Proceed to step S28;

[0228] S210, Order ;

[0229] S211, if Execute step S212, otherwise let Proceed to step S28;

[0230] S212, Order Vital signs distance unit set A total of There are vital signs signals in each distance unit.

[0231] In a specific embodiment, the method for calculating the vital sign phase signal based on the set of vital sign distance units in step S3 includes:

[0232] The respiratory weight and heart rate weight of different distance units are calculated based on the set of vital sign distance units. Then, the signals in the set of vital sign distance units are merged according to the weight to obtain human respiratory signals and heart rate signals. Further merging is then performed to obtain vital sign phase signals.

[0233] In a specific embodiment, the method of calculating the respiratory weight and heart rate weight of different distance units based on the set of vital sign distance units, and then merging the signals in the set of vital sign distance units according to the weights to obtain human respiratory signals and heart rate signals, and further merging them to obtain vital sign phase signals includes:

[0234] S31: Calculation domain transformation b = 1, 2, ..., B, the formula is as follows:

[0235]

[0236] S32: Calculate parameters The formula is as follows:

[0237]

[0238]

[0239] in, and Indicates the upper and lower limits of human respiratory rate. and Indicates the upper and lower limits of human heart rate;

[0240] S33: Calculation Components within the range of human respiratory rate and components within the heart rate range b = 1, 2, ..., B, the formula is as follows:

[0241]

[0242]

[0243] S34: Determine the respiratory reference signal and heartbeat reference signal The formula is as follows:

[0244]

[0245]

[0246] S35: Calculate the weighted respiratory value and heart rate weights The formula is as follows:

[0247]

[0248]

[0249] in, Indicates the covariance of the two. Represents the variance of sequence x;

[0250] S36: According to the aforementioned respiratory weights and the heartbeat weights By merging the signals from the vital sign distance unit set, the respiratory phase signal is obtained. and heartbeat phase signal The formula is as follows:

[0251]

[0252]

[0253] S37: Obtaining the breathing untangling signal after untangling. Heartbeat untangling signal ;

[0254] S38: Merging to obtain vital sign phase signals The formula is as follows:

[0255] .

[0256] In a specific embodiment, in step S4, the method for decomposing the vital sign phase signal into several components, analyzing the respiratory signal component and the heartbeat signal component, and calculating the human respiratory rate and heartbeat rate includes:

[0257] S41: Calculate the vital signs phase signal differential signal The formula is as follows:

[0258] m=1,2,…,M-1;

[0259]

[0260] S42: The bandpass phase signal is obtained after passing through a bandpass filter. m = 1, 2, ..., M;

[0261] S43: Set the number of decompositions ;

[0262] S44: Will Decomposed into Each signal component ;

[0263] S45: Let the decompose index ;

[0264] S46: Calculate peak frequency The formula is as follows:

[0265]

[0266] S47: If ,make ,

[0267] If the above steps are not executed, proceed to step S46; otherwise, proceed to step S48.

[0268] S48: If ,make ,

[0269] If the above steps are not executed, proceed to step S46; otherwise, proceed to step S49.

[0270] S49: If Execute step S410, otherwise let Execute step S46;

[0271] S410: Estimating Human Respiratory Rate and heart rate The formula is as follows:

[0272]

[0273] .

[0274] In one specific embodiment, in step S44, the Decomposed into Each signal component The methods include:

[0275] S441: Set Embedding Dimension ;

[0276] S442: Constructing the trajectory matrix The formula is as follows:

[0277]

[0278] S443: Yes The decomposition is as follows:

[0279]

[0280] Where G is the singular value matrix, U is a left orthogonal matrix, and P is a right orthogonal matrix;

[0281] S444: Extract the signal The main components are shown in the following formula:

[0282]

[0283]

[0284]

[0285] S445: Reconstruct the index ;

[0286] S446: Calculate the signal reconstruction matrix The formula is as follows:

[0287]

[0288] S447: Reconstructing the... Each signal component is represented by the following formula:

[0289]

[0290] S448: If Execute step S449, otherwise let Execute step S446;

[0291] S449: Obtain Each signal component .

[0292] Example 2

[0293] Please refer to Figure 1 This embodiment can be considered an improved or extended embodiment of embodiment 1, specifically as follows:

[0294] A method for long-distance vital signal monitoring based on multi-channel technology, the method comprising the following steps:

[0295] S1: Acquire multi-channel echo signals from the human body and preprocess them, then merge the preprocessed multi-channel echo signals.

[0296] S2: Based on the merged echo signal, detect the vital signs within the distance unit to obtain a set of vital sign distance units containing multiple distance units; wherein, the distance unit is obtained by Fourier transform processing of the echo signal from the human body's multi-channel signal.

[0297] S3: Calculate the vital sign phase signal based on the set of vital sign distance units;

[0298] S4: Decompose the vital signs phase signal into several components, analyze the respiratory signal component and the heartbeat signal component, and calculate the human respiratory rate and heartbeat rate.

[0299] Specifically, this invention first preprocesses human body reflection signals acquired from multiple channels, improving the signal-to-noise ratio of these signals without altering the radar system hardware. This not only reduces system complexity but also facilitates debugging and deployment. Secondly, by locating multiple range cells containing the human target using phase parameters and processing vital sign signals reflected from different body parts, the invention enhances the radar system's ability to acquire and analyze long-range vital sign signals. Finally, by decomposing human vital signs into multiple signal components and extracting those that match human breathing and heartbeat frequencies, the invention suppresses the influence of respiratory harmonics and noise, improving the robustness and accuracy of long-range vital sign monitoring.

[0300] In a specific embodiment, in step S1, the method for acquiring and preprocessing the multi-channel echo signals of the human body, and merging the preprocessed multi-channel echo signals includes:

[0301] The echo signals from multiple channels of the human body are acquired. After preprocessing, including removing DC bias and static object noise interference, the channel weights are calculated, and then the signals from multiple channels are merged according to the weights.

[0302] In one specific embodiment, a method for acquiring multi-channel echo signals of the human body, performing preprocessing including removing DC bias and static object clutter interference, calculating channel weights, and then merging signals from multiple channels according to the weights includes:

[0303] S11: Acquire multi-channel echo signals from the human body and store them in the original data matrix. In the given information, k = 1, 2, ..., K, m = 1, 2, ..., M, n = 1, 2, ..., N; where M represents the number of frames of the transmitted signal, N represents the number of sampling points in each frame of the transmitted signal, and K represents the number of channels.

[0304] Specifically, when , , At that time, the echo signals from 8 channels are received and stored in the original data matrix. In the given information, k = 1, 2, ..., 8, m = 1, 2, ..., 6000, n = 1, 2, ..., 100;

[0305] S12: After preprocessing the echo signals of multiple channels to remove DC bias and static object clutter interference, calculate the channel weights, and then merge the signals of multiple channels according to the weights, specifically as follows;

[0306] S121: Calculate the DC bias compensation data matrix ;

[0307] Specifically, when , , At that time, the DC bias compensation data matrix is ​​obtained. ;

[0308] S122: Based on the DC bias compensation data matrix Calculate the mean matrix The formula is as follows:

[0309]

[0310] Specifically, when , , When the mean matrix is ​​obtained, ;

[0311] S123: According to the mean matrix Calculate peak vector For k=1,2,…,K, the formula is as follows:

[0312]

[0313]

[0314] in, express Domain transformation;

[0315] Specifically, when At that time, search for the distance cell with the highest energy among the 8 channels, denoted as ;

[0316] S124: Let the peak matrix ; where the peak matrix The formula for calculating the column vector is:

[0317] ;

[0318] Calculate the channel matrix The formula is as follows:

[0319]

[0320] Where H represents the conjugate transpose of the matrix;

[0321] S125: Yes The decomposition is as follows:

[0322]

[0323] Among them, eigenvalues , It is the corresponding feature vector;

[0324] S126: Set the channel weight ;

[0325] S127: Calculate the normalized weights For k=1,2,…,K, the formula is as follows:

[0326]

[0327] S128: Based on the normalized weights Calculate the channel merging matrix The formula is as follows:

[0328]

[0329] Specifically, when , At that time, the channel merging matrix is ​​obtained. .

[0330] In one specific embodiment, in step S121, the DC bias compensation data matrix is ​​calculated. The methods include:

[0331] S1211: Extract the original data matrix real part matrix and imaginary part matrix ;

[0332] Specifically, when , , When, the real part matrix is ​​obtained. and imaginary part matrix ;

[0333] S1212: Set channel index ;

[0334] S1213: Let the distance cell index ;

[0335] S1214: Let the real part of the vector Imaginary part vector ;

[0336] in, It is a column vector of length M;

[0337] Specifically, when , , hour, ,

[0338] , It is a column vector of length 6000;

[0339] S1215: Construct the center matrix X and the radius vector y, as shown in the following formula:

[0340]

[0341]

[0342] S1216: Solving DC parameters The formula is as follows:

[0343]

[0344] in, It is a vector of length 3;

[0345] Specifically, ;

[0346] S1217: Calculation and The formula is as follows:

[0347]

[0348]

[0349] Specifically, when , At that time, and ;

[0350] S1218: Calculation The formula is as follows:

[0351]

[0352] in, Represents the imaginary unit;

[0353] S1219: If Execute step S12110, otherwise let Execute step S1214;

[0354] Specifically, , , At this point, step S12110 is executed;

[0355] S12110: If Execute step S12111, otherwise let Execute step S1213;

[0356] Specifically, , , At this time, order ;Execute step S1213;

[0357] S12111: Obtain .

[0358] In a specific embodiment, the method for obtaining a set of vital sign distance cells containing multiple distance cells by detecting vital signals within the distance cells based on the merged echo signals in step S2 includes:

[0359] The phase of all range cells in the echo signal after channel merging is extracted, the phase parameter of each range cell is calculated, the detection window length and parameter threshold are set, the vital signs within the range cells are detected, and a set of vital signs range cells containing multiple range cells is obtained.

[0360] In one specific embodiment, the method for extracting the phase of all range cells of the echo signal after channel merging, calculating the phase parameter of each range cell, setting the detection window length and parameter threshold, detecting vital signs within the range cells, and obtaining a set of vital sign range cells containing multiple range cells includes:

[0361] S21: Extract Phase of each distance unit m = 1, 2, ..., M;

[0362] S22: Calculation Related functions and its domain transformation The formula is as follows:

[0363]

[0364]

[0365] S23: Calculation parameters and The formula is as follows:

[0366]

[0367] in, This indicates the lower limit of human respiratory rate. This indicates the upper limit of human heart rate. Indicates the sampling frequency;

[0368] Specifically, when , , , At that time, seek , ;

[0369] S24: Calculate phase parameters For n=1,2,…,N, the formula is as follows:

[0370]

[0371] S25: Search The formula is as follows:

[0372]

[0373] Specifically, ;

[0374] S26: Calculate the mean of the phase parameters and standard deviation The formula is as follows:

[0375]

[0376]

[0377] Specifically, , ;

[0378] S27: Set the human target detection window length to L, let ;

[0379] Specifically, the human target detection window length is set to 3, making... ;

[0380] S28: Calculate the phase parameter threshold The formula is as follows:

[0381]

[0382] in, For adjustment factors;

[0383] Specifically, when hour, ;

[0384] S29, if Execute step S210, otherwise let Proceed to step S28;

[0385] Specifically, , , At this time, order Proceed to step S28;

[0386] S210, Order ;

[0387] S211, if Execute step S212, otherwise let Proceed to step S28;

[0388] S212, Order Vital signs distance unit set A total of There are signs of life within a certain distance unit;

[0389] Specifically, , A total of 4 distance units were detected to have vital signs.

[0390] In a specific embodiment, the method for calculating the vital sign phase signal based on the set of vital sign distance units in step S3 includes:

[0391] The respiratory weight and heart rate weight of different distance units are calculated based on the set of vital sign distance units. Then, the signals in the set of vital sign distance units are merged according to the weight to obtain human respiratory signals and heart rate signals. Further merging is then performed to obtain vital sign phase signals.

[0392] In a specific embodiment, the method of calculating the respiratory weight and heart rate weight of different distance units based on the set of vital sign distance units, and then merging the signals in the set of vital sign distance units according to the weights to obtain human respiratory signals and heart rate signals, and further merging them to obtain vital sign phase signals includes:

[0393] S31: Calculation domain transformation b = 1, 2, ..., B, the formula is as follows:

[0394]

[0395] S32: Calculate parameters The formula is as follows:

[0396]

[0397]

[0398] in, and Indicates the upper and lower limits of human respiratory rate. and Indicates the upper and lower limits of human heart rate;

[0399] Specifically, when , , , , , At that time, seek , , , ;

[0400] S33: Calculation Components within the range of human respiratory rate and components within the heart rate range b = 1, 2, ..., B, the formula is as follows:

[0401]

[0402]

[0403] Specifically, to obtain , ;

[0404] S34: Determine the respiratory reference signal and heartbeat reference signal The formula is as follows:

[0405]

[0406]

[0407] Specifically, , ;

[0408] S35: Calculate the weighted respiratory value and heart rate weights The formula is as follows:

[0409]

[0410]

[0411] in, Indicates the covariance of the two. Represents the variance of sequence x;

[0412] Specifically, to obtain , ;

[0413] S36: According to the aforementioned respiratory weights and the heartbeat weights By merging the signals from the vital sign distance unit set, the respiratory phase signal is obtained. and heartbeat phase signal The formula is as follows:

[0414]

[0415]

[0416] S37: Obtaining the breathing untangling signal after untangling. Heartbeat untangling signal ;

[0417] S38: Merging to obtain vital sign phase signals The formula is as follows:

[0418] .

[0419] In a specific embodiment, in step S4, the method for decomposing the vital sign phase signal into several components, analyzing the respiratory signal component and the heartbeat signal component, and calculating the human respiratory rate and heartbeat rate includes:

[0420] S41: Calculate the vital signs phase signal differential signal The formula is as follows:

[0421] m=1,2,…,M-1;

[0422]

[0423] S42: The bandpass phase signal is obtained after passing through a bandpass filter. m = 1, 2, ..., M;

[0424] S43: Set the number of decompositions ;

[0425] Specifically, ;

[0426] S44: Will Decomposed into Each signal component ;

[0427] Specifically, Decomposed into 6 signal components ;

[0428] S45: Let the decompose index ;

[0429] S46: Calculate peak frequency The formula is as follows:

[0430]

[0431] Specifically, when , , At that time, seek ;

[0432] S47: If ,make , If the above steps are not executed, proceed to step S46; otherwise, proceed to step S48.

[0433] Specifically, , , , At this point, proceed to step S48;

[0434] S48: If ,make , If the above steps are not executed, proceed to step S46; otherwise, proceed to step S49.

[0435] Specifically, , , , At this time, order , Execute step S46;

[0436] S49: If Execute step S410, otherwise let Execute step S46;

[0437] S410: Estimating Human Respiratory Rate and heart rate The formula is as follows:

[0438]

[0439] .

[0440] Specifically, when , At that time, the human respiratory rate was calculated. Heart rate .

[0441] In one specific embodiment, in step S44, the Decomposed into Each signal component The methods include:

[0442] S441: Set Embedding Dimension ;

[0443] Specifically, setting the embedding dimension ;

[0444] S442: Constructing the trajectory matrix The formula is as follows:

[0445]

[0446] S443: Yes The decomposition is as follows:

[0447]

[0448] Where G is the singular value matrix, U is a left orthogonal matrix, and P is a right orthogonal matrix;

[0449] S444: Extract the signal The main components are shown in the following formula:

[0450]

[0451]

[0452]

[0453] when At that time, six main components of the signal were extracted;

[0454] S445: Reconstruct the index ;

[0455] S446: Calculate the signal reconstruction matrix The formula is as follows:

[0456]

[0457] Specifically, when , , At that time, the signal reconstruction matrix is ​​obtained.

[0458] ;

[0459] S447: Reconstructing the... Each signal component is represented by the following formula:

[0460]

[0461] S448: If Execute step S449, otherwise let Execute step S446;

[0462] Specifically, , , At this time, order Execute step S446;

[0463] S449: Obtain Each signal component .

[0464] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for long-range vital signal monitoring based on multi-channel technology, used in millimeter-wave radar, characterized in that, The method includes the following steps: S1: Acquire multi-channel echo signals from the human body and preprocess them, then merge the preprocessed multi-channel echo signals. S2: Detect vital signs within the range cells based on the merged echo signals to obtain a set of vital sign range cells containing multiple range cells; S3: Calculate the vital sign phase signal based on the set of vital sign distance units; S4: Decompose the vital signs phase signal into several components, analyze the respiratory signal component and the heartbeat signal component, and calculate the human respiratory rate and heartbeat rate. In step S1, the echo signals from multiple channels of the human body are acquired and preprocessed. The method for merging the preprocessed multi-channel echo signals includes: The echo signals from multiple channels of the human body are acquired. After preprocessing, including removing DC bias and static object noise interference, the channel weights are calculated, and then the signals from multiple channels are merged according to the weights. In step S2, the method for obtaining a set of vital sign range cells containing multiple range cells by detecting vital signals within the range cells based on the merged echo signals includes: Extract the phase of all range cells of the echo signal after channel merging, calculate the phase parameter of each range cell, set the detection window length and parameter threshold, detect the vital signs within the range cells, and obtain a set of vital sign range cells containing multiple range cells; In step S3, the method for calculating the vital sign phase signal based on the set of vital sign distance units includes: The respiratory weight and heart rate weight of different distance units are calculated based on the set of vital sign distance units. Then, the signals in the set of vital sign distance units are merged according to the weight to obtain human respiratory signals and heart rate signals. Further merging is then performed to obtain vital sign phase signals.

2. The method for long-distance vital signal monitoring based on multi-channel according to claim 1, characterized in that, A method for acquiring multi-channel echo signals from the human body, performing preprocessing including removing DC bias and static object clutter interference, calculating channel weights, and then merging signals from multiple channels according to the weights includes: S11: Acquire multi-channel echo signals from the human body and store them in the original data matrix. In the given information, k = 1, 2, ..., K, m = 1, 2, ..., M, n = 1, 2, ..., N; where M represents the number of frames of the transmitted signal, N represents the number of sampling points in each frame of the transmitted signal, and K represents the number of channels. S12: After preprocessing the echo signals of multiple channels to remove DC bias and static object clutter interference, calculate the channel weights, and then merge the signals of multiple channels according to the weights, specifically as follows; S121: Calculate the DC bias compensation data matrix ; S122: Based on the DC bias compensation data matrix Calculate the mean matrix The formula is as follows: S123: According to the mean matrix Calculate peak vector For k=1,2,…,K, the formula is as follows: in, express Domain transformation; S124: Let the peak matrix ; where the peak matrix The formula for calculating the column vector is: ； Calculate the channel matrix The formula is as follows: Where H represents the conjugate transpose of the matrix; S125: Yes The decomposition is as follows: Among them, eigenvalues , It is the corresponding feature vector; S126: Set the channel weight ; S127: Calculate the normalized weights For k=1,2,…,K, the formula is as follows: S128: Based on the normalized weights Calculate the channel merging matrix The formula is as follows: 。 3. The method for long-distance vital signal monitoring based on multi-channel according to claim 2, characterized in that, In step S121, the DC bias compensation data matrix is ​​calculated. The methods include: S1211: Extract the original data matrix real part matrix and imaginary part matrix ; S1212: Set channel index ; S1213: Let the distance cell index ; S1214: Let the real part of the vector Imaginary part vector ; in, It is a column vector of length M; S1215: Construct the center matrix X and the radius vector y, as shown in the following formula: S1216: Solving DC parameters The formula is as follows: in, It is a vector of length 3; S1217: Calculation and The formula is as follows: S1218: Calculation The formula is as follows: in, Represents the imaginary unit; S1219: If Execute step S12110, otherwise let Execute step S1214; S12110: If Execute step S12111, otherwise let Execute step S1213; S12111: Obtain .

4. The method for long-distance vital signal monitoring based on multi-channel according to claim 1, characterized in that, The method for extracting the phase of all range cells in the echo signal after channel merging, calculating the phase parameters of each range cell, setting the detection window length and parameter threshold, detecting vital signs within the range cells, and obtaining a set of vital sign range cells containing multiple range cells includes: S21: Extract Phase of each distance unit m = 1, 2, ..., M; S22: Calculation Related functions and its domain transformation The formula is as follows: S23: Calculation parameters and The formula is as follows: in, This indicates the lower limit of human respiratory rate. This indicates the upper limit of human heart rate. Indicates the sampling frequency; S24: Calculate phase parameters For n=1,2,…,N, the formula is as follows: S25: Search The formula is as follows: S26: Calculate the mean of the phase parameters and standard deviation The formula is as follows: S27: Set the human target detection window length to L, let ; S28: Calculate the phase parameter threshold The formula is as follows: in, For adjustment factors; S29, if Execute step S210, otherwise let Proceed to step S28; S210, Order ; S211, if Execute step S212, otherwise let Proceed to step S28; S212, Order Vital signs distance unit set A total of 100,000 were detected There are vital signs signals in one distance unit.

5. A method for long-distance vital signal monitoring based on multi-channel according to claim 4, characterized in that, The method for calculating the respiratory weight and heart rate weight of different distance units based on the set of vital sign distance units, and then merging the signals in the set of vital sign distance units according to the weights to obtain human respiratory signals and heart rate signals, and further merging them to obtain vital sign phase signals includes: S31: Calculation domain transformation b = 1, 2, ..., B, the formula is as follows: S32: Calculate parameters The formula is as follows: in, and Indicates the upper and lower limits of human respiratory rate. and Indicates the upper and lower limits of human heart rate; S33: Calculation Components within the range of human respiratory rate and components within the heart rate range b = 1, 2, ..., B, the formula is as follows: S34: Determine the respiratory reference signal and heartbeat reference signal The formula is as follows: S35: Calculate the weighted respiratory value and heart rate weights The formula is as follows: in, Indicates the covariance of the two. Represents the variance of sequence x; S36: According to the aforementioned respiratory weights and the heartbeat weights By merging the signals from the vital sign distance unit set, the respiratory phase signal is obtained. and heartbeat phase signal The formula is as follows: S37: Obtaining the breathing untangling signal after untangling. Heartbeat untangling signal ; S38: Merging to obtain vital sign phase signals The formula is as follows: 。 6. The method for long-distance vital signal monitoring based on multi-channel according to claim 1, characterized in that, In step S4, the method for decomposing the vital sign phase signal into several components, analyzing the respiratory signal component and the heartbeat signal component, and calculating the human respiratory rate and heartbeat rate includes: S41: Calculate the vital signs phase signal differential signal The formula is as follows: ,m=1,2,…,M-1; S42: The bandpass phase signal is obtained after passing through a bandpass filter. m = 1, 2, ..., M; S43: Set the number of decompositions ; S44: Will Decomposed into Each signal component ; S45: Let the decompose index ; S46: Calculate peak frequency The formula is as follows: S47: If ,make , If the above steps are not executed, proceed to step S46; otherwise, proceed to step S48. S48: If ,make , If the above steps are not executed, proceed to step S46; otherwise, proceed to step S49. S49: If Execute step S410, otherwise let Execute step S46; S410: Estimating Human Respiratory Rate and heart rate The formula is as follows: 。 7. A method for long-distance vital signal monitoring based on multi-channel according to claim 6, characterized in that, In step S44, Decomposed into Each signal component The methods include: S441: Set Embedding Dimension ; S442: Constructing the trajectory matrix The formula is as follows: S443: Yes The decomposition is as follows: Where G is the singular value matrix, U is a left orthogonal matrix, and P is a right orthogonal matrix; S444: Extract the signal The main components are shown in the following formula: S445: Reconstruct the index ; S446: Calculate the signal reconstruction matrix The formula is as follows: S447: Reconstructing the... Each signal component is represented by the following formula: S448: If Execute step S449, otherwise let Execute step S446; S449: Obtain Each signal component .

Images