CN112401863A - Non-contact real-time vital sign monitoring system and method based on millimeter wave radar - Google Patents
Non-contact real-time vital sign monitoring system and method based on millimeter wave radar Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/0507—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves using microwaves or terahertz waves
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
Abstract
The invention discloses a non-contact real-time vital sign monitoring system and a non-contact real-time vital sign monitoring method based on a millimeter wave radar; the monitoring system includes: the system comprises a millimeter wave transceiver module, a real-time signal acquisition module, a real-time signal processing module and a visualization module; the real-time signal processing module is used for extracting a respiratory signal and a heart rate signal through clutter suppression; the visualization module is used for carrying out waveform fitting on the respiration signal and the heart rate signal and displaying the position, the respiration and the heart rate of the testee in real time through a visualization interface. The invention effectively overcomes the influence of environmental noise through clutter suppression, and improves the precision of a monitoring system; in addition, fitting is carried out on the respiration signal and the heart rate signal through an iterative algorithm, and related waveforms and monitoring results are visually displayed.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a millimeter-wave radar-based non-contact real-time vital sign monitoring system and method.
Background
Non-contact vital sign monitoring mainly adopts technologies such as WiFi, ultra-wideband radar, millimeter wave radar and the like, and monitors vital signs (such as respiration, heart rate and the like) of a human body by monitoring the periodic change of a human body reflection signal. The non-contact vital sign monitoring based on the millimeter wave radar has the advantages of small transmitting power, low cost, high precision and the like, and gradually becomes a popular research field in academia and industry in recent years.
A non-contact vital sign monitoring system based on a millimeter wave radar detects mm-level periodic micro displacement of a chest cavity caused by respiration (inspiration/expiration) and heart beating of a human body by transmitting and receiving millimeter waves, and monitors respiration and heart rate. Specifically, the monitoring system measures the time it takes for the signal to reflect back from the body after a low power consumption millimeter wave is transmitted. Taking breathing as an example, when a human body inhales, the chest of the human body expands and moves forwards, so that the reflecting time is reduced; otherwise, the reflection time is increased. By measuring and analyzing the periodic variation of the thoracic cavity, the waveform and the frequency of respiration and heart rate can be extracted, and the monitoring of the vital signs of the human body is realized.
But the prior art has a plurality of defects. The main body is as follows: firstly, the existing system is easily interfered by various noises from the environment, and the stability is poor; secondly, most of the existing systems have certain defects, or cannot export and visually display respiration and heart rate data in real time, or cannot accurately display related vital sign monitoring results in real time.
Therefore, the construction of a non-contact vital sign monitoring system with good real-time performance, strong robustness and high accuracy becomes an urgent problem to be solved in the field.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a non-contact real-time vital sign monitoring system and a non-contact real-time vital sign monitoring method based on a millimeter wave radar, and aims to solve the problems that the vital sign monitoring is not high in accuracy and poor in visualization effect due to the fact that clutter suppression is lacked, so that the vital sign monitoring is easily interfered by environmental noise in the prior art.
The invention provides a non-contact real-time vital sign monitoring system based on a millimeter wave radar, which comprises: the system comprises a millimeter wave transceiver module, a real-time signal acquisition module, a real-time signal processing module and a visualization module; the input end of the real-time signal acquisition module is connected to the output end of the millimeter wave transceiving module, the first output end of the real-time signal acquisition module is connected to the input end of the millimeter wave transceiving module, the second output end of the real-time signal acquisition module is connected to the input end of the real-time signal processing module, and the input end of the visualization module is connected to the output end of the real-time signal processing module; the millimeter wave transceiver module is used for transmitting millimeter waves and receiving echo signals of the millimeter waves; the real-time signal acquisition module is used for acquiring millimeter wave signals in real time and packaging and outputting echo signals; the real-time signal processing module is used for extracting a respiratory signal and a heart rate signal through clutter suppression; the visualization module is used for carrying out waveform fitting on the respiration signal and the heart rate signal and displaying the position, the respiration and the heart rate of the testee in real time through a visualization interface.
Further, the real-time signal acquisition module comprises: the parameter adjusting unit is used for determining the detection range of the millimeter wave radar; the data capturing unit is used for monitoring the UDP port and capturing the UDP data packet in real time; the data transmission unit is used for periodically splicing, packaging and transmitting the newly added data; and a sliding window setting unit for setting a sliding window.
Further, the maximum monitoring distance in the detection range is
Distance resolution of
Wherein c is the speed of light 3 × 108/，FsFor sampling frequency of sample point on chirp signal，KslopeIs the slope of the chirp signal and B is the swept bandwidth of the chirp signal.
Further, the real-time signal processing module includes: the clutter suppression unit is used for processing data in the window to realize clutter interference suppression and echo selection; the band-pass filtering unit is used for extracting the respiratory signal and the heart rate signal through band-pass filtering; and the vital sign calculation unit is used for extracting the respiratory frequency and the heartbeat frequency by a frequency domain analysis method.
Further, the clutter suppression unit includes: a first unit configured to suppress stationary noise within a millimeter wave measurement range; and the second unit is used for suppressing non-stationary noise in the millimeter wave measurement range.
Still further, the vital signs calculation unit comprises: the respiratory frequency calculation unit is used for searching a peak of a respiratory signal through a time domain peak searching algorithm and obtaining the respiratory frequency through calculating the frequency of the peak; and the heartbeat frequency calculating unit is used for calculating the power spectral density of the sequence by adopting a modified periodogram power spectral density estimation method so as to estimate the heartbeat frequency, and eliminating frequency domain offset by adopting fitting so as to further optimize and calibrate the heartbeat frequency.
The invention also provides a non-contact real-time vital sign monitoring method based on the millimeter wave radar, which comprises the following steps:
transmitting millimeter waves and receiving echo signals reflected by the millimeter waves;
performing clutter suppression and echo selection on the echo signal, and then extracting a respiratory signal and a heart rate signal;
and fitting the respiration signal and the heart rate signal to realize real-time visual display of the signal and the detection result.
Furthermore, performing clutter suppression on the echo signal specifically includes: the suppression of stationary noise in the millimeter wave measurement range is realized through adaptive background subtraction of weighting coefficient fitting; and the suppression of non-stationary noise in the millimeter wave measurement range is realized through singular value decomposition.
Further, the echo selection is specifically: signals related to vital signs are picked out from the echo signals.
Furthermore, the respiratory signal and the heart rate signal are extracted in a band-pass filtering mode, a peak of the respiratory signal is searched by adopting a time domain peak searching algorithm, and the respiratory frequency is obtained by calculating the frequency of the peak; and calculating the power spectral density of the sequence by adopting a modified periodogram power spectral density estimation method so as to estimate the heartbeat frequency, and eliminating frequency domain offset by adopting fitting so as to further optimize and calibrate the heartbeat frequency.
Compared with the prior art, the invention has the following obvious outstanding characteristics and obvious technical progress:
(1) the invention effectively overcomes the influence of environmental noise and improves the precision of the monitoring system by clutter suppression.
(2) The invention realizes the real-time acquisition, real-time analysis and visualization of data; specifically, UDP data is captured in real time through a Socket module and is transmitted back to an upper computer, and real-time data acquisition is achieved; real-time detection of human vital signs is realized through time-frequency domain analysis; fitting the respiration signal and the heart rate signal through an iterative algorithm, and visually displaying the relevant waveform and the monitoring result.
Drawings
Fig. 1 is a schematic block diagram of a non-contact real-time vital sign monitoring system based on a millimeter wave radar according to an embodiment of the present invention.
Fig. 2 is a flowchart of an implementation of the millimeter-wave radar-based non-contact real-time vital sign monitoring method according to the embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a millimeter wave transceiver module in the millimeter wave radar-based non-contact real-time vital sign monitoring system according to the embodiment of the present invention.
Fig. 4 is a schematic diagram of an FMCW radar transmitting signal and an echo signal according to an embodiment of the present invention.
Fig. 5 is a block diagram of a sliding window provided by an embodiment of the present invention.
Fig. 6 is a graph showing the experimental results of the system and method according to the embodiment of the present invention, wherein (a) is a graph showing the respiration, heart rate and ECG control waveforms of the first subject, (b) is a graph showing the respiration, heart rate and ECG control waveforms of the second subject, and (c) is a graph showing the respiration, heart rate and ECG control waveforms of the third subject.
Fig. 7 is a fitted visualization interface provided in an embodiment of the present invention, in which (a) a real-time detection result diagram of the respiration rate and the measured subject distance and the respiration original waveform and the fitted waveform of the respiration of the subject, and (b) a real-time detection result diagram of the heart rate and the fitted waveform of the heart rate of the subject, and the heart beat frequency are shown.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention effectively eliminates stationary noise and non-stationary noise from the environment by clutter suppression on the basis of real-time signal acquisition; the position of the tested person is dynamically tracked through echo selection, and the precision of vital sign detection is improved; frequency domain offset of the heart rate signal is effectively eliminated through calibration optimization, and accurate estimation of the heartbeat frequency is achieved; and performing high-quality fitting reconstruction on the heart rate and the respiratory signals in real time through an iterative algorithm.
Fig. 1 shows a schematic block diagram of a non-contact real-time vital signs monitoring system based on millimeter wave radar according to an embodiment of the present invention, and for convenience of illustration, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
the real-time, stable and high-precision non-contact vital sign monitoring system provided by the invention comprises: the system comprises a millimeter wave transceiver module 1, a real-time signal acquisition module 2, a real-time signal processing module 3 and a visualization module 4; wherein, the input of real-time signal acquisition module 2 is connected to the output of millimeter wave transceiver module 1, the first output of real-time signal acquisition module 2 is connected to the input of millimeter wave transceiver module 1, the second output of real-time signal acquisition module 2 is connected to the input of real-time signal processing module 3, the input of visualization module 4 is connected to the output of real-time signal processing module 3, millimeter wave transceiver module 1 transmits the millimeter wave and receives its echo signal, its echo signal is packed and passed to the host computer in real time by real-time signal acquisition module 2, handle and the analysis through real-time signal processing module 3 afterwards, carry out waveform fitting and visual show by visualization module 4 at last.
The millimeter wave transceiver module 1 is mainly used for transmitting millimeter waves and receiving millimeter wave echo signals. The method mainly comprises the following steps: the system comprises three parts, namely millimeter wave radar receiving and transmitting, high-precision AD conversion and digital signal processing. The millimeter wave radar transmitting and receiving part adopts an MIMO antenna technology, comprises 2 paths of transmitting antennas Tx1 and Tx2 and 4 paths of receiving antennas Rx1, Rx2, Rx3 and Rx4, and respectively consists of parallel microstrip antennas. Each transmitting antenna has independent phase and amplitude control and can transmit 77GHz-81GHz chirp; each receiving antenna can be operated independently or simultaneously. A high-precision AD conversion section performs high-precision analog-to-digital conversion of 16 bits on a signal received by a reception antenna. The digital signal processing part adopts FPGA or DSP to preprocess the echo signal.
The real-time signal acquisition module 2 is used for setting millimeter wave parameters and executing real-time capturing, storing and splicing operation of data. Firstly, adjusting parameters and determining the detection range of the millimeter wave radar; monitoring a UDP port through a Socket module, capturing a UDP data packet in real time and storing original data in an upper computer; thirdly, setting a timer, periodically splicing and packaging the newly added data and transmitting the newly added data to a real-time signal processing module; finally, a sliding window is set to prepare for subsequent signal processing.
The real-time signal processing module 3 is used for extracting a respiration signal and a heart rate signal; preprocessing data in a sliding window, suppressing clutter interference and selecting echoes; then, performing band-pass filtering operation to extract a respiratory signal and a heart rate signal; and finally, on the basis of power spectral density estimation, eliminating the offset of a signal frequency domain by fitting by adopting an improved frequency domain analysis method, and extracting the heartbeat frequency.
The visualization module 4 is used for realizing visualization of real-time data and monitoring results thereof. Fitting the respiration signal and the heart rate signal by adopting an iterative algorithm, and storing the fitting result in real time; and displaying (i) the position, (ii) the respiration and heart rate waveforms, (iii) the respiration rate and the heart rate of the subject in real time through a visual interface.
The invention can solve the problems of clutter suppression and signal fitting. First, the echo signal of the millimeter wave may include various clutter interferences, such as stationary noise from static objects (reflected signals) such as tables and walls, non-stationary noise from moving objects (reflected signals), and the like, thereby causing great trouble to vital sign monitoring, especially heart rate monitoring. In the conventional clutter suppression, the ambient noise in a specific environment is generally collected, and then the ambient noise is subtracted from the millimeter wave echo signal to eliminate static clutter interference. However, the method has no universality, cannot adapt to the change of the environment, and has poor clutter suppression effect. In order to adaptively and more effectively suppress clutter, the invention designs adaptive filtering algorithms respectively aiming at stationary noise and non-stationary noise on the basis of environmental noise analysis, and suppresses various noises from the environment. Secondly, since vital sign monitoring, especially heart rate monitoring, requires weak mm-level useful signals to be extracted from strong noise and strong interference, which has extremely high requirements on back-end signal processing algorithms. The traditional signal reconstruction/fitting method adopts wavelet transform and other methods to perform simple band-pass filtering, or adopts EEMD (ensemble empirical mode decomposition) and other methods to perform reconstruction. However, in general, the former has poor filtering effect, and the latter has difficulty in universality and adaptivity in selecting modal components, that is: for subjects with a low or high heart rate, it is difficult for EEMD to adaptively select a suitable modal component or combination of modal components. On the basis of analysis and comparison of a plurality of traditional algorithms, the invention provides a waveform fitting method based on an iterative algorithm, and high-quality fitting reconstruction is carried out on heart rate and respiratory signals in real time.
Fig. 2 shows a flowchart of an implementation of the millimeter-wave radar-based non-contact real-time vital sign monitoring method according to the embodiment of the present invention, which is detailed as follows:
the non-contact real-time vital sign monitoring method based on the millimeter wave radar comprises the following steps:
(1) the millimeter wave transceiver module realizes millimeter wave transceiving, high-precision AD conversion and digital signal processing operation.
The specific treatment process is as follows: the present invention adopts FMCW (Frequency Modulated Continuous pulse) millimeter Wave radar, and a transceiver module thereof is shown in fig. 3, and the transceiver module comprises 2 paths of transmitting antennas Tx and 4 paths of receiving antennas Rx. The millimeter wave radar transmission and echo signals are shown in fig. 4.
And (1-1) a transmitting terminal. As shown in fig. 3, the oscillator provides a reference signal, the reference signal passes through a phase-locked loop to generate a fundamental frequency signal, the fundamental frequency signal passes through a frequency multiplier to obtain a Chirp signal (Chirp), and the Chirp signal passes through a power amplifier and is transmitted by a transmitting antenna. The carrier wave of the transmitted signal is a sawtooth wave, as shown in FIG. 4, with a period TfThe bandwidth of the frequency modulation band is B; the frame period (i.e. the period of the sawtooth wave repetition) being Ti. The expression of the transmitted signal is:
in the formula ATxIs the amplitude of the transmitted signal, KslopeIs the slope of the sawtooth wave, fcFor the center frequency of the radar transmitted signal,
is the initial phase of the transmitted signal.
And (1-2) receiving the data. As shown in fig. 3, the receiving antenna RXReceiving and processing millimeter wave echo signals (namely, signals reflected by objects such as a human body and the like irradiated by the millimeter wave radar). The specific process comprises the following steps: the echo signal is orthogonally mixed with the original transmitting signal after passing through a low noise amplifier, a high-frequency signal is filtered by a low-pass filter to obtain an intermediate-frequency signal, the intermediate-frequency signal is subjected to AD conversion after passing through an intermediate-frequency amplifier to obtain a digital signal, and then the related digital signal is sent to a high-precision FPGA or DSP module for preprocessing by a microcontroller (main control unit). The process isThe process can be described by the following formula,
(1-2-1) the expression of the echo signal is: sRx(t)＝ARxSTx(t-τ)......(2-1)；
In the formula ARxFor echo signal amplitude, ARxInversely proportional to the distance of the millimeter wave radar to the target; tau is the delay of the radar echo signal,
where c is the speed of light, d0Is the center distance of the millimeter wave radar to the target chest movement, Arsin(2πfrt) and Ahsin(2πfht) thoracic displacement due to respiration and heartbeat, respectively, ArAnd AhMaximum displacement of the thoracic cavity, f, caused by respiration and heartbeat of the human body, respectivelyrAnd fhRespiratory and heartbeat frequencies.
(1-2-2) mixing the echo signal with the original transmission signal,
after low-pass filtering, the intermediate frequency signal in the formula is obtained, and the frequency f of the intermediate frequency signalIF＝2πKslopeτ, phase
(2) The real-time signal acquisition module is used for setting radar parameters, executing real-time capture, storage and splicing operation of millimeter wave echo signals, and setting a sliding window to prepare for rear-end signal processing. The specific treatment process is as follows:
(2-1) radar parameter setting: the millimeter waves have non-contact property and non-interference property, can penetrate through materials such as plastics, dry walls and clothes, and have a wide measurable range. Its maximum detection distance dmaxAnd distance resolution dresCan be flexibly adjusted by modifying the relevant parameters of the millimeter wave radar,
wherein c is the speed of light 3 × 108m/s，FsIs the sampling frequency, K, of the sample points on ChirpslopeThe slope of Chirp and B the sweep bandwidth of Chirp are shown in fig. 4. For example: if Fs＝2×106sps，Kslope70MHz/us, B4 GHz (where sps is the number of samples per second), then dmax＝4.29m，dres＝3.76cm。
And (2-2) capturing, storing and splicing data. And monitoring a UDP port through a Socket module, capturing a UDP data packet in real time and storing original data in an upper computer. Then, a timer is set, when t is equal to tstepAnd when the time is long (for example, the time t is equal to 1s), splicing and packaging the newly added data and transmitting the newly added data to the real-time signal processing module.
And (2-3) sliding the window. Setting a sliding window, wherein the length and the step length of the window are respectively twindowAnd tstep. For example: when t iswindow30s and tstepWhen 1s, the block diagram is shown in fig. 5.
(3) And performing clutter suppression, echo selection and band-pass filtering operation on data in the sliding window through a real-time signal processing module, extracting a respiratory signal and a heart rate signal, and calculating the frequency of the respiratory signal and the heart rate signal.
The specific treatment process is as follows:
and (3-1) clutter suppression. Various clutter interferences may be included in the echo signal of the millimeter wave, such as: stationary noise from static objects (reflected signals) such as tables, walls, etc., non-stationary noise from moving objects (reflected signals), etc. The center frequency of the clutter power spectrum is close to zero frequency and close to the thoracic vibration frequency caused by human respiration and heartbeat (respiration is 0.1 Hz-0.6 Hz, heart rate is 0.8 Hz-2 Hz), and the two frequencies are easy to be mixed, so that the monitoring of human vital signs is greatly interfered, and therefore clutter signals are indispensable to inhibit. If used separately
Q represents the original echo signal, the echo signal after filtering stationary clutter, and the echo signal after filtering non-stationary clutter, all of which are nxm matrices, then the filtering process can be expressed as follows:
(3-1-1) suppression of stationary clutter. The invention adopts self-adaptive background subtraction based on weighting coefficient fitting to inhibit stable noise in a millimeter wave measurement range. As shown in fig. 4, at some slow time tnThe filtering of stationary clutter may be expressed as:
wherein
And Bn(m) characterizing t respectivelynThe original echo signal at the moment, the echo signal after filtering the stable noise and the background noise estimation are M1Is measured. Wherein the content of the first and second substances,
where λ is a weighting coefficient, λ ∈ [0, 1], and λ ═ 0.95, for example.
(3-1-2) suppression of non-stationary clutter. The invention adopts Singular Value Decomposition (SVD) to inhibit non-stationary noise in the millimeter wave measurement range. Firstly, the signal matrix is formed
The decomposition into an orthogonal matrix is carried out,
wherein U and y are NxN and MxM unitary matrices, respectively, and H represents a conjugate transpose; sigma is an N x M diagonal matrix comprising
The singular value of (a). Then, the maximum singular value lambda is divided in the diagonal matrix sigmamaxSetting all singular values except the singular values to be 0 to obtain a diagonal matrix
And finally, the signal is reconstructed,
in the formula, Q is the signal after filtering the non-stationary noise wave.
And (3-2) echo selection. And accurately positioning the distance of the subject, and selecting an echo signal of the distance unit, wherein the echo signal contains periodic variation thinking of the chest cavity of the subject caused by breathing and heartbeat.
Echo selection refers to the extraction of the original signal related to the vital signs of the subject from the ambient noise while accurately locating the subject range unit. The specific operation is as follows: first, a one-dimensional FFT is performed on each row of the matrix Q to obtain an NxM distance matrix R. Each column of the matrix R characterizes a distance cell, as shown in fig. 4. For example: the distance unit represented by the m column is m x dres，dresFor distance resolution (e.g. d)res3.76cm), see formula (4). Secondly, the energy sum on each distance unit is calculated,
wherein M is ∈ [1, M ∈]. Thirdly, find the column where the maximum energy sum max (E (m)) is located, and index the column as mmaxThe distance cell characterized in this column is the test distance of the subject. Fourth, its mth is extracted from the matrix QmaxThe column signal, calculating the phase and performing a phase unwrapping operation, the result being denoted as the sequence x (N), N ∈ [1, N [ ]]。
And (3-3) band-pass filtering. The band-pass filter is designed to extract the respiratory signal and the heart rate signal respectively. The method for performing band-pass filtering by using wavelet transform specifically comprises the following steps: first, calculate the wavelet transform of x (n), DWT { x (n) }, then perform band-pass filtering in the wavelet domain,
wherein the pass band of respiration and heart rateL，fH]Respectively as follows: [0.1-0.6]Hz and [0.8-2.5]Hz. Last through inverse smallThe wave transformation extracts the heart rate signal and the respiration signal, respectively. This process can be described using the following formula:
and (3-4) calculating vital signs. Processing the sequence x (n) within a sliding window (comprising the filtered respiratory signal sequence x)br(n) and a heart rate signal sequence xhr(n)), calculating a respiratory frequency and a heartbeat frequency, respectively; as shown in fig. 2:
(3-4-1) respiratory rate calculation:
respiratory signal x is searched by adopting time domain peak searching algorithm findpeaksbr(n) and calculating the frequency thereof.
(3-4-2) heartbeat frequency calculation:
sequence x is calculated by Welch (modified periodogram power spectral density estimation)hr(n) power spectral density, preliminary estimate of frequency of heart beats; and calibrating and optimizing the heartbeat frequency by adopting an improved frequency domain analysis algorithm.
The method specifically comprises the following steps: (3-4-2-1) spectral estimation: sequence xhrThe power spectral density of (n) can be calculated by:
wherein, FFT { xhr(n) is the sequence xhr(N) Fourier transform, N ∈ [1, N ∈]. In order to reduce errors, the invention adopts a sectional averaging method to measure the power spectral density PhrSmoothing is performed, namely: will sequence xhr(n) dividing the signal into L small segments, each small segment comprises W sampling points, respectively estimating the power spectrum of each small segment, and taking the average value as the whole sequence xhr(n) power spectrum estimation. Wherein N is equal to or less than L × W, and if L segments do not overlap with each other, N is L × W. Maximum value of power spectral density PmaxThe corresponding frequency is the preliminarily estimated heartbeat frequency fhr0。
(3-4-2-1) calibration optimization: firstly x ishr(n) performing FFT processing, and searching a frequency domain peak of the FFT processing by using findpeaks; select and fhr0Wave of closest valueThe peak is taken as a main peak of a frequency domain; selecting a frequency domain main peak and two wave peaks on the left side and the right side of the frequency domain main peak as useful data; and set other data to 0; performing IFFT to obtain reconstructed time domain signal and calculating its slope kxslope. The heartbeat per minute frequency can be obtained by:
the method can accurately eliminate the offset of the signal frequency domain and carry out calibration optimization on the heartbeat frequency.
(4) The respiration signal and the heart rate signal are fitted, so that real-time and visual display of the signals and detection results is realized.
The specific treatment process is as follows:
(4-1) iterative fitting:
for the respiratory signal and heart rate signal sequence (i.e. x.br(n) and xhr(n)) fitting a quasi-sine function, and storing the quasi-sine function to an upper computer in real time,
wherein, b1～b4For the parameters, fitting is performed in an iterative algorithm. b1，b2，b3，b4Respectively, the amplitude factor, the translation factor, the scaling factor and the offset factor of the sine-like function.
And (4-2) visualization. And displaying the position of the subject, the waveforms of the respiration and the heart rate of the subject, the respiratory frequency and the heart rate of the subject in real time through a visual interface. In order to verify the reliability of the vital sign monitoring system and method provided by the present embodiment, a plurality of subjects are recruited to participate in the test with the duration of 100 seconds. Three representative subjects are selected, the heart rates of the three subjects are respectively positioned in three different frequency intervals, namely a high frequency interval, a middle frequency interval and a low frequency interval, the heart rates and the respiration waveforms are shown in fig. 6, and (a), (b) and (c) respectively show visual interfaces of the waveforms in the test process of the three subjects. The results are shown in Table 1, and it is clear from Table 1 that: the detection result of the embodiment is highly consistent with the detection result of the wearable ECG device (synchronously acquired in the experimental process) in any section. The embodiment updates and displays the distance of the subject and the respiration and heart rate waveforms once per second, thereby realizing the real-time detection and accurate measurement of the vital signs. The visualization results are shown in fig. 7, where (a) and (b) respectively show the heart rate and respiratory signal after updating fitting every minute, and the real-time detection results, including: position of subject, number of heartbeats per minute and number of breaths. Therefore, the embodiment realizes real-time monitoring and accurate measurement of the vital signs.
TABLE 1 Vital sign monitoring results
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A non-contact real-time vital sign monitoring system based on millimeter wave radar, comprising: the system comprises a millimeter wave transceiver module (1), a real-time signal acquisition module (2), a real-time signal processing module (3) and a visualization module (4);
the input end of the real-time signal acquisition module (2) is connected to the output end of the millimeter wave transceiver module (1), the first output end of the real-time signal acquisition module (2) is connected to the input end of the millimeter wave transceiver module (1), the second output end of the real-time signal acquisition module (2) is connected to the input end of the real-time signal processing module (3), and the input end of the visualization module (4) is connected to the output end of the real-time signal processing module (3);
the millimeter wave transceiver module (1) is used for transmitting millimeter waves and receiving echo signals of the millimeter waves;
the real-time signal acquisition module (2) is used for acquiring millimeter wave signals in real time and packaging and outputting the echo signals;
the real-time signal processing module (3) is used for extracting a respiratory signal and a heart rate signal through clutter suppression;
the visualization module (4) is used for carrying out waveform fitting on the respiration signal and the heart rate signal and displaying the position, the respiration and the heart rate of the subject in real time through a visualization interface.
2. The contactless real-time vital signs monitoring system according to claim 1, wherein the real-time signal acquisition module (2) comprises:
the parameter adjusting unit is used for determining the detection range of the millimeter wave radar;
the data capturing unit is used for monitoring the UDP port and capturing the UDP data packet in real time;
the data transmission unit is used for periodically splicing, packaging and transmitting the newly added data;
and a sliding window setting unit for setting a sliding window.
3. The non-contact real-time vital signs monitoring system of claim 2, wherein the maximum monitoring distance in the detection range is
Distance resolution of
Wherein c is the speed of light 3 × 108m/s，FsIs the sampling frequency, K, of a sample point on a chirp signalslopeIs the slope of the chirp signal and B is the swept bandwidth of the chirp signal.
4. The contactless real-time vital signs monitoring system according to any of the claims 1-3, wherein the real-time signal processing module (3) comprises:
the clutter suppression unit is used for processing data in the window to realize clutter interference suppression and echo selection;
the band-pass filtering unit is used for extracting the respiratory signal and the heart rate signal through band-pass filtering;
and the vital sign calculation unit is used for extracting the respiratory frequency and the heartbeat frequency by a time-frequency domain analysis method.
5. The contactless real-time vital signs monitoring system of claim 4, wherein the clutter suppression unit comprises:
a first unit configured to suppress stationary noise within a millimeter wave measurement range;
and the second unit is used for suppressing non-stationary noise in the millimeter wave measurement range.
6. The contactless real-time vital signs monitoring system according to claim 4 or 5, wherein the vital signs calculation unit comprises:
the respiratory frequency calculation unit is used for searching a peak of a respiratory signal through a time domain peak searching algorithm and obtaining the respiratory frequency through calculating the frequency of the peak;
and the heartbeat frequency calculating unit is used for calculating the power spectral density of the sequence by adopting a modified periodogram power spectral density estimation method so as to estimate the heartbeat frequency, and eliminating frequency domain offset by adopting fitting so as to further optimize and calibrate the heartbeat frequency.
7. A non-contact real-time vital sign monitoring method based on a millimeter wave radar is characterized by comprising the following steps:
transmitting millimeter waves and receiving echo signals reflected by the millimeter waves;
performing clutter suppression and echo selection on the echo signal, and then extracting a respiratory signal and a heart rate signal;
and fitting the respiration signal and the heart rate signal to realize real-time visual display of the signal and the detection result.
8. The non-contact real-time vital signs monitoring method of claim 7, wherein the clutter suppression of the echo signal is specifically:
the suppression of stationary noise in the millimeter wave measurement range is realized through adaptive background subtraction of weighting coefficient fitting;
and the suppression of non-stationary noise in the millimeter wave measurement range is realized through singular value decomposition.
9. The non-contact real-time vital signs monitoring method according to claim 7 or 8, wherein the echo selection is in particular: signals related to vital signs are picked out from the echo signals.
10. The non-contact real-time vital sign monitoring method according to any one of claims 7 to 9, wherein the respiration signal and the heart rate signal are extracted by band-pass filtering, a peak of the respiration signal is found by using a time-domain peak-finding algorithm, and a respiration rate is obtained by calculating a frequency of the peak; and calculating the power spectral density of the sequence by adopting a modified periodogram power spectral density estimation method so as to estimate the heartbeat frequency, and eliminating frequency domain offset by adopting fitting so as to further optimize and calibrate the heartbeat frequency.
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| PCT/CN2020/131783 WO2022104868A1 (en) | 2020-11-19 | 2020-11-26 | Millimeter wave radar-based non-contact real-time vital sign monitoring system and method |
| ZA2023/05013A ZA202305013B (en) | 2020-11-19 | 2023-05-05 | Millimeter wave radar-based non-contact real-time vital sign monitoring system and method |
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