CN116077044A

CN116077044A - Vital sign detection method based on millimeter wave radar

Info

Publication number: CN116077044A
Application number: CN202211546883.7A
Authority: CN
Inventors: 梁庆真; 周杨; 张彭豪
Original assignee: Sichuan Qiruike Technology Co Ltd
Current assignee: Sichuan Qiruike Technology Co Ltd
Priority date: 2022-12-05
Filing date: 2022-12-05
Publication date: 2023-05-09

Abstract

The invention relates to the field of radar signal detection, in order to improve detection accuracy, a vital sign detection method based on millimeter wave radar comprises the following steps: 1. acquiring an original signal of respiration and heartbeat overlapping; 2. the denoising method based on phase space reconstruction is adopted to carry out signal separation on the original signal so as to obtain a respiratory signal and a heartbeat signal, and the method specifically comprises the following steps: 21. reconstructing the phase space of respiration and heartbeat respectively by adopting different embedding dimensions and time delays to obtain a high-dimensional phase space; 22. a zero subspace in Gao Weixiang space represents noise components, an attractor space represents respiratory signals or heartbeat signal components, the zero subspace is projected to the attractor space continuously to eliminate the noise components, and the projected phase points are corrected; 23. carrying out weighted anti-reconstruction processing on all corrected phase points to obtain respiratory signals and heartbeat signals; 3. the respiration signal and the heartbeat signal are analyzed to obtain a respiration value and a heartbeat value. By adopting the mode, the detection accuracy is improved.

Description

Vital sign detection method based on millimeter wave radar

Technical Field

The invention relates to the field of radar signal detection, in particular to a vital sign detection method based on millimeter wave radar.

Background

The non-contact breath heartbeat detection mode based on the millimeter wave radar has the following advantages: 1. the respiration and heartbeat detection is non-contact, and the comfort is not affected. 2. The millimeter wave detection accuracy is very high, and can reach the detection accuracy of <1 mm. 3. The sensor is very small in size and very simple to install. 4. The power consumption is very low, and the method is suitable for long-term vital sign monitoring. 5. Distance, speed and angle detection can be performed, and a positioning function is realized. However, the prior art has the problem of low detection accuracy in the process of processing respiratory and heartbeat signals.

Disclosure of Invention

In order to improve detection accuracy, the application provides a vital sign detection method based on millimeter wave radar.

The invention solves the problems by adopting the following technical scheme:

the vital sign detection method based on the millimeter wave radar comprises the following steps:

step 1, acquiring an original signal of respiration and heartbeat overlapping;

step 2, performing signal separation on an original signal by adopting a denoising method based on phase space reconstruction to obtain a respiratory signal and a heartbeat signal, which specifically comprises the following steps:

step 21, reconstructing the phase space of respiration and heartbeat respectively by adopting different embedding dimensions and time delays to obtain a high-dimensional phase space;

step 22, gao Weixiang, the zero subspace in the space represents the noise component, the suction subspace represents the respiratory signal or the heartbeat signal component, the zero subspace is projected to the suction subspace continuously to eliminate the noise component, and the projected phase point is corrected;

step 23, carrying out weighted anti-reconstruction processing on all corrected phase points to obtain respiratory signals and heartbeat signals;

and 3, analyzing the respiration signal and the heartbeat signal to obtain a respiration value and a heartbeat value.

Further, the specific step of acquiring the original signal in the step 1 is: transmitting electromagnetic wave signals to the space range to be detected through a millimeter wave radar, receiving echo signals, and processing the echo signals to obtain original signals of respiration and heartbeat overlapping; the echo signal processing specifically includes:

step 11, preprocessing echo signals;

step 12, performing Fourier transform on the preprocessed echo signals to obtain a distance unit of the target;

step 13, determining the phase of the target according to the distance unit where the target is located;

and 14, performing phase unwrapping and phase difference on the phase of the target to obtain an original signal of the respiration heartbeat overlapping.

Further, the preprocessing in step 11 includes: removing static clutter and/or removing direct current interference.

Further, in the step 13, the method for determining the distance unit where the target is located is as follows: and accumulating all the distance units in the unit processing period, wherein the distance unit with the largest energy is the distance unit where the target is located.

Further, in the step 14, the phase unwrapping means: when the phase difference value between two adjacent moments is larger than pi or smaller than-pi, carrying out + -2 pi on the value of the latter moment; phase difference refers to: the relative change value of the phase after phase unwrapping compared to the first frame phase.

Further, in the step 21, a mutual information method is used to calculate the time delay of the phase space reconstruction, and a Cao algorithm is used to calculate the embedding dimension.

Further, in the step 23, the weight function adopts a normalized hanning window when the weight reconstruction is performed.

Further, the step 3 specifically includes:

step 31, performing time domain analysis on the respiration signals to judge whether an apnea or breath hold situation exists; carrying out frequency domain analysis on the respiration signals to obtain respiration values per minute;

and step 32, carrying out frequency domain analysis on the heartbeat signal to obtain a heartbeat value per minute.

Further, the step 31 specifically includes:

step 311, calculating the peak value of the respiratory signal, averaging the peak value, and marking as M;

step 312, determining the magnitude relation between M and the threshold G, if M is greater than G, entering step 313, otherwise determining that the patient is apnoea or breath-hold, i.e. the breath is 0, and starting to determine the next signal;

step 313, obtaining the mean square error of the respiratory signal;

step 314, performing Fourier transform on the respiratory signal, and finding out the frequency p of the peak value after the Fourier transform;

step 315, setting a threshold T, and calculating a respiration value of 60×p× (FS/fftNum) when the mean square error is greater than or equal to the threshold T, where FS is the signal sampling frequency and fftNum is the length of the fourier transform; when the mean square error is less than the threshold T, then the breath is considered to be 0.

Further, the step 32 specifically includes:

step 321, calculating breathing harmonic according to the breathing value calculated in the step 31;

step 322, performing respiratory harmonic reduction processing on the heartbeat signal;

step 323, performing fourier transform on the heartbeat signal after the respiratory harmonics are reduced, and finding out the frequency f of the peak value after the fourier transform;

step 324, the heart rate value is 60 xf× (FS/fftNum), where FS is the signal sampling frequency and fftNum is the length of the fourier transform.

Compared with the prior art, the invention has the following beneficial effects: according to the invention, the denoising method based on phase space reconstruction is adopted to perform signal separation on the original signal so as to obtain the respiratory signal and the heartbeat signal, so that the effect of a filter is realized, the retention of weak signal components is realized, and the detection accuracy is improved.

Drawings

FIG. 1 is a flow chart of a vital sign detection method based on millimeter wave radar;

FIG. 2 is a flow chart of echo signal processing;

fig. 3 is a flow chart of processing an original signal.

Detailed Description

The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

As shown in fig. 1, the vital sign detection method based on millimeter wave radar includes:

step 1, acquiring an original signal of respiration and heartbeat overlapping;

Specifically, as shown in fig. 2, in this embodiment, the specific steps for acquiring the original signal are as follows: transmitting electromagnetic wave signals to the space range to be detected through a millimeter wave radar, receiving echo signals, processing the echo signals to obtain original signals overlapped by respiration and heartbeat, and processing the echo signals specifically comprises the following steps:

step 11, preprocessing the echo signals, including removing static clutter and/or removing direct current interference.

Under experimental conditions, clutter created by stationary objects within each range bin is substantially unchanged, while respiratory heartbeat motion approximates sinusoidal motion with a sum of zero. Based on this condition, the same distance units of all echo pulses are averaged, which can be considered as stationary clutter within the target unit. Subtracting the line average value from each line of the echo matrix, namely eliminating the interference of clutter generated by static objects on target echo. The radar echo signal is subjected to quadrature sampling to obtain an IQ complex signal, the distribution of the time domain IQ signal on the complex plane is along with the dispersion of the amplitude and the phase of the sampling point on the complex plane, and if no direct current component exists, the dispersion point takes a zero point as a center of a circle, the amplitude as a radius, and the phase changing along with time as an angle, and the distribution is in an arc shape. If the signal is interfered by clutter generated by a static object, the amplitude of the estimated phase evolution along with time is often smaller than a true value due to the existence of direct current components, and the larger the clutter signal intensity is, the larger the deviation value is, so that the estimated deviation of the phase time sequence can be compensated and corrected by using a signal post-processing method, and the noise interference of the direct current component clutter on a target echo can be eliminated.

Step 12, performing fourier transform on the preprocessed echo signal to obtain a distance unit of the target, which specifically includes:

when the human body is positioned in front of the radar for a certain distance, the distance R=R of the radar to the target is obtained due to the tiny motion of the chest of the human body ₀ +X(mt _s ) Wherein R is ₀ For a fixed target position, t _s For the frame period, m represents the frame number, X (mt) _s ) Representing the chest amplitude of the human body over time. The millimeter wave radar emits signals in the form of linear frequency modulation continuous wave, and the expression of the emitted signals is

Wherein f _c Represents radar carrier frequency, μ represents frequency modulation slope, μ=b/T _c B is the bandwidth of the linear frequency modulated continuous wave signal, T _c Is a repetition period of Chirp. The echo signal reflected back via the target has the same waveform as the transmission signal, but the electromagnetic wave is transmitted from the radar and reflected via the target with a delay defined as τ, so the received echo signal of the millimeter wave radar is expressed as: />

Where τ=2r/c is the instantaneous delay of the echo, R is the initial distance of the target, and c is the speed of light. In millimeter wave radar, the difference frequency signal expression after passing through the mixer and the high pass filter is: />

Wherein f _R For the difference frequency, +.>

Phi is the phase of the difference frequency signal,

due to->

Is very small and negligible, so +.>

Wherein (1)>

For the wavelength of the transmitted signal, in this case, +.>

Performing FFT processing on the Q (t) in a distance dimension to obtain the distance of the target, namely obtaining a distance unit of the target, wherein the signal form after the distance dimension FFT is completed is as follows: />

And selecting a distance unit where the peak maximum value is located as the distance unit of the target point cloud.

And step 13, determining the phase of the target according to the distance unit where the target is located.

Before extracting the phase of the distance unit where the target is located, the distance unit where the target is located needs to be acquired by utilizing a millimeter wave radar. Because the data acquisition object is a human body target and needs long-time data acquisition, the distance units can change within a certain range, but the distance units of the same target are ensured to be extracted in the subsequent phase extraction process, so that all the distance units in one processing period are accumulated on the basis of the distance units of the target acquired in the step 12, and the distance unit with the largest energy is the distance unit to be extracted. From the signal form obtained in step 12 after the distance dimension FFT is completed, the phase of the distance unit where the target is located is obtained as follows:

The millimeter wave radar transmitting signal is in a complex signal form, and the phase value of the target can be solved through an arctangent function according to the orthogonal characteristics of the two paths of signals. In the phase information extraction process, the extracted phase range is positioned at [ -pi]In the meantime, assuming that the phase change is changed from the third quadrant to the second quadrant at two moments, the phase change is a negative angle, when the angle is changed to the real axis (Re axis), the value output by the angle of the second quadrant through the arctangent function is a positive value, and a beat of 2 pi occurs in the positive value, and the beat of 2 pi is compensated at this time, namely, the disentanglement is obtained. The phase difference is the relative change value of the phase after the unwrapping compared with the phase of the first frame. After solving the phase difference of the target for all the emission signals, the vital sign signals containing respiratory heartbeats can be deduced as follows:

the respiratory heartbeat mixed waveform is obtained.

As shown in fig. 3, step 2, a denoising method based on phase space reconstruction is used to perform signal separation on the original signal to obtain a respiratory signal and a heartbeat signal.

The embedding theorem shows that the prime mover system can be recovered from the time series, and by observation, a set of measurements of the nonlinear system can be obtained, i.e., the time series, and then used to construct a set of m-dimensional vectors associated with τ, which if τ and m are properly described, can describe the prime mover system. m and τ are respectively embedded dimension and time delay, and the observed time sequence describes that the original system is phase space reconstruction, namely, the original signals overlapped by the respiratory heartbeats are restored into a dynamic model of the original system (a heartbeat system and a respiratory system) through the phase space reconstruction.

The invention adopts a mutual information method to calculate the time delay tau of phase space reconstruction; the calculation principle of the mutual information method is as follows:

let the entropy H of the system be its average information content for the variable x, namely:

let [ s, q ]]＝[x(t)，x(t+τ)]Where τ is the time delay, the total coupling system is (S, Q), if x is known at time t, then the average uncertainty of x at time t+τ is: h (q|s) =h (S, Q) -H (S), wherein ∈r>

Since H (S, Q) is the uncertainty of Q in isolation, H (q|s) is the uncertainty of Q for S, which is known. Therefore, s is known to reduce the uncertainty of q, where mutual information is: i (Q, S) =h (Q) -H (q|s) =h (Q) +h (S) -H (S, Q) =i (S, Q). In general, mutual information is: />

If the vector is a reconstruction of a delay time, I _n The time lag corresponding to the first time (tau) reaches the minimum is the optimal delay time.

The Cao method can effectively distinguish random signals from determined signals by only one time delay parameter tau, and can calculate m through less data volume, so the method is selected to calculate the embedding dimension, and the calculation method is as follows:

assume that each phase space vector in a multidimensional phase space is: x (i) = { X (i), X (i+τ), …, X (i+ (m-1) τ) }, the nearest neighbor point corresponding to the phase space vector with dimension m is X ^NN (i) The distance between them is: r is R _m (i)＝||x(i)-x ^NN (i) I, if the dimension is m plus 1, distance R _m (i) Will become R _m+1 (i)，

If R is _m+1 (i) Ratio R _m (i) The false field point on the low-dimensional orbit is formed by projecting two non-adjacent points in the high-dimensional attractor, and the false field point is +.>

R is as follows _m (i)＝||x(i)-x ^NN (i) Substitution of I into I>

The method can obtain: />

The Cao method rewrites it as:

wherein x is _m (i)、/>

And x _m+1 (i)、

The i-th vector and its nearest neighbors in the m and m+1 dimensions of phase space, respectively. If defined as follows:

E1(m)＝E(m+1)/E(m)

when m is increased to a certain value m ₀ If E1 (m) tends to be constantDetermining, indicating the certainty of the time series; if the value of E1 (m) has been increasing, this time series randomness is indicated. For practical time series of finite length, it is difficult to determine whether the value of E1 (m) is stable, so another criterion can be used to determine:

E2(m)＝E ^* (m+1)/E ^* (m)

for the characteristics of unpredictability of the random time sequence and no correlation among the data, the formula E2 (m) is always 1; if the time series is deterministic, the correlation between data points will vary with m, i.e., there is an m value where E2 (m) is not equal to 1, i.e., the embedding dimension.

Set S _n Is the original system signal and is subjected to noise W _n The influence of (a) the measured time series Z _n Denoted as Z _n ＝S _n +W _n . For a time series with L sampling points

The Cao algorithm and the mutual information method are respectively used for selecting the embedding dimension m and the time delay tau to obtain the reconstructed phase space +.>

Can be expressed as:

Z _n ＝[Z _n ,Z _n+τ ,Z _n+2τ ,...,Z _n+(m-1)τ ] (1)

in the ideal noise-free (W _n =0), there is a phase-space mapping relationship as follows:

when noise (W) _n Not equal to 0), formula (2) can be expressed as:

/>

formula (2) is at Z _n Neighborhood N of (2) _n The inner linearisable expansion is:

wherein R is a diagonal weight matrix; a, a ⁿ Is a direction matrix;

is a higher order error.

For a pair of

Performing wavelet packet decomposition to obtain some sub-bands, estimating noise band by the energy of each sub-band as a percentage of the original signal energy, regarding the relatively smaller ratio as noise band, and estimating phase point Z by the noise level T' as the ratio of the signal energy in the noise band to the original signal energy _n Is a neighbor radius of:

wherein the method comprises the steps of

Respectively are the phase points Z _n Minimum and maximum Euclidean distances from other phase points, +.>

For radius correction factor, +>

Neighboring point neighborhood N _n The definition is as follows:

wherein, the liquid crystal display device comprises a liquid crystal display device,

as the phase point Z _n Neighborhood centroid of->

N is the number of intra-neighborhood phase points.

Also, formula (3) is at Z _n Is developed as in-neighborhood linearization:

wherein eta _n Is the sum of the noise. Equation (7) shows that in this m-dimensional phase space there is an attractor space and a null space (consisting of Q mutually orthogonal vectors, denoted as p= [ P ] ₁ ,P ₂ ,...,P _Q ]) When no noise exists, the attractor only exists in the attractor space, when noise pollution exists, components exist in the whole phase space, the noise is necessarily generated by the zero subspace, and noise components can be filtered through a projection method to the attractor space. The corrected value after projection can be expressed as:

wherein θ is _n For the correction amount of the correction amount,

m ₀ the number of feature vectors constituting the attractor space. And finally, carrying out weighted anti-reconstruction processing on all the corrected phase points, wherein a normalized Hanning window is adopted as a weight function.

Different delay time and embedding dimension of respiration and heartbeat are calculated respectively through a mutual information method and a Cao algorithm, and heartbeat signals and respiration signals are recovered respectively through a zero subspace and an attractor subspace projection mode, so that the effect of a filter is achieved, the retention of weak signal components is achieved, and the detection accuracy is improved.

In the embodiment, the breathing signal is recovered by adopting the parameter with the embedding dimension of 9 and the time delay of 8 as the parameter of phase space reconstruction; the parameters with embedding dimension 11 and time delay 6 are selected as parameters of phase space reconstruction to recover the heartbeat signal.

Step 3, analyzing the respiration signal and the heartbeat signal to obtain a respiration value and a heartbeat value:

step 31, performing time domain analysis on the respiration signals to judge whether an apnea or breath hold situation exists; by judging the abnormal situation, the abnormal situation can be found conveniently in time, and rescue can be performed in time if necessary. Carrying out frequency domain analysis on the respiration signals to obtain respiration values per minute; the method specifically comprises the following steps:

step 312, determining the magnitude relation between M and the threshold G, if M is greater than G, entering step 313, otherwise determining that the patient is in an apnea or breath hold, i.e. breathing is 0, starting determining the next signal, and taking G as 5.02X10 ⁸ ；

Step 313, obtaining the mean square error of the respiratory signal;

Step 32, performing frequency domain analysis on the heartbeat signal to obtain a heartbeat value per minute:

step 322, performing respiration harmonic reduction processing on the heartbeat signal, namely dividing the respiration harmonic value in the heartbeat signal by 100 to achieve the purpose of reducing the amplitude of the heartbeat signal;

Claims

1. The vital sign detection method based on the millimeter wave radar is characterized by comprising the following steps of:

step 1, acquiring an original signal of respiration and heartbeat overlapping;

2. The vital sign detection method based on millimeter wave radar according to claim 1, wherein the specific step of acquiring the original signal in step 1 is: transmitting electromagnetic wave signals to the space range to be detected through a millimeter wave radar, receiving echo signals, and processing the echo signals to obtain original signals of respiration and heartbeat overlapping; the echo signal processing specifically includes:

step 11, preprocessing echo signals;

3. The millimeter wave radar-based vital sign detection method according to claim 2, wherein the preprocessing in step 11 includes: removing static clutter and/or removing direct current interference.

4. The vital sign detection method based on millimeter wave radar according to claim 2, wherein in the step 13, the distance unit determining method where the target is located is: and accumulating all the distance units in the unit processing period, wherein the distance unit with the largest energy is the distance unit where the target is located.

5. The millimeter wave radar-based vital sign detection method according to claim 2, wherein in the step 14, the phase unwrapping means: when the phase difference value between two adjacent moments is larger than pi or smaller than-pi, carrying out + -2 pi on the value of the latter moment; phase difference refers to: the relative change value of the phase after phase unwrapping compared to the first frame phase.

6. The vital sign detection method based on the millimeter wave radar according to claim 1, wherein the step 21 calculates the time delay of the phase space reconstruction by using a mutual information method and calculates the embedding dimension by using a Cao algorithm.

7. The millimeter wave radar-based vital sign detection method according to claim 1, wherein the weighting function in the step 23 adopts a normalized hanning window when the weighting construction process is performed.

8. The millimeter wave radar-based vital sign detection method according to any one of claims 1-7, wherein the step 3 specifically comprises:

9. The millimeter wave radar-based vital sign detection method according to claim 8, wherein the step 31 specifically comprises:

step 313, obtaining the mean square error of the respiratory signal;

10. The millimeter wave radar-based vital sign detection method according to claim 8, wherein the step 32 specifically comprises: