CN113940660A - Method for detecting respiratory frequency and respiratory volume by radar based on complex signal adjustment - Google Patents
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Abstract
The invention discloses a method for detecting respiratory frequency and respiratory volume by a radar based on complex signal regulation, which comprises the steps of receiving a reflecting signal of a living body by applying a Doppler radar, taking a transmitting signal as a local oscillation signal, carrying out down-conversion on the transmitting signal and the reflecting signal to obtain a baseband signal, obtaining two paths of I/Q demodulation signals and a synthesized complex signal thereof by adopting an orthogonal frequency mixing mode, carrying out Fourier transform on the complex signal to obtain a signal frequency spectrum, inhibiting harmonic components, and taking a peak value in the obtained corresponding frequency spectrum as a value of the respiratory frequency; and then, constructing a relation model between the respiratory rate and the respiratory volume by using the BP neural network, and further completing the measurement of the respiratory volume of the non-contact living body. By adopting the method for detecting the respiratory rate and the respiratory volume by the radar based on complex signal adjustment, the detection system has a simple structure, the detection method is simple and quick, the respiratory volume of the living body is measured under the non-contact condition, and the interference to the living body can be effectively reduced.
Description
Technical Field
The invention relates to the technical field of respiration monitoring, in particular to a method for detecting respiratory frequency and respiratory volume by a radar based on complex signal adjustment.
Background
In recent years, with the rapid development of modern medical detection technology along with the advancement of science and technology, the vital sign detection technology based on radar sensing is receiving attention due to its non-contact characteristic. The non-contact detection means is applied to special life bodies such as infants, burn patients, infectious disease patients and the like, has certain advantages, can ensure the stability of the test and the reliability of the result, simultaneously avoids secondary damage, and protects the safety of doctors and patients to the maximum extent. The vital sign parameters such as the respiratory volume, the respiratory frequency and the like have important significance in the health condition detection of a living body. At present, the non-contact Doppler radar for detecting the respiratory frequency has the problems of limited detection sensitivity and accuracy, more complex system structure, small-angle approximation, empty detection points and the like; the research for detecting the respiration quantity based on a non-contact Doppler radar sensing detection system is lacked.
Therefore, the accurate and convenient respiratory rate and respiratory volume detection method has high application value by utilizing the Doppler radar detection system which has a simple structure and can avoid the problem of empty detection points and the influence of direct current bias.
Disclosure of Invention
The invention aims to provide a method for detecting respiratory rate and respiratory volume by a radar based on complex signal adjustment, which aims to solve the problems of limited sensitivity and accuracy, complex system structure, small-angle approximation and empty detection points of a non-contact Doppler radar detection system in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for detecting respiratory rate and respiratory volume based on a radar adjusted by a complex signal is characterized by comprising the following steps:
s1: transmitting a signal through a Doppler radar, receiving a reflection signal of a living body, taking part of the transmission signal T (t) as a local oscillation signal, and performing down-conversion with the reflection signal R (t) to obtain a baseband signal;
s2: carrying out quadrature frequency mixing on the baseband signals to obtain two paths of I/Q demodulation signals and a synthesized complex signal thereof;
s3: the complex signal is converted into a digital signal by an analog-to-digital converter, then Fourier transform is carried out on the digital signal, and a frequency spectrum signal is displayed;
s4: suppressing harmonic components to obtain a value of respiratory frequency;
s5: the measured respiratory rate value is used as an input layer for training a BP neural network, and the actual respiratory rate value measured by the spirometer is used as an output layer, so that a relation model between the respiratory rate and the respiratory rate is created;
s6: the Doppler radar detects the value of the breathing frequency of the living body, and then the breathing volume of the living body can be output through the trained BP neural network.
Further, the baseband signal mathematical model in S1 is constructed as follows:
the transmission signal t (t) of the doppler radar is represented as:
where f denotes the carrier frequency,
representing phase noise;
the reflected signal R (t) is expressed as:
where x (t) represents the thoracic vibration caused by breathing:
x(t)＝m·sin(ωt)
d represents the distance between the detection system and the life body to be detected, c represents the propagation speed of the electromagnetic wave, lambda represents the wavelength of the electromagnetic wave, and m represents the vibration amplitude;
taking the transmission signal T (t) as a local oscillation signal, and performing down-conversion with the reflection signal R (t) to obtain a baseband signal B (t), which is represented by the following formula:
wherein the content of the first and second substances,
θ0the phase shift is determined by the phase shift of the reflecting surface (about 180 degrees), the distance between the mixer and the antenna, and other factors;
representing the residual phase noise of the oscillator.
Further, the baseband signals are subjected to quadrature frequency mixing to obtain two paths of I/Q demodulation signals and a mathematical model of a synthesized complex signal thereof as follows:
synthesizing two paths of I/Q baseband signals with the phase difference of 90 degrees into a complex signal C (t):
preferably, the complex signal in S3 is processed by a second-order low-pass filter before being converted into a digital signal by an analog-to-digital converter.
Further, performing fourier transform on the synthesized complex signal, and expressing as:
G[n]＝ω[n]⊙{FFT[C(t)]}[n]
wherein "<" > denotes a windowing operator, FFT [ ] denotes a Fourier transform, and n denotes a sample point of a digitized echo signal.
Further, a frequency domain accumulation algorithm is applied to suppress harmonic components, and the expression is as follows:
H[i]＝L[i]+jL[i]
wherein the content of the first and second substances,
where i is 1, …, n-1, g (i) denotes a function of g (n), and κ denotes a spectral component.
Preferably, the spectral peak obtained by the frequency domain accumulation algorithm is regarded as the value of the respiratory frequency.
Further, the relationship between the breathing rate and the breathing volume in S5 is constructed as follows:
s51: detecting the respiratory frequency of the obtained life body as an input layer variable of a training BP neural network model, and taking the actual value of the respiratory volume as an output layer variable;
s52: according to the formula
Determining the node number and the layer number of the hidden layer, wherein m, n and l respectively represent the node numbers of the hidden layer, the input layer and the output layer, and alpha represents an adjustable positive integer between 1 and 10;
s53: the actual respiratory rate value is measured by a spirometer, and the set BP neural network is trained by using the respiratory rate and the actual respiratory rate value corresponding to the respiratory rate;
s54: and (3) detecting the respiratory rate by using the trained relationship model of the respiratory rate and the respiratory volume by using the system, and outputting the respiratory volume.
Preferably, in S53, the number of training cycles of the BP neural network is set to 300, the learning rate is set to 0.01, and the root mean square error of the validation set stops after 7 consecutive times of model training iterations without decreasing.
A system for detecting respiratory rate and respiratory volume based on a radar regulated by a complex signal is characterized by comprising the following modules:
antenna and radio frequency module: transmitting a signal to a to-be-detected living body, receiving a reflected signal carrying living body information, and performing orthogonal frequency mixing on the reflected signal and a non-transmitted signal;
the signal digital processing module: filtering out intermodulation signals, and converting the filtered signals into digital signals by an analog-to-digital converter;
the digital signal processing module: carrying out Fourier transform on the digital signal, inhibiting harmonic components through frequency domain accumulation, and obtaining a respiratory frequency value from the obtained frequency spectrum;
the respiratory rate and respiratory volume relation model building module comprises: and taking the value of the respiratory rate measured by the system as an input layer of a training BP neural network, taking the actual value of the respiratory rate measured by the spirometer as an output layer, and creating a relation model between the respiratory rate and the respiratory rate.
Compared with the prior art, the invention has the following advantages and positive effects:
(1) the invention adopts the orthogonal frequency mixing mode to demodulate signals, outputs two paths of baseband signals and synthesized complex signals thereof, and carries out Fourier transform on the complex signals, thereby overcoming the empty detection point problem in the small angle approximation process.
(2) On the basis of completion of Fourier transform, harmonic components are suppressed, so that the value of the respiratory frequency can be obtained, and high-precision parameter estimation is realized.
(3) A relation model between the respiratory rate and the respiratory volume is established by utilizing the BP neural network, the non-contact respiratory volume detection can be realized according to the respiratory rate value detected by the Doppler radar in the invention, the detection method is simple, quick and accurate, and the interference to a living body can be effectively reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a block diagram of the detection method of the present invention;
FIG. 2 is a block diagram of a BP neural network training method according to the present invention;
FIG. 3 is a system framework diagram of the present invention;
fig. 4 shows two thoracic vibration patterns in the sleep state of the present invention.
Detailed Description
In order to more clearly illustrate the objects, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the embodiment of the invention, under the working condition of the radar, the electromagnetic wave can be regarded as plane wave, and the mathematical model of the reflection and incidence signals of the to-be-detected living body can be reasonably simplified.
The present invention will be described in detail with reference to the following specific examples:
example 1
As shown in fig. 1, a system for detecting respiratory rate and respiratory volume based on a radar with complex signal adjustment includes the following modules:
antenna and radio frequency module: including signal receiving, transmitting antennas and quadrature mixers. The transmitting antenna transmits signals to a to-be-detected living body, the receiving antenna receives reflected signals carrying breathing signals of the living body, and the orthogonal frequency mixer carries out orthogonal frequency mixing on the reflected signals and the transmitted signals which are not transmitted;
the signal digital processing module: the low-pass filter is used for filtering intermodulation signals and reducing interference; the signal after interference elimination is converted into a digital signal by an analog-to-digital converter, and the digital signal is transmitted to a next module;
the digital signal processing module: carrying out Fourier transform on the digital signal, inhibiting harmonic components through frequency domain accumulation, and obtaining a respiratory frequency value from the obtained frequency spectrum;
the respiratory rate and respiratory volume relation model building module comprises: and (3) taking the value of the respiratory rate measured by the system as an input layer of the training BP neural network, and taking the actual value of the respiratory rate measured by the spirometer as an output layer, so as to create a relation model between the respiratory rate and the respiratory rate.
As shown in fig. 2, the method for detecting the respiratory rate based on the radar adjusted by the complex signal by using the system shown in fig. 1 comprises the following steps:
s1: transmitting a signal through a Doppler radar, receiving a reflection signal of a living body, taking part of the transmission signal T (t) as a local oscillation signal, and performing down-conversion with the reflection signal R (t) to obtain a baseband signal;
s2: carrying out quadrature frequency mixing on the baseband signals to obtain two paths of I/Q demodulation signals and a synthesized complex signal thereof;
s3: the complex signal is converted into a digital signal by an analog-to-digital converter, then Fourier transform is carried out on the digital signal, and a frequency spectrum signal is displayed;
s4: suppressing harmonic components to obtain a value of respiratory frequency;
preferably, in S1, the adopted radio frequency signal has a frequency of 2.45GHz, and the transmit signal is divided into two paths, one path of signal is emitted to the living body to be detected through the transmit antenna, and the other path of signal is used as a local oscillation signal, so as to achieve synchronization between the transmitter and the receiver; and performing down-conversion on the local oscillator signal and a reflected signal which carries the breathing signal of the living body and is received by the receiving antenna to obtain a baseband signal B (t).
Preferably, the body to be tested is positioned at a position 0.8cm right in front of the detection system, and the body keeps a static standing or sitting state and breathes at a constant speed.
The null detection point problem occurs when the local oscillator signal is the same as the reflected signal or remains 180 ° out of phase. In order to avoid the related problems, the present embodiment employs a passive I/Q mixer to perform quadrature down-conversion on the signal, and directly outputs two paths of I/Q demodulation signals with a phase difference of 90 °. Therefore, when the perception capability of the I path signal is reduced, the Q path signal obtains the best perception effect. Wherein, the complex signal C (t) is synthesized by two paths of I/Q baseband signals with the phase difference of 90 degrees. It should be noted that, before the complex signal is converted into a digital signal, a second-order low-pass filter is used to process the signal, so as to filter the interference of the intermodulation signal, and the filtered signal is passed through a capacitor to isolate the dc component.
Further, in the process of converting the complex signal into the digital signal by the analog-to-digital converter, since the vital signal is usually less than 3Hz, the embodiment samples according to the nyquist sampling theorem to meet the design requirement; and then carrying out Fourier transform and windowing operation on the composite digital signal to obtain a respiratory signal frequency spectrum of the to-be-detected living body. The composite digital signal is subjected to Fourier transform, the sensing capability of the I/Q two paths of signals is balanced, and therefore the problem of empty detection points in the small-angle approximation process is solved.
Under normal conditions, the respiratory rate of the living body is between 0.15Hz and 0.5 Hz. Preferably, the time window value range of the extracted respiratory frequency is 0.1Hz to 0.6 Hz.
In order to further improve the signal-to-noise ratio of the signal, the present embodiment applies a frequency domain accumulation algorithm to suppress harmonic components.
Preferably, the peak value corresponding to the frequency spectrum obtained by the frequency domain accumulation algorithm is regarded as the value of the respiratory frequency.
In this embodiment, the breathing rate of the human body in the standing and sitting behavioral modes can be measured according to the method.
Example 2
As shown in fig. 2, the method for detecting respiration volume based on complex signal adjusted radar by using the system shown in fig. 1 comprises the following steps:
s5: establishing a relation model between the respiratory rate and the respiratory volume by taking the respiratory rate value measured in the embodiment 1 as an input layer of a training BP neural network and taking the respiratory volume actual value measured by a spirometer as an output layer;
s6: the Doppler radar detects the breathing frequency of the living body, and then the breathing volume of the living body can be output through the trained BP neural network.
As shown in fig. 3, the relationship between the breathing rate and the breathing volume in S5 is constructed as follows:
s51: the respiratory frequency of the detected life body is used as an input layer variable of a training BP neural network model, and the actual value of the respiratory volume is used as an output layer variable;
s52: according to the formula
Determining the number of nodes and the number of layers of the hidden layer, wherein m, n and l respectively represent the number of nodes of the hidden layer, the input layer and the output layer, the number of the hidden layers in the embodiment comprises two layers, and alpha represents an adjustable positive integer between 1 and 10;
s53: in the embodiment, the actual respiratory volume value is measured by a spirometer, and the set BP neural network is trained by using the respiratory frequency and the actual respiratory volume value corresponding to the respiratory frequency;
s54: and (3) detecting the respiratory rate by using the trained relationship model of the respiratory rate and the respiratory volume by using the system, and outputting the respiratory volume.
Preferably, in step S53, in order to improve the accuracy and reliability of the training model, a part of the collected data is used as training data, a part of the collected data is used as verification data, another part of the collected data is used as test data, and the selection of data for different purposes is randomly performed. The training cycle number of the BP neural network is set to 300, the learning rate is set to 0.01, and the iteration is stopped when the root mean square error of the verification set is not reduced for 6 times continuously in the process of model training iteration.
In this embodiment, the respiration rate of the human body in the standing and sitting behavioral modes can be measured according to the method.
Example 3
The method for detecting the breathing frequency and the breathing volume based on the radar adjusted by the complex signal can be used for detecting the breathing frequency and the breathing volume in the embodiments 1 and 2, and can also be used for detecting the sleep breathing state, such as the sleep apnea recognition.
Since the living body to be tested may change the sleeping posture at variable times in the sleeping state, the difference between this embodiment and embodiment 1 is that the living body to be tested enters the sleeping state in the lying state. As shown in fig. 4, the vibration amplitude m of the chest vibration x (t) ═ m · sin (ω t) includes not only the acquisition of the back and abdomen movement in the lying state but also the acquisition of the back and abdomen movement in the lateral state.
Other steps and parameter settings of the method in this embodiment are the same as those in embodiment 1, the breathing state of the living body in the sleep state is continuously detected and recorded according to the method in embodiment 1, and the number of times of sleep apnea is determined according to the measured values of the breathing frequency and the breathing volume.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims. It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A method for detecting respiratory rate and respiratory volume by a radar based on complex signal adjustment is characterized by comprising the following specific steps:
s1: transmitting a signal through a Doppler radar, receiving a reflection signal of a living body, taking part of the transmission signal T (t) as a local oscillation signal, and performing down-conversion with the reflection signal R (t) to obtain a baseband signal;
s2: carrying out quadrature frequency mixing on the baseband signals to obtain two paths of I/Q demodulation signals and a synthesized complex signal thereof;
s3: the complex signal is converted into a digital signal by an analog-to-digital converter, then Fourier transform is carried out on the digital signal, and a frequency spectrum signal is displayed;
s4: suppressing harmonic components to obtain a value of respiratory frequency;
s5: the measured respiratory rate value is used as an input layer for training a BP neural network, and the actual respiratory rate value measured by the spirometer is used as an output layer, so that a relation model between the respiratory rate and the respiratory rate is created;
s6: the Doppler radar detects the value of the breathing frequency of the living body, and the value can output the breathing volume of the living body through the trained BP neural network.
2. The method for detecting respiratory rate and respiratory volume based on complex signal conditioning radar as claimed in claim 1, wherein the mathematical model of the baseband signal in S1 is constructed as follows:
the transmission signal t (t) of the doppler radar is represented as:
where f denotes the carrier frequency,
representing phase noise;
the reflected signal R (t) is expressed as:
where x (t) represents the thoracic vibration caused by breathing:
x(t)＝m·sin(ωt)
d represents the distance between the detection system and the life body to be detected, c represents the propagation speed of the electromagnetic wave, lambda represents the wavelength of the electromagnetic wave, and m represents the vibration amplitude;
taking the transmission signal T (t) as a local oscillation signal, and performing down-conversion with the reflection signal R (t) to obtain a baseband signal B (t), which is represented by the following formula:
wherein the content of the first and second substances,
θ0which is indicative of the phase shift, is,
representing the residual phase noise of the oscillator.
3. The method for detecting the respiratory rate and the respiratory volume of a radar based on complex signal adjustment as claimed in claim 2, wherein the baseband signals are quadrature mixed to obtain two paths of I/Q demodulation signals and a mathematical model of a synthesized complex signal thereof as follows:
synthesizing two paths of I/Q baseband signals with the phase difference of 90 degrees into a complex signal C (t):
4. the method for detecting respiratory rate and respiratory volume of radar based on complex signal conditioning of claim 1, wherein the complex signal in S3 is processed by a second-order low-pass filter before being converted into a digital signal by an analog-to-digital converter.
5. The method of claim 3, wherein the synthesized complex signal is Fourier transformed by the complex signal conditioning-based radar, and the expression is as follows:
G[n]＝ω[n]⊙{FFT[C(t)]}[n]
wherein, the "<" > indicates a windowing operator, the FFT [ ] indicates a Fourier transform, and n indicates a digitized sample point.
6. The method of claim 5, wherein the harmonic components are suppressed by applying a frequency domain accumulation algorithm, and the expression is as follows:
H[i]＝L[i]+jL[i]
wherein the content of the first and second substances,
where i is 1, …, n-1, g (i) denotes a function of g (n), and κ denotes a spectral component.
7. The method for detecting respiratory rate and respiratory volume based on complex signal conditioning radar as claimed in claim 6, wherein the frequency domain accumulation algorithm is used to obtain the peak of the frequency spectrum as the value of the respiratory rate.
8. The method for detecting the respiratory rate and the respiratory volume of the radar based on complex signal adjustment according to claim 1, wherein the relationship between the respiratory rate and the respiratory volume in S5 is constructed as follows:
s51: detecting the respiratory frequency of the obtained life body as an input layer variable of a training BP neural network model, and taking the actual value of the respiratory volume as an output layer variable;
s52: according to the formula
Determining the node number and the layer number of the hidden layer, wherein m, n and l respectively represent the hidden layer, and inputtingThe number of nodes of the layers and the output layer, alpha represents an adjustable positive integer between 1 and 10;
s53: the actual respiratory rate value is measured by a spirometer, and the set BP neural network is trained by using the respiratory rate and the actual respiratory rate value corresponding to the respiratory rate;
s54: and (3) detecting the respiratory rate by using the trained relationship model of the respiratory rate and the respiratory volume by using the system, and outputting the respiratory volume.
9. The method for detecting respiratory rate and respiratory volume based on complex signal conditioning radar as claimed in claim 8, wherein in S53, the number of training cycles of the BP neural network is set to 300, the learning rate is set to 0.01, and the rms error of the validation set stops iteration without decreasing for 7 consecutive times in the process of model training iteration.
10. A system for detecting respiratory rate and respiratory volume based on a radar regulated by a complex signal is characterized by comprising the following modules:
antenna and radio frequency module: transmitting a signal to a to-be-detected living body, receiving a reflected signal carrying a breathing signal of the living body, and performing orthogonal frequency mixing on the reflected signal and a non-transmitted transmitting signal;
the signal digital processing module: filtering out intermodulation signals, and converting the filtered signals into digital signals by an analog-to-digital converter;
the digital signal processing module: carrying out Fourier transform on the digital signal, inhibiting harmonic components through frequency domain accumulation, and obtaining a respiratory frequency value from the obtained frequency spectrum;
the respiratory rate and respiratory volume relation model building module comprises: and taking the value of the respiratory rate measured by the system as an input layer of a training BP neural network, taking the actual value of the respiratory rate measured by the spirometer as an output layer, and creating a relation model between the respiratory rate and the respiratory rate.
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