CN112515656B - Position-independent respiration monitoring method based on acoustic environment response - Google Patents
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Abstract
The invention provides a position-independent breath detection method based on acoustic environment response, which comprises the following steps: arranging an acoustic wave transmitter and a receiver in a room, wherein the acoustic wave transmitter circularly transmits an excitation signal, the excitation signal is an extremely short broadband signal, the excitation signal is formed by splicing a section of windowed frequency modulation signal and a 0 sequence, and the receiver receives signals with the same length as the excitation signal in real time and continuously calculates a CFR sequence in a set frequency band; and then, continuously filling a matrix with a fifo function, trending each frequency point in the time dimension after filling, calculating autocorrelation, extracting a plurality of sections of CFR sequences with strongest autocorrelation, and normalizing; finally, the sequences are synthesized into continuous waveforms, and respiration monitoring of the subject in a resting state is realized through polling. The invention can solve the problem that the traditional breath detection based on the acoustic ranging and the breath air flow Doppler effect is sensitive to the direction of the object, and can realize the breath detection irrelevant to the position.
Description
Technical Field
The invention belongs to the technical field of respiration monitoring, and particularly relates to a sleep respiration monitoring method which utilizes sound wave signals in a non-contact mode indoors and does not need receiving and transmitting equipment to face a subject.
Background
Respiration is one of the most basic and important physiological sign information of animals, and human or livestock are prone to respiratory diseases, which can lead to the occurrence of pathological respiratory processes with different degrees, such as: compared with normal respiration, some diseases can cause respiratory symptoms such as shortness of breath, respiratory disturbance, dyspnea and the like. Therefore, continuous respiration monitoring is an important means for judging the health status and further performing disease prevention and control. With the rapid development of sensing technology and information technology, lossless and automatic respiration monitoring technology gradually becomes a development trend and a research hotspot. Currently, automatic respiration monitoring techniques can be broadly divided into two main categories, contact and non-contact. The contact method utilizes specific sensors such as pressure sensors, gas sensors or heat sensors in the wearable equipment to realize respiration monitoring by measuring chest and abdomen movements, breathing sounds, respiratory airflows and the like, but has the problems of high price, aggressiveness, necessity of being carried next to the skin at any time and the like.
At present, the non-contact respiration monitoring technology, particularly the respiration monitoring technology based on sound waves, is gradually paid attention to, compared with the contact monitoring technology, the non-contact respiration monitoring technology does not need to be carried by a human body or attached to any equipment, sound wave signals are widely existing, and the non-invasive respiration monitoring technology has the advantages of being non-invasive, convenient, low in cost and the like. However, the current research is mainly based on chest and abdomen direct ranging (Nandakumar R, gollakota S, watson N.Contactless Sleep Apnea Detection on Smartphones [ C ]// International Conference on Mobile Systems, applications, and services.ACM,2015:45-57; wang, T., D.Zhang, Y.Zheng, T.Gu, X.Zhou and B.Dorizzi.2018.C-FMCW Based Contactless Respiration Detection Using Acoustic Signal.proceedings of the ACM on Interactive, mobile, wearable and Ubiquitous Technologies 1 (4): 1-20.) and respiratory airflow Doppler shift (Arlotto P, grimmaldi M, naeck R, ginoux JM. ultrasonic contactless sensor for breathing monitoring.Sensors (Basel) 2014Aug 20;14 (8): 15371-86.Doi:10.3390/S140815371.PMID:25140632; PMCID: PMC 4179033.) Wang, T., D.Zhang, L.Wang, Y.Zheng, T.Gu, B.Dorizzi and X.Zhou.2019.Contact Respiration Monitoring Using Ultrasound Signal With Off-the-shell Audio devices IEEE Internet of Things Journal 6 (2): 2959-2973.) principles, which are all extremely sensitive to the location of the monitored object, require the transceiver and the monitored object to be in a fixed area and toward the object.
Disclosure of Invention
In order to overcome the disadvantages of the prior art, the present invention aims to provide a position independent respiration monitoring method based on acoustic environment response, which mainly uses the phenomenon that the fluctuation of chest and abdomen causes the indoor acoustic channel to change when people or livestock breathe, namely, the principle that the fluctuation of chest and abdomen can cause the Channel Frequency Response (CFR) to change in indoor environment, and then realizes the position independent respiration monitoring of a monitored object by a design method.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method of location independent respiration monitoring based on acoustic environmental response, comprising the steps of:
Step 2, the fast Fourier transform algorithm is adopted to process the current received signal x r (n) excitation signal x with known transmission t (n) solving the frequency spectrum, solving a channel frequency response sequence H (k) at the current moment in the indoor environment,
n is the power of 2 closest to the magnitude of N, according to the excitation signal x t Frequency range of (n) extracting H from H (k) r (k) Representing a useful channel frequency response sequence;
step 3, setting a matrix Buffer with fifo function, and setting H at the current moment r (k) From the end of Buffer write, the column direction of the resulting matrix represents the time dimension, and the row direction represents [ f ] c ,f c +B]Frequencies within the range, f c The initial frequency of frequency modulation is B is the frequency modulation bandwidth;
step 4, when the Buffer is in a full state, entering a step 5, otherwise, resuming the step 2 and the step 3;
step 5, trending is carried out on each column of data in the Buffer, namely a channel frequency response sequence;
step 6, rapidly calculating the autocorrelation of each column of sequences in the Buffer by using a time domain convolution theorem, and obtaining the maximum value R (k);
step 7, according to the set autocorrelation threshold R and the parameter j, selecting the front j row channel frequency response sequence with the strongest autocorrelation from the Buffer, writing into the set Final FRs matrix Buffer, and normalizing the Buffer;
step 8, synthesizing continuous waveforms of data in the FinalFRs to obtain a respiratory wave sequence currBreathwave;
and 9, smoothing the respiratory wave sequence currBreathwave synthesized at the current moment, namely, the respiratory wave monitored in real time, realizing the visualization of the respiratory wave, continuously jumping to the operation of the step 2 for circulation, and realizing the respiratory monitoring of the indoor object in a resting state.
Preferably, the excitation signal x t (n) is formed by splicing a section of windowed linear or sinusoidal frequency modulation signal with an all-0 sequence, the frequency modulation signal period is T, the duration of the all-0 sequence signal is T ', the total duration of the signal is t=T+T', namely the duty ratio of the signal is
Wherein the frequency of the chirp signal varies with time f 1 (n) and phase variation with time u 1 (n) are as follows:
frequency of sinusoidal FM signal varies with time f 2 (n) and phase variation with time u 2 (n) are as follows:
the expression of the frequency modulated signal is as follows:
x t1 (n)＝cos(u(n))
u (n) takes u 1 (n) or u 2 (n)；
The expression for the all 0 sequence signal is as follows:
x t2 (n)＝[0,2*0,…n*0]
the transmitted excitation signal expression is as follows:
x t (n)＝x t1 (Tf s )+x t2 (T′f s )
where n is the sampling point number, i.e., n=1, 2, …, tf s ，f s For the system sampling frequency, T s For the sampling period of the system,
preferably, the calculation formula of the channel frequency response sequence H (k) at the current time is as follows:
wherein H (k) represents the current time and the frequency is
Is set to be a channel frequency response sequence of (c),
n is the power of 2 closest to the size of N, X t (k) Representing the frequency spectrum of the excitation signal, X r (k) Representing the frequency spectrum of the received signal, since the frequency range of the excitation signal is [ f c ,f c +B]Therefore f c <f<f c +B, the final range of values for k is as follows:
thereby extracting H r (k)。
Preferably, the matrix Buffer size is a×b, expressed as follows:
wherein,,
H r_a (k) H representing the time of the a-th row of the deposit r (k) Sequence. A is generally greater than or equal to 150.
Preferably, the Buffer being full refers to the matrix Buffer being full H r (k) The number of rows of the sequence is equal to a; the detrending of each column of data in Buffer is to subtract an optimal (least square or polynomial) fitting curve from each column of data.
Preferably, the calculation formula of the maximum value R (k) is as follows:
R(k)＝max(IFFT(X(w)X * (w)))
wherein X (w) represents the spectrum of each column in the Buffer, X * (w) represents a conjugated spectrum.
Preferably, the writing the set FinalFRs matrix buffer memory is to initialize the FinalFRs matrix to a 0 matrix with a size of a×j, directly assign the value, and normalize to the maximum value.
Preferably, in the step 8, the following traversal operation is performed on the j-column data in the FinalFRs:
step (1), adding corresponding elements of the sequence, and recording as Wave;
step (2), taking absolute values of the Wave sequences, and adding and summing all elements to obtain a value which is Sum;
step (3), if Sum < LastSum, wave subtracts the current first column sequence amplified by 2 times, and then executes step (2) again to avoid waveforms with phase differences of nearly half period, wherein LastSum is the value obtained in the last traversal, and is 0,l =1, 2, …, j;
step (4), assigning the current Sum to LastSum;
and after traversing, dividing each numerical value of the Wave sequence by j, and averaging to obtain the current synthesized respiratory Wave sequence, which is recorded as currBreathwave.
In order to enable the continuously refreshed respiratory waveform to be continuous, the waveform with the phase difference of nearly half period at the last moment needs to be avoided, and the following steps are adopted:
step (1), adding two sequences of currBreathwave and LastBreathwave separately and corresponding elements, and operating according to the step (2) to obtain values respectively marked as Sum2, sum1 and tempSum, wherein LastBreathwave is a respiratory wave sequence synthesized at the last moment;
and (2) inverting each value of the currBreathwave sequence if tempSum is less than or equal to Sum2 or tempSum is less than or equal to Sum 1.
Preferably, the sound wave transmitter and the sound wave receiver are arranged at the same place or different places indoors, the indoor environment is relatively closed, and a single-shot or multiple-shot single-shot form is adopted.
Compared with the prior art, the method can solve the problem that the traditional respiration monitoring based on the acoustic ranging and the respiratory airflow Doppler effect is sensitive to the direction of the object, and can realize the position-independent respiration monitoring.
Drawings
Fig. 1 is a flow chart of the method of the present invention.
Fig. 2 is a model of acoustic channel transmission for monitoring respiration in accordance with the method of the present invention.
Fig. 3 is a time domain diagram of the transmitted excitation signal.
Fig. 4 is a graph of Channel Frequency Response (CFR).
Fig. 5 is a graph of the CFR sequences of the first few columns with the strongest autocorrelation.
Fig. 6 is a graph of respiration waveforms monitored by the method of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.
As shown in fig. 1, the present invention is a position-independent respiration monitoring method based on acoustic environment response, comprising the steps of:
firstly, arranging sound wave transmitters at the same place or different places in an indoor environmentTx and acoustic wave receiver Rx, wherein the acoustic wave transmitter Tx cyclically transmits a set excitation signal x t (n) the acoustic wave receiver Rx receives signal data x of equal length as the excitation signal in real time without blocking r (n) and then resolving CFR sequences in combination with the transmitted and received signal data.
And writing the CFR sequence into a matrix with a fifo function, calculating autocorrelation values of each piece of data (each row of data) of each frequency point in the matrix in the time dimension, further selecting the first few sections of sequences with the strongest autocorrelation to synthesize continuous waveforms, and finally smoothing the synthesized waveform sequences to obtain the respiratory wave monitored in real time.
The acoustic transmitter Tx of the present invention is an electronic device with the capability of transmitting ultrasonic signals in air, which has a certain available bandwidth and a large transmission power, for example: sound box with ultrasonic frequency band transmitting capability, inverse piezoelectric transducer, etc.; the sound wave receiver Rx is a microphone or a piezoelectric transducer for receiving an ultrasonic frequency band, and has better performance effects such as nondirectionality, high sensitivity and the like. In this embodiment, as a core of signal processing, the arithmetic unit may directly use a PC, and then connect with a speaker and a microphone to form an audio transceiver system. If the independent product is to be realized, a system-level chip or a DSP chip can be adopted and combined with the transceiver unit for design. The arrangement of the transceiver units can adopt the forms of transceiver integration or transceiver variant and the like, in addition, the indoor environment is relatively closed, and the number of the transceiver units can select the forms of single-transmission single-reception, multi-directional single-transmission single-reception, multiple-transmission multiple-reception and the like according to the specific indoor environment.
In fact, as shown in the indoor acoustic channel transmission model of fig. 2, the closed indoor environment has abundant multipath reflection, and the received signal is divided into two parts, one part is a signal received by the microphone directly or indirectly through the chest and abdomen reflection of the tested person, and the other part is a signal received by the acoustic receiver Rx through the static environment reflection completely, so that the echo signal can be modeled as follows:
wherein,,
the attenuation coefficient of the ith reflection signal which is directly or indirectly reflected from the tested person and received by the sound wave receiver; />
Attenuation coefficient of the j-th reflected signal reflected from the static environment and received by the acoustic receiver; Δn i And Deltan j Is the corresponding time delay in sample points. In practice, only a part of the multipath signal can be received by the acoustic wave receiver Rx, which is called an effective multipath reflection signal, see the solid line shown in fig. 2, and the multipath reflection signal not received by the acoustic wave receiver is called an ineffective multipath reflection, see the broken line shown in fig. 2.
If the transceiver, the object to be measured and the static environment are regarded as an integral system, the breathing process accompanied by the fluctuation of the chest can cause the physical channel of the acoustic wave transmission to change, mainly from the following aspects:
1. the fluctuation of the chest part causes the dynamic change of the quantity of effective reflected signals, namely N in the above formula is changed;
2. dynamic variation of attenuation coefficient of effective reflected signal caused by breast relief, i.e. in the above
Dynamic changes occur.
In summary, the respiration process will change the indoor acoustic wave propagation channel, and in turn, the real-time changes in the indoor acoustic wave propagation channel parameters can reflect respiration. Thus, location-independent respiration monitoring can be achieved by monitoring changes in the parameters of the acoustic wave propagation channel in the room in real time.
Referring to fig. 1, the specific implementation steps of the method are as follows:
a. referring to fig. 3, an excitation signal x is emitted t (n) is designed as an extremely short wideband signal, consisting of a windowed linear or sinusoidal FM signal x t1 (n) and the full 0 sequence x t2 (n) the frequency modulation signals are spliced, the period of the frequency modulation signals is T, the duration of the all-0 sequence signals is T ', the total duration of the signals is t=T+T', namely the duty ratio of the signals is
The frequency and phase of the linear frequency modulation signal are changed along with time as shown in the following formulas 1 and 3 respectively, the sine frequency modulation is shown in the following formulas 2 and 4, the frequency modulation signal is shown in the following formula 5 respectively, the expression of the all 0 sequence signal is shown in the formula 6, and finally, the expression of the transmitted excitation signal is shown in the formula 7:
x t1 (n) =cos (u (n)) 5
x t2 (n)＝[0,2*0,…n*0]6. The method is to
x t (tf s )＝x t1 (Tf s )+x t2 (T′f s ) 7. The method of the invention
In the above, the system sampling rate is f s ，
n is the sampling point number, i.e. n=1, 2, …, tf s ，f c Representing the starting frequency of the frequency modulation, f in order to avoid the influence of ambient noise and to disturb the perceived object c The specific value of the sound wave frequency band is generally greater than or equal to 18KHz, and is based on the available bandwidth of the transceiver and the audible sound wave frequency band of the perception objectTo determine, B represents the frequency modulation bandwidth;
b. the fast Fourier transform algorithm is adopted for the current received signal x r (n) excitation signal x with known transmission t (n) solving a frequency spectrum, and calculating a Channel Frequency Response (CFR) sequence at the current moment in the indoor environment, wherein the sequence is represented by H (k), and the calculation formula is as follows:
in equation 8 above, H (k) represents the current time and frequency of the system
Is selected from the group consisting of a CFR sequence of (C),
n is the power of 2 closest to the size of N, X t (k) Representing the frequency spectrum of the excitation signal, X r (k) Representing the frequency spectrum of the received signal, since the frequency range of the excitation signal is [ f c ,f c +B]Therefore f c <f<f c The range of values of +B and k is as follows, thereby extracting H r (k) The sequence plot is shown in fig. 4;
c. setting an axb with fifo function
Matrix Buffer of the size to H at the current moment r (k) Written from the end of Buffer, the column direction of the matrix represents the time dimension, and the row direction represents [ f ] c ,f c +B]Frequencies within the range;
d. when the Buffer is in a full state, carrying out subsequent operation, otherwise, resuming the operations of b, c and d;
e. trending each column of data in Buffer, namely CFR sequence, in the time dimension;
f. and rapidly calculating the autocorrelation of the CFR sequences of each column of the Buffer by using the time domain convolution theorem, and obtaining the maximum value R (k), wherein the following formula is adopted:
R(k)＝max(IFFT(X(w)X * (w))
In the above formula, X (w) represents the frequency spectrum of each column in the Buffer, X * (w) represents a conjugated spectrum;
g. according to the set autocorrelation threshold R and the parameter j, selecting the CFR sequence with the highest autocorrelation in the first j columns from the Buffer, writing the CFR sequence into the set FinalFRs matrix Buffer, and normalizing the CFR sequence, wherein the sequence is plotted as shown in FIG. 5;
h. continuous waveform synthesis is carried out on the data in the FinalFRs, and the following traversal operation is carried out on the j-row data:
(1) adding the corresponding elements of the sequences, and marking as Wave;
(2) taking absolute values of the Wave sequences, adding and summing all elements, and finally obtaining a value which is marked as Sum;
(3) if Sum < LastSum (the last traversal gets a value, initially 0), wave subtracts the current first column sequence (l=1, 2 … j) amplified 2 times, and step (2) is performed again to avoid the waveform in which the phase difference is nearly half a period, see a reverse waveform shown in fig. 5;
(4) the current Sum is assigned to LastSum.
After traversing, dividing each numerical value of the Wave sequence by j to obtain a currently synthesized respiratory Wave sequence, and marking the currently synthesized respiratory Wave sequence as currBreathwave; finally, in order to enable the continuously refreshed respiratory waveform to be continuous, the waveform with a phase difference of nearly half period at the last moment needs to be avoided, and the following steps are adopted:
(5) adding two sequences of currBreath wave and LastBreath wave (respiratory wave sequence synthesized at last moment) separately and corresponding elements, operating according to a similar step (2), and finally obtaining values respectively marked as Sum2, sum1 and tempSum;
(6) if tempSum is less than or equal to Sum2 or tempSum is less than or equal to Sum1, the numerical values of the currBreathwave sequence are inverted.
i. Smoothing the respiratory wave sequence currBreathwave synthesized at the current moment, and realizing the visualization of respiratory waves, as shown in fig. 6. And continuing to jump to the operation b for circulation, so that respiration monitoring of the subject in the resting state is realized by polling, and respiration of the subject in the resting state is monitored.
Claims (10)
1. A method of location independent respiration monitoring based on acoustic environmental response comprising the steps of:
step 1, arranging an acoustic wave transmitter and an acoustic wave receiver in a room, wherein the acoustic wave transmitter circularly transmits an excitation signal x t (n) receiving the excitation signal x in real time without blocking by an acoustic receiver t (n) equal-length Signal data x r (n)；
Step 2, the fast Fourier transform algorithm is adopted to process the current received signal x r (n) excitation signal x with known transmission t (n) solving the frequency spectrum, solving a channel frequency response sequence H (k) at the current moment in the indoor environment,
n is the power of 2 closest to the magnitude of N, according to the excitation signal x t Frequency range of (n) extracting H from H (k) r (k) Representing a useful channel frequency response sequence;
step 3, setting a matrix Buffer with fifo function, and setting H at the current moment r (k) From the end of Buffer write, the column direction of the resulting matrix represents the time dimension, and the row direction represents [ f ] c ,f c +B]Frequencies within the range, f c The initial frequency of frequency modulation is B is the frequency modulation bandwidth;
step 4, when the Buffer is in a full state, entering a step 5, otherwise, resuming the step 2 and the step 3;
step 5, trending is carried out on each column of data in the Buffer, namely a channel frequency response sequence;
step 6, rapidly calculating the autocorrelation of each column of sequences in the Buffer by using a time domain convolution theorem, and obtaining the maximum value R (k);
step 7, according to the set autocorrelation threshold R and the parameter j, selecting the front j row channel frequency response sequence with the strongest autocorrelation from the Buffer, writing into the set Final FRs matrix Buffer, and normalizing the Buffer;
step 8, synthesizing continuous waveforms of data in the FinalFRs to obtain a respiratory wave sequence currBreathwave;
and 9, smoothing the respiratory wave sequence currBreathwave synthesized at the current moment, namely, the respiratory wave monitored in real time, realizing the visualization of the respiratory wave, continuously jumping to the operation of the step 2 for circulation, and realizing the respiratory monitoring of the indoor object in a resting state.
2. The method of location independent respiration monitoring based on acoustic environmental response of claim 1, wherein the excitation signal x t (n) is formed by splicing a section of windowed linear or sinusoidal frequency modulation signal with an all-0 sequence, the frequency modulation signal period is T, the duration of the all-0 sequence signal is T ', the total duration of the signal is t=T+T', namely the duty ratio of the signal is
Wherein the frequency of the chirp signal varies with time f 1 (n) and phase variation with time u 1 (n) are as follows:
frequency of sinusoidal FM signal varies with time f 2 (n) and phase variation with time u 2 (n) are as follows:
the expression of the frequency modulated signal is as follows:
x t1 (n)＝cos(u(n))
u (n) takes u 1 (n) or u 2 (n)；
The expression for the all 0 sequence signal is as follows:
x t2 (n)＝[0,2*0,…n*0]
the transmitted excitation signal expression is as follows:
x t (n)＝x t1 (Tf s )+x t2 (T′f s )
where n is the sampling point number, i.e., n=1, 2, …, tf s ，f s For the system sampling frequency, T s For the sampling period of the system,
3. the location independent respiration monitoring method based on acoustic environment response according to claim 1, characterized in that the calculation formula of the channel frequency response sequence H (k) at the present moment is as follows:
wherein H (k) represents the current time and the frequency is
Is set to be a channel frequency response sequence of (c),
n is the mostApproximately to the power of 2 of size n, X t (k) Representing the frequency spectrum of the excitation signal, X r (k) Representing the frequency spectrum of the received signal, since the frequency range of the excitation signal is [ f c ,f c +B]Therefore f c <f<f c +B, the final range of values for k is as follows:
thereby extracting H r (k)。
4. The location independent respiration monitoring method based on acoustic environment response according to claim 1, characterized in that the matrix Buffer size is a x b, expressed as follows:
wherein,,
H r_a (k) H representing the time of the a-th row of the deposit r (k) Sequence.
5. The method for location independent respiration monitoring based on acoustic environment response according to claim 4, wherein the Buffer is full state indicating that the matrix Buffer is full H r (k) The number of rows of the sequence is equal to a; the trending of each column of data in the Buffer is to subtract an optimal fitting curve from each column of data.
6. The location independent respiration monitoring method based on acoustic environment response according to claim 1, characterized in that the calculation formula of the maximum value R (k) is as follows:
R(k)＝max(IFFT(X(w)X * (w)))
wherein X (w) represents the spectrum of each column in the Buffer, X * (w) represents a conjugated spectrum.
7. The method for monitoring breath based on acoustic environment response and independent of position according to claim 1, wherein the writing of the set FinalFRs matrix buffer is to initialize the FinalFRs matrix to 0 matrix with a size of a x j, and the normalization is the maximum normalization.
8. The method for location independent respiration monitoring based on acoustic environment response according to claim 1, wherein in step 8, the following traversal operation is performed on the co-j columns of data in FinalFRs:
step (1), adding corresponding elements of the sequence, and recording as Wave;
step (2), taking absolute values of the Wave sequences, and adding and summing all elements to obtain a value which is Sum;
step (3), if Sum < LastSum, wave subtracts the current first column sequence amplified by 2 times, and then executes step (2) again to avoid waveforms with phase differences of nearly half period, wherein LastSum is the value obtained in the last traversal, and is 0,l =1, 2, …, j;
step (4), assigning the current Sum to LastSum;
and after traversing, dividing each numerical value of the Wave sequence by j, and averaging to obtain the current synthesized respiratory Wave sequence, which is recorded as currBreathwave.
9. The method for position independent respiration monitoring based on acoustic environment response according to claim 8, wherein in order to enable continuous refreshing of the respiration waveform, it is necessary to avoid the waveform of the phase difference of nearly half period at the previous time, the following steps are adopted:
step (1), adding two sequences of currBreathwave and LastBreathwave separately and corresponding elements, and operating according to the step (2) to obtain values respectively marked as Sum2, sum1 and tempSum, wherein LastBreathwave is a respiratory wave sequence synthesized at the last moment;
and (2) inverting each value of the currBreathwave sequence if tempSum is less than or equal to Sum2 or tempSum is less than or equal to Sum 1.
10. The method for monitoring position-independent respiration based on acoustic environment response according to claim 1, wherein the acoustic wave transmitter and the acoustic wave receiver are arranged at the same place or different places indoors, the indoor environment is relatively closed, and a single-shot or multiple single-shot form is adopted.
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