CN113708784B - Remote non-contact respiration rate estimation method, system and storage medium - Google Patents
Remote non-contact respiration rate estimation method, system and storage medium Download PDFInfo
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Abstract
The invention provides a remote non-contact type respiration rate estimation method, a remote non-contact type respiration rate estimation system and a storage medium. The method comprises the following steps: receiving radio frequency signals through at least two antennas; selecting any one receiving antenna as a reference antenna, and carrying out conjugate multiplication on the channel state information of each subcarrier of the rest receiving antennas and the channel state information of the subcarrier corresponding to the selected reference antenna to construct new channel state information of each subcarrier of different antennas; carrying out filtering smoothing processing on the new channel state information in a period of time window to obtain a time domain respiration signal; obtaining the optimal mapping characteristic of new channel state information of each subcarrier according to a time domain respiration signal in a period of time window, and selecting subcarrier data with a proper state from all subcarriers to participate in subsequent respiration rate estimation; and performing time-frequency joint analysis according to the selected optimal mapping characteristics of the subcarriers, and finally determining the breathing rate of the observation target.
Description
Technical Field
The invention relates to the field of radio communication perception application, belongs to the field of signal processing, and particularly relates to a method, a system and a storage medium for realizing remote non-contact respiration rate estimation based on information acquired from commercial WiFi equipment.
Background
In daily health monitoring and medical disease diagnosis, the human respiration rate is an important physiological index for analyzing the health status of a human body. In recent years, with the rapid development of the internet of things, many researches and applications in relevant aspects exist, but the traditional respiration monitoring technology is in a contact type, a sensor is required to be in direct physical contact with a monitored human body, discomfort of the monitored human body (especially old people and infants) can be caused, the old people often forget to carry the contact type sensor, and the breathing state of the observed person can not be monitored for a long time.
To solve this problem, we can consider a non-contact respiration monitoring technique, i.e. obtaining the respiration rate of the target without any sensor being carried by the observed target. As the thorax can generate periodic and regular fluctuating motion in the normal breathing process of a human body, the influence on ubiquitous electromagnetic signals in the surrounding environment is generated, and the phenomenon can be utilized to realize non-contact human body breathing rate estimation based on radar. However, radar-based solutions often require the use of specially designed hardware, which is expensive, thereby limiting the range of applications for radar-based respiration rate estimation solutions.
At present, commercial WiFi devices are widely existed in our daily life, so we can use commercial WiFi devices to realize contactless human respiration rate estimation, and this technology has recently received a lot of attention. At first, researchers analyzed and extracted the human respiration rate according to Received Signal Strength (RSS) data obtained from WiFi devices, however, RSS is not sensitive to the tiny motion of the chest cavity, and is easily submerged by the noise of the environment and devices, so that the human respiration rate cannot be estimated stably and reliably. In contrast to RSS, channel State Information (CSI) in WiFi devices describes the energy attenuation and phase change experienced by wireless electromagnetic signals during propagation. The periodic fluctuation of the thoracic cavity caused by the human respiration can cause the channel state information to show periodic change, and the CSI is more sensitive to the tiny fluctuation of the thoracic cavity caused by the respiration, so that the CSI can be utilized to realize the estimation of the human respiration rate.
Due to the limitation of hardware of the WiFi device, the phase of the CSI may be affected by various phase errors of the receiving end, including Carrier Frequency Offset (CFO), sampling Frequency Offset (SFO), and Packet Detection Delay (PDD), and these phase offset errors are constantly changing with time and cannot be known and eliminated exactly, so that it is impossible to estimate the respiration rate by directly using the periodic change of the original CSI phase information. On the other hand, if the breathing rate is estimated only by using the periodic variation of the amplitude information of the CSI with time, since the amplitude is not phase-sensitive to the minute motion in the environment, the existing method has a severe requirement on the position of the monitored target in the environment, and many areas cannot realize stable and reliable breathing rate estimation, that is, there is a problem of monitoring blind areas. In addition, because the human body is interfered by various external factors during breathing, the breathing rate is greatly changed in a short time due to the micro-motion of the body and the speaking, and the estimation of the breathing rate is influenced. The technical problem to be solved at present is how to eliminate the phase offset in the CSI signal, expand the environmental range of respiration monitoring, and achieve stable and reliable respiration rate estimation.
The closest prior art is a contactless respiration detection method and device in the university of Beijing, zhang Daqing (patent publication No. CN 110301917A). The invention receives radio frequency signals through at least two receiving antennas, and for channel state information corresponding to the received radio frequency signals, the ratio of the two is taken to construct new characteristic data reflecting the channel state information of each subcarrier, so that phase offset can be eliminated. And determining the optimal respiration detection characteristic data of each subcarrier according to the new channel state information data in a period of time, and performing fusion calculation on the optimal respiration detection characteristic data obtained by the plurality of subcarriers to obtain the respiration rate of the corresponding detection target.
However, in the above scheme, the CSI ratio obtained by two antennas on the same device is used to eliminate the phase offset, but the effect is not very good, and the method for constructing new channel state information data of each subcarrier is not efficient and consumes time. The method for estimating the respiratory rate is not stable and accurate enough, the first peak value of the fused autocorrelation function is directly selected to be used as the corresponding respiratory cycle estimation, and the method is easy to generate large errors.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a remote non-contact type respiration rate estimation method, a remote non-contact type respiration rate estimation system and a storage medium, which can eliminate phase shift, enlarge the respiration monitoring range, solve the blind zone problem of respiration monitoring, and provide a stable and accurate respiration rate estimation result, thereby realizing remote non-contact type stable and effective respiration rate estimation.
The invention provides a remote non-contact type respiration rate estimation method, which comprises the following steps:
step 1, receiving radio frequency signals from a transmitting device T by a receiving device R, wherein the receiving device R is provided with two or more receiving antennas;
step 2, each receiving antenna comprises channel state information of a plurality of subcarriers, any receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is multiplied by the channel state information of the subcarrier corresponding to the selected reference antenna in a conjugate mode, new channel state information data of each subcarrier of different antennas are constructed, and phase offset in signals is eliminated;
step 3, carrying out filtering smoothing treatment on the new channel state information in a period of time window, eliminating the influence of noise and obtaining a time domain respiration signal;
step 4, according to the time domain respiration signal in a period of time window, carrying out feature analysis on the filtered time domain respiration signal to obtain the optimal mapping feature of the new channel state information of each subcarrier, and selecting subcarrier data with a proper state from all subcarriers to participate in subsequent respiration rate estimation;
and 5, performing time-frequency joint analysis according to the selected optimal mapping characteristics of the subcarriers, and finally determining the respiration rate of the observation target.
Further, in step 1, the radio frequency signal is a radio frequency signal of the same transmitting device, and the transmitting device includes a commercial WiFi device.
Further, in step 2, the conjugate multiplication includes: sampling the RF signals of a pair of receiving antennas on a receiving device R at a system sampling rate F s Calculating to obtain channel state information on the subcarrier f at the moment t, wherein the channel state information is complex; suppose H 1 (t, f) is channel state information of the reference antenna; suppose that the channel state information of the remaining receiving antennas is H, respectively 2 (t, f), \8230 \ 8230; new channel state information is constructed for the channel state information of the rest receiving antennas and is respectively recorded as
Wherein the content of the first and second substances,
8230, wherein the operation is conjugation.
Further, in step 3, the filtering and smoothing the new channel state information within the time window includes: the size of a time window is W, and the unit is second; and smoothing the obtained new channel state information data by adopting an S-G filter, eliminating the influence of a noise signal and obtaining a filtered time domain respiration signal.
Further, in step 4, the performing feature analysis on the filtered time-domain respiratory signal to obtain an optimal mapping feature of the new channel state information of each subcarrier includes:
step 41, extracting the real part I (t, f) and the imaginary part Q (t, f) of the filtered channel state information within a period of time window; the size of a time window is W, and the unit is second; the channel state information in the time window is all sampling points in the time interval, namely sampling points between t-W seconds and t seconds; system sampling rate F s So that the sampling points in the time window have W × F s A plurality of;
step 42, for the real part I = [ I (t-W + 1/F) in W seconds s ),I(t-W+2/F s ),...,I(t)]And imaginary part Q = [ Q (t-W + 1/F) s ),Q(t-W+2/F s ),...,Q(t)]By vectors [ cos theta, sin theta ] associated with different angles],0≤θ<Pi performing inner product; the real part and imaginary part formulas are convenient to express, and f is omitted; thereby obtaining different candidate mapping characteristics y (t, theta) = I (t) cos theta + Q (t) sin theta, wherein 0 is not less than theta<π；
Step 43, assuming that the serial number of the current subcarrier is k, calculating the signal-to-noise ratios (SNRs) of different candidate mapping characteristics in the time window, and taking the candidate mapping characteristic with the largest SNR as the optimal mapping characteristic Y of the current subcarrier k (t)＝[y k (t-W+1/F s ),y k (t-W+2/F s ),...,y k (t)]。
Further, in step 43, the calculating the SNR for the signal-to-noise ratios of the different candidate mapping features within the time window includes: and converting the candidate mapping feature data in W seconds into corresponding frequency domain signals through Fast Fourier Transform (FFT), and calculating the ratio of the maximum amplitude of the candidate mapping feature data to the sum of the amplitudes in all frequency domains to serve as the signal-to-noise ratio of the candidate mapping feature.
Furthermore, in step 4, the selecting subcarrier data with a suitable state from all subcarriers to participate in the subsequent respiration rate estimation includes: calculating SNR of optimal mapping characteristics of all subcarriers k Selecting the maximum SNR max ＝max(SNR k ) (ii) a Signal-to-noise ratio SNR for selecting those optimal mapping characteristics k Greater than or equal to 0.8 x SNR max The subcarriers are used as proper subcarrier data, the subcarriers form a set K, and the signal-to-noise ratios of the optimal mapping characteristics of the proper subcarriers meeting the requirements are normalized to obtain
Furthermore, in step 5, according to the selected optimal mapping characteristics of the subcarriers, performing time-frequency joint analysis to determine the respiration rate of the observation target, including:
step 51, performing time domain autocorrelation analysis on the optimal mapping characteristics of the kth subcarrier in the set K; calculating a breathing frequency candidate set on a frequency domain of the breathing frequency candidate set;
step 52, combining the optimal mapping characteristics of all subcarriers in the set K to generate a fused time-frequency analysis result;
and 53, calculating and determining the breathing rate of the observation target according to the obtained fused time-frequency analysis result.
Further, in step 51, performing time-domain autocorrelation analysis on the optimal mapping characteristics of the kth subcarrier in the set K, including: calculating the autocorrelation function of its time domain, in particular
Wherein
y k (t) is the optimal mapping characteristic for the kth subcarrier at time t, W x F s Is the number of samples within the time window,
is y within the time window k (t) average value.
Further, in step 51, calculating a set of breathing frequency candidates in the frequency domain thereof comprises: calculating the FFT of the time domain signal in the optimal mapping characteristic W seconds of the kth subcarrier to obtain a frequency domain signal Y k (f) (ii) a Randomizing the time sequence of the optimal mapping characteristic within W seconds, and then performing FFT (fast Fourier transform), wherein the maximum amplitude value is taken as the noise signal intensity; randomizing for multiple times to obtain corresponding noise signal intensity, sequencing in an ascending order, and taking the last but one noise signal intensity as a reference noise threshold; for frequency domain signal Y k (f) And selecting the frequency of the frequency domain signal with the amplitude larger than the reference noise threshold value as a breathing frequency candidate set, and taking the ratio of the signal amplitude to the reference noise threshold value as a confidence coefficient of the frequency domain signal.
Further, in step 52, the optimal mapping characteristics of all subcarriers in the set K are combined to generate a fused time-frequency analysis result, which includes: autocorrelation function R for each subcarrier k (t) signal-to-noise ratio SNR normalized according thereto k-norm Performing weighted summation to generate a fused time domain autocorrelation function
Taking a union set of respiratory frequency candidate sets obtained by each subcarrier, summing and sequencing corresponding confidence coefficient of the union set, and selecting a frequency corresponding to a maximum value as a coarse respiratory frequency f on a frequency domain base 。
Further, in step 53, calculating and determining a respiration rate of the observation target according to the obtained fused time-frequency analysis result, including: according to the obtained rough respiratory frequency f on the frequency domain base Calculated to get the timeCorresponding respiratory cycle interval [ T ] on domain 1 ,T 2 ]Is concretely provided with
Wherein N is fft The number of points selected for fast fourier transform; for a fused time-domain autocorrelation function R ave (T) is selected within the interval [ T 1 ,T 2 ]Index i corresponding to the first peak above and sampling rate F s The ratio of the final respiration cycle to the total respiration cycle
The unit is second/time; converting the breathing cycle into a breathing rate, i.e.
Units are times/minute.
The invention provides a remote non-contact respiration rate estimation system, which is characterized by comprising the following components:
the data transmitting and receiving module is used for receiving radio frequency signals from the transmitting equipment T by the receiving equipment R, and the receiving equipment R is provided with two or more receiving antennas;
each receiving antenna comprises channel state information of a plurality of subcarriers, any one receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is multiplied by the channel state information of the subcarrier corresponding to the selected reference antenna in a conjugate mode, new channel state information data of each subcarrier of different antennas are constructed, and phase offset in signals is eliminated;
the noise elimination module is used for filtering and smoothing the new channel state information in a period of time window, eliminating the influence of noise and obtaining a time domain respiration signal;
the subcarrier data selection module is used for performing feature analysis on the filtered time domain respiration signal according to the time domain respiration signal in a time window to obtain the optimal mapping feature of new channel state information of each subcarrier, and selecting subcarrier data with a proper state from all subcarriers to participate in subsequent respiration rate estimation;
and the respiration rate determining module is used for carrying out time-frequency joint analysis according to the optimal mapping characteristics of the selected subcarriers and finally determining the respiration rate of the observation target.
The invention provides a computer-readable storage medium, which is characterized by comprising a stored program, wherein the program is used for controlling a device where the computer-readable storage medium is located to execute the remote non-contact respiration rate estimation method when the program is executed.
In the invention, each receiving antenna comprises channel state information of a plurality of subcarriers, any one receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is multiplied by the channel state information of the subcarrier corresponding to the selected reference antenna in a conjugate mode, new channel state information data of each subcarrier of different antennas are constructed, phase offset in signals is eliminated, and the strength of respiratory signals is enhanced.
In addition, according to the selected optimal mapping characteristics of the subcarriers, time-frequency joint analysis is carried out, and finally the breathing rate of the observation target is determined.
The invention has the advantages that: the invention provides a remote non-contact type respiration rate estimation method, which is used for realizing the respiration rate estimation of an observation target by utilizing a wireless radio frequency signal transmitted by commercial WiFi equipment. The problem of phase offset on commercial WiFi hardware is solved by utilizing the conjugate multiplication of the channel state information of two receiving antennas on the same receiving device, the strength of a respiratory signal is enhanced, the optimal mapping characteristic analysis is further utilized, the detectable range of respiratory rate estimation is greatly expanded, and the efficiency of an optimal mapping characteristic analysis algorithm is improved. In addition, the accuracy and stability of respiration rate estimation are improved and the robustness of the method to environmental interference is enhanced by using time-frequency joint analysis.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description in the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 shows a block flow diagram of a remote contactless respiration rate estimation method according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
Interpretation of related terms:
RSS: received Signal Strength.
CSI: channel State Information, which describes the physical State of the wireless electromagnetic signal propagation space, is a complex number.
Breathing rate: the number of breaths per minute of a human.
The invention provides a remote non-contact type respiration rate estimation method, which comprises the following steps:
step 1, receiving radio frequency signals from a transmitting device T by a receiving device R, wherein the receiving device R is provided with two or more receiving antennas;
step 2, each receiving antenna comprises channel state information of a plurality of subcarriers, any receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is multiplied by the channel state information of the subcarrier corresponding to the selected reference antenna in a conjugate mode, new channel state information data of each subcarrier of different antennas are constructed, and phase offset in signals is eliminated;
step 3, carrying out filtering smoothing processing on the new channel state information in a period of time window, eliminating the influence of noise and obtaining a time domain respiration signal;
step 4, according to the time domain respiration signal in a period of time window, carrying out feature analysis on the filtered time domain respiration signal to obtain the optimal mapping feature of the new channel state information of each subcarrier, and selecting subcarrier data with a proper state from all subcarriers to participate in the subsequent respiration rate estimation;
and 5, performing time-frequency joint analysis according to the selected optimal mapping characteristics of the subcarriers, and finally determining the respiration rate of the observation target.
Further, in step 1, the radio frequency signal is a radio frequency signal of the same transmitting device, and the transmitting device includes a commercial WiFi device.
Further, in step 2, the conjugate multiplication comprises: sampling the RF signals of a pair of receiving antennas on a receiving device R at a system sampling rate F s The channel state information on the subcarrier f at the time t is obtained by calculation and is complex; suppose H 1 (t, f) is channel state information of the reference antenna; suppose that the channel state information of the remaining receiving antennas is H, respectively 2 (t, f), \8230 \ 8230; new channel state information is constructed for the channel state information of the rest receiving antennas and is respectively recorded as
823060, 8230; wherein the content of the first and second substances,
823060, 8230, wherein the operation is conjugation.
Further, in step 3, the filtering and smoothing the new channel state information within the time window includes: the size of a time window is W, and the unit is second; and smoothing the obtained new channel state information data by adopting an S-G filter, eliminating the influence of a noise signal and obtaining a filtered time domain respiration signal.
Further, in step 4, the performing feature analysis on the filtered time-domain respiratory signal to obtain an optimal mapping feature of the new channel state information of each subcarrier includes:
step 41, extracting real part I (t, f) and imaginary part Q (t, f) of the filtered channel state information within a period of time window; the size of a time window is W, and the unit is second; the channel state information in the time window is all sampling points in the time interval, namely sampling points between t-W seconds and t seconds; system sampling rate F s So that the sampling points in the time window have W × F s A plurality of;
step 42, for the real part I = [ I (t-W + 1/F) in W seconds s ),I(t-W+2/F s ),...,I(t)]And imaginary part Q = [ Q (t-W + 1/F) s ),Q(t-W+2/F s ),...,Q(t)]By vectors [ cos theta, sin theta ] associated with different angles],0≤θ<Pi performing inner product; the real part and imaginary part formulas are convenient to express, and f is omitted; thereby obtaining different candidate mapping characteristics y (t, theta) = I (t) cos theta + Q (t) sin theta, wherein 0 is not more than theta<π；
Step 43, assuming that the serial number of the current subcarrier is k, calculating the SNR of different candidate mapping features in the time window, and taking the candidate mapping feature with the largest SNR as the optimal mapping feature Y of the current subcarrier k (t)＝[y k (t-W+1/F s ),y k (t-W+2/F s ),...,y k (t)]。
Further, in step 43, the calculating the SNR for the signal-to-noise ratios of the different candidate mapping features within the time window includes: and converting the candidate mapping feature data in W seconds into corresponding frequency domain signals through Fast Fourier Transform (FFT), and calculating the ratio of the maximum amplitude of the candidate mapping feature data to the sum of the amplitudes in all frequency domains to serve as the signal-to-noise ratio of the candidate mapping feature.
Furthermore, in step 4, the selecting subcarrier data with a suitable state from all subcarriers to participate in the subsequent respiration rate estimation includes: calculating SNR of optimal mapping characteristics of all subcarriers k Selecting the maximum SNR max ＝max(SNR k ) (ii) a Signal-to-noise ratio SNR for selecting those optimal mapping characteristics k Greater than or equal to 0.8 x SNR max As suitable subcarrier data, which form a set K, andand normalizing the signal-to-noise ratio of the optimal mapping characteristics of the proper subcarriers meeting the requirements to obtain the signal-to-noise ratio
Furthermore, in step 5, according to the selected optimal mapping feature of the sub-carrier, performing time-frequency joint analysis to determine the respiration rate of the observation target, including:
step 51, performing time domain autocorrelation analysis on the optimal mapping characteristics of the kth subcarrier in the set K; calculating a breathing frequency candidate set on a frequency domain of the breathing frequency candidate set;
step 52, combining the optimal mapping characteristics of all subcarriers in the set K to generate a fused time-frequency analysis result;
and 53, calculating and determining the breathing rate of the observation target according to the obtained fused time-frequency analysis result.
Further, in step 51, performing time-domain autocorrelation analysis on the optimal mapping characteristics of the kth subcarrier in the set K, including: calculating the autocorrelation function of its time domain, in particular R k (t)＝[r k (0),r k (1),...,r k (i),...,r k (W*F s -1)]Wherein
y k (t) is the optimal mapping characteristic for the kth subcarrier at time t, W x F s Is the number of samples within the time window,
is y within the time window k (t) average value.
Further, in step 51, calculating a set of breathing frequency candidates in the frequency domain thereof comprises: calculating the FFT of the time domain signal in the optimal mapping characteristic W seconds of the kth subcarrier to obtain a frequency domain signal Y k (f) (ii) a Randomizing the time sequence of the optimal mapping features within W seconds, performing FFT, and taking the maximum amplitude value as the noiseAn acoustic signal strength; randomizing for multiple times to obtain corresponding noise signal intensity, sequencing in ascending order, and taking the last but one noise signal intensity as a reference noise threshold; for frequency domain signal Y k (f) The frequency of the frequency domain signal with the amplitude larger than the reference noise threshold is selected as a breathing frequency candidate set, and the ratio of the signal amplitude to the reference noise threshold is used as a confidence coefficient of the frequency domain signal.
Further, in step 52, generating a fused time-frequency analysis result by combining the optimal mapping features of all the subcarriers in the set K, including: autocorrelation function R for each subcarrier k (t) signal-to-noise ratio SNR normalized according thereto k-norm Performing weighted summation to generate a fused time domain autocorrelation function
For the respiratory frequency candidate set union set obtained by each subcarrier, summing the corresponding confidence coefficient and sequencing, and selecting the frequency corresponding to the maximum value as the coarse respiratory frequency f on the frequency domain base 。
Further, in step 53, calculating and determining a respiration rate of the observation target according to the obtained fused time-frequency analysis result, including: according to the obtained rough respiratory frequency f on the frequency domain base Calculating to obtain a respiration cycle interval [ T ] corresponding to the time domain 1 ,T 2 ]Specifically, it is
Wherein N is fft The number of points selected for fast fourier transform; for a fused time-domain autocorrelation function R ave (T) is selected within the interval [ T 1 ,T 2 ]Index i corresponding to the first peak above and sampling rate F s The ratio of the final respiration cycle to the total respiration cycle
The unit is second/time; converting the respiratory cycle to a respiratory rate, i.e.
Units are times/minute.
The invention provides a remote non-contact respiration rate estimation system, which is characterized by comprising the following components:
the data transmitting and receiving module is used for receiving radio frequency signals from the transmitting equipment T by the receiving equipment R, and the receiving equipment R is provided with two or more receiving antennas;
a phase offset elimination module, wherein each receiving antenna comprises channel state information of a plurality of subcarriers, any receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is conjugate multiplied with the channel state information of the subcarrier corresponding to the selected reference antenna, new channel state information data of each subcarrier of different antennas are constructed, and phase offset in signals is eliminated;
the noise elimination module is used for carrying out filtering smoothing processing on the new channel state information in a period of time window, eliminating the influence of noise and obtaining a time domain respiration signal;
the subcarrier data selection module is used for performing feature analysis on the filtered time domain respiration signal according to the time domain respiration signal in a time window to obtain the optimal mapping feature of new channel state information of each subcarrier, and selecting subcarrier data with a proper state from all subcarriers to participate in subsequent respiration rate estimation;
and the respiration rate determining module is used for carrying out time-frequency joint analysis according to the optimal mapping characteristics of the selected subcarriers and finally determining the respiration rate of the observation target.
The invention provides a computer-readable storage medium, which is characterized by comprising a stored program, wherein the program is used for controlling a device where the computer-readable storage medium is located to execute the remote non-contact respiration rate estimation method when the program is executed.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (11)
1. A remote non-contact respiration rate estimation method is characterized in that,
step 1, receiving radio frequency signals from a transmitting device T by a receiving device R, wherein the receiving device R is provided with two or more receiving antennas;
step 2, each receiving antenna comprises channel state information of a plurality of subcarriers, any receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is multiplied by the channel state information of the subcarrier corresponding to the selected reference antenna in a conjugate mode, new channel state information data of each subcarrier of different antennas are constructed, and phase offset in signals is eliminated;
step 3, carrying out filtering smoothing treatment on the new channel state information in a period of time window, eliminating the influence of noise and obtaining a time domain respiration signal;
step 4, according to the time domain respiration signal in a period of time window, carrying out feature analysis on the filtered time domain respiration signal to obtain the optimal mapping feature of the new channel state information of each subcarrier, and selecting subcarrier data with a proper state from all subcarriers to participate in subsequent respiration rate estimation;
the performing feature analysis on the filtered time domain respiration signal to obtain the optimal mapping feature of the new channel state information of each subcarrier includes:
extracting real and imaginary parts of the filtered channel state information within a time window; performing inner product on the real part and the imaginary part and different angle vectors to obtain different candidate mapping characteristics; wherein the value range of the angle theta is more than or equal to 0 and less than phi; calculating signal-to-noise ratios (SNRs) of different candidate mapping characteristics in a time window, and taking the candidate mapping characteristic with the largest SNR as the optimal mapping characteristic of the current subcarrier;
and 5, performing time-frequency joint analysis according to the selected optimal mapping characteristics of the subcarriers, and finally determining the breathing rate of the observation target.
2. The method according to claim 1, wherein in step 1, the rf signal is an rf signal of the same transmitting device, and the transmitting device comprises a commercial WiFi device.
3. The method of claim 1, wherein in step 2, the conjugate multiplication comprises: sampling radio frequency signals of a pair of receiving antennas on a receiving device R, wherein the system sampling rate is F s Calculating to obtain channel state information on the subcarrier f at the moment t, wherein the channel state information is complex; suppose H 1 (t, f) is channel state information of the reference antenna; suppose that the channel state information of the remaining receiving antennas is H, respectively 2 (t, f), \8230 \ 8230; new channel state information is constructed for the channel state information of the rest receiving antennas and is respectively recorded as
\8230; wherein, the first and the second end of the pipe are connected with each other,
8230, wherein the operation is conjugation.
4. The method of claim 1, wherein the step 3 of smoothing the new channel state information within the time window comprises: the size of a time window is W, and the unit is second; and smoothing the obtained new channel state information data by adopting an S-G filter, eliminating the influence of a noise signal and obtaining a filtered time domain respiratory signal.
5. The method according to claim 1, wherein in step 4, the performing feature analysis on the filtered time-domain respiration signal to obtain an optimal mapping feature of new channel state information of each subcarrier includes:
step 41, extracting the real part I (t, f) and the imaginary part Q (t, f) of the filtered channel state information within a period of time window; the size of a time window is W, and the unit is second; the channel state information in the time window is all sampling points in the time interval, namely sampling points between t-W seconds and t seconds; system sampling rate F s So that the sampling points in the time window have W × F s A plurality of;
step 42, for the real part I = [ I (t-W + 1/F) in W seconds s ),I(t-W+2/F s ),...,I(t)]And imaginary part Q = [ Q (t-W + 1/F) s ),Q(t-W+2/F s ),...,Q(t)]By vectors [ cos θ, sin θ ] different from the angle of the other],0≤θ<Pi performing inner product; the real part and imaginary part formulas are convenient to express, and f is omitted; thereby obtaining different candidate mapping characteristics y (t, theta) = I (t) cos theta + Q (t) sin theta, wherein 0 is not more than theta<π；
Step 43, assuming that the serial number of the current subcarrier is k, calculating the SNR of different candidate mapping features in the time window, and taking the candidate mapping feature with the largest SNR as the optimal mapping feature Y of the current subcarrier k (t)＝[y k (t-W+1/F s ),y k (t-W+2/F s ),...,y k (t)]。
6. The method according to claim 1, wherein in step 4, said selecting proper sub-carrier data from all sub-carriers to participate in the subsequent respiration rate estimation comprises: calculating SNR of optimal mapping characteristics of all subcarriers k Selecting the maximum SNR max ＝max(SNR k ) (ii) a SNR for selecting those optimal mapping characteristics k Greater than or equal to 0.8 x SNR max The subcarriers of (b) are taken as proper subcarrier data, the subcarriers form a set K, and the signal-to-noise ratios of the optimal mapping characteristics of the proper subcarriers which meet the requirements are normalized to obtain
7. The method of claim 5, wherein the step 43 of calculating the SNR of the different candidate mapping features within the time window comprises: and converting the candidate mapping feature data in W seconds into corresponding frequency domain signals through Fast Fourier Transform (FFT), and calculating the ratio of the maximum amplitude of the candidate mapping feature data to the sum of the amplitudes in all frequency domains to serve as the signal-to-noise ratio of the candidate mapping feature.
8. The method of claim 1, wherein in step 5, performing time-frequency joint analysis according to the selected optimal mapping feature of the sub-carriers to determine the respiration rate of the observation target, comprises:
step 51, performing time domain autocorrelation analysis on the optimal mapping characteristics of the kth subcarrier in the set K, and calculating a respiratory frequency candidate set on the frequency domain of the optimal mapping characteristics;
step 52, combining the optimal mapping characteristics of all subcarriers in the set K to generate a fused time-frequency analysis result;
and 53, calculating and determining the breathing rate of the observation target according to the obtained fused time-frequency analysis result.
9. The method according to claim 8, wherein in step 51, performing a time-domain autocorrelation analysis on the optimal mapping characteristics of the kth subcarrier in the set K includes: calculating the autocorrelation function of its time domain, in particular R k (t)＝[r k (0),r k (1),...,r k (i),...,r k (W*F s -1)]Wherein
y k (t) is the optimal mapping characteristic for the kth subcarrier at time t, W x F s Is the number of samples within the time window,
is y within the time window k (t) average value;
in step 51, calculating a respiratory frequency candidate set in a frequency domain thereof includes: calculating the FFT of the time domain signal in the optimal mapping characteristic W seconds of the kth subcarrier to obtain a frequency domain signal Y k (f) (ii) a Randomizing the time sequence of the optimal mapping characteristic within W seconds, and then performing FFT (fast Fourier transform), wherein the maximum amplitude value is taken as the noise signal intensity; randomizing for multiple times to obtain corresponding noise signal intensity, sequencing in ascending order, and taking the last but one noise signal intensity as a reference noise threshold; for frequency domain signal Y k (f) Selecting the frequency of the frequency domain signal with the amplitude larger than the reference noise threshold value as a breathing frequency candidate set, and taking the ratio of the signal amplitude to the reference noise threshold value as a confidence coefficient of the frequency domain signal;
in step 52, generating a fused time-frequency analysis result by combining the optimal mapping characteristics of all subcarriers in the set K, including: autocorrelation function R for each subcarrier k (t) signal-to-noise ratio SNR normalized according to it k-norm Performing weighted summation to generate a fused time domain autocorrelation function
Taking a union set of respiratory frequency candidate sets obtained by each subcarrier, summing and sequencing corresponding confidence coefficient of the union set, and selecting a frequency corresponding to a maximum value as a coarse respiratory frequency f on a frequency domain base ；
In step 53, calculating and determining the respiration rate of the observation target according to the obtained fused time-frequency analysis result, including: according to the obtained rough respiratory frequency f on the frequency domain base Calculating to obtain a respiration cycle interval [ T ] corresponding to the time domain 1 ,T 2 ]Is concretely provided with
Wherein N is fft The number of points selected for fast Fourier transform; for a fused time-domain autocorrelation function R ave (T) selecting in the interval [ T ] 1 ,T 2 ]Index i corresponding to the first peak above and sampling rate F s The ratio of the ratio is taken as the final respiratory cycle
The unit is second/time; converting the respiratory cycle to a respiratory rate, i.e.
Units are times/minute.
10. A remote contactless respiration rate estimation system comprising:
the data transmitting and receiving module is used for receiving radio frequency signals from the transmitting equipment T by the receiving equipment R, and the receiving equipment R is provided with two or more receiving antennas;
each receiving antenna comprises channel state information of a plurality of subcarriers, any one receiving antenna is selected as a reference antenna, the channel state information of each subcarrier of the rest receiving antennas is multiplied by the channel state information of the subcarrier corresponding to the selected reference antenna in a conjugate mode, new channel state information data of each subcarrier of different antennas are constructed, and phase offset in signals is eliminated;
the noise elimination module is used for filtering and smoothing the new channel state information in a period of time window, eliminating the influence of noise and obtaining a time domain respiration signal;
the subcarrier data selection module is used for performing feature analysis on the filtered time domain respiration signal according to the time domain respiration signal in a time window to obtain the optimal mapping feature of new channel state information of each subcarrier, and selecting subcarrier data with a proper state from all subcarriers to participate in subsequent respiration rate estimation;
the performing feature analysis on the filtered time domain respiratory signal to obtain the optimal mapping feature of the new channel state information of each subcarrier includes:
extracting real and imaginary parts of the filtered channel state information within a time window; performing inner product on the real part and the imaginary part and different angle vectors to obtain different candidate mapping characteristics; wherein the value range of the angle theta is more than or equal to 0 and less than phi; calculating signal-to-noise ratios (SNRs) of different candidate mapping characteristics in a time window, and taking one candidate mapping characteristic with the largest SNR as the optimal mapping characteristic of the current subcarrier;
and the respiration rate determining module is used for carrying out time-frequency joint analysis according to the optimal mapping characteristics of the selected subcarriers and finally determining the respiration rate of the observation target.
11. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method of any of claims 1-9.
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