CN111766442B - Method, device and equipment for determining human respiratory waveform and readable storage medium - Google Patents
Method, device and equipment for determining human respiratory waveform and readable storage medium Download PDFInfo
- Publication number
- CN111766442B CN111766442B CN202010704894.8A CN202010704894A CN111766442B CN 111766442 B CN111766442 B CN 111766442B CN 202010704894 A CN202010704894 A CN 202010704894A CN 111766442 B CN111766442 B CN 111766442B
- Authority
- CN
- China
- Prior art keywords
- waveform
- human body
- frequency
- waveforms
- respiratory
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/02—Arrangements for measuring frequency, e.g. pulse repetition rate; Arrangements for measuring period of current or voltage
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
- G01R23/165—Spectrum analysis; Fourier analysis using filters
- G01R23/167—Spectrum analysis; Fourier analysis using filters with digital filters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R25/00—Arrangements for measuring phase angle between a voltage and a current or between voltages or currents
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
- G01R29/0807—Measuring electromagnetic field characteristics characterised by the application
- G01R29/0814—Field measurements related to measuring influence on or from apparatus, components or humans, e.g. in ESD, EMI, EMC, EMP testing, measuring radiation leakage; detecting presence of micro- or radiowave emitters; dosimetry; testing shielding; measurements related to lightning
- G01R29/0821—Field measurements related to measuring influence on or from apparatus, components or humans, e.g. in ESD, EMI, EMC, EMP testing, measuring radiation leakage; detecting presence of micro- or radiowave emitters; dosimetry; testing shielding; measurements related to lightning rooms and test sites therefor, e.g. anechoic chambers, open field sites or TEM cells
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
- G01R29/0864—Measuring electromagnetic field characteristics characterised by constructional or functional features
- G01R29/0878—Sensors; antennas; probes; detectors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/08—Measuring electromagnetic field characteristics
- G01R29/0864—Measuring electromagnetic field characteristics characterised by constructional or functional features
- G01R29/0892—Details related to signal analysis or treatment; presenting results, e.g. displays; measuring specific signal features other than field strength, e.g. polarisation, field modes, phase, envelope, maximum value
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Engineering & Computer Science (AREA)
- Animal Behavior & Ethology (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Pulmonology (AREA)
- Pathology (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Physiology (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The application discloses a human respiratory waveform determining method, device, equipment and computer readable storage medium, wherein the method comprises the following steps: transmitting the frequency-modulated electromagnetic wave to the human body to be detected, receiving the reflected frequency-modulated electromagnetic wave, and obtaining an intermediate frequency signal by mixing the received frequency-modulated electromagnetic wave; extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body; extracting a phase from the signal, and processing the phase to obtain an original respiration waveform; and carrying out signal matching on the original breathing waveform to determine a waveform matched with human breathing, and combining the waveforms to obtain the breathing waveform of the human body to be detected. According to the technical scheme, non-contact detection of human breath to be detected is achieved by transmitting the frequency-modulated electromagnetic wave, receiving the reflected frequency-modulated electromagnetic wave and processing the frequency-modulated electromagnetic wave, so that the experience of user breath detection is improved.
Description
Technical Field
The present application relates to the field of human breath detection technology, and more particularly, to a method, apparatus, device, and computer readable storage medium for determining a human breath waveform.
Background
The health condition, sleep condition and the like of the human body can be obtained by detecting the respiration of the human body.
At present, most of the existing human breath detection is contact type, namely, a detection device capable of detecting human breath is directly contacted with a human body so as to realize human breath detection, and the contact type detection mode can influence users in different states to different degrees, so that the user experience degree can be reduced. For example: for a user in a sleep state, contact respiration detection may affect the sleep quality of the user, which not only reduces the experience of the respiration detection of the user, but also cannot better detect the sleep respiration quality.
In summary, how to improve the experience of the breath detection of the user is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the foregoing, it is an object of the present application to provide a method, apparatus, device and computer readable storage medium for determining a respiratory waveform of a human body, for improving the experience of respiratory detection of a user.
In order to achieve the above object, the present application provides the following technical solutions:
a method of determining a human respiratory waveform, comprising:
transmitting frequency-modulated electromagnetic waves to a human body to be detected, receiving the frequency-modulated electromagnetic waves reflected by the human body to be detected, and obtaining intermediate frequency signals by mixing the received and transmitted frequency-modulated electromagnetic waves;
extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT (fast Fourier transform) calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body;
extracting a phase from the signal and processing the phase to obtain an original respiration waveform;
and carrying out signal matching on the original respiratory waveform to determine a waveform matched with human respiration, and combining the waveforms to obtain the respiratory waveform of the human body to be detected.
Preferably, after processing the phases to obtain the original respiration waveform, the method further comprises:
the raw respiratory waveform is high pass filtered to filter out baseline values.
Preferably, before the waveforms are combined to obtain the respiratory waveform of the human body to be tested, the method further includes:
calculating waveform similarity among the waveforms, and classifying the waveforms with continuous FFT result indexes and waveform similarity larger than a preset value into a group;
correspondingly, the waveforms are combined to obtain the breathing waveform of the human body to be detected, which comprises the following steps:
and combining the waveforms in each group to obtain the breathing waveforms of the human body to be tested.
Preferably, the high-pass filtering of the original respiration waveform includes:
the raw respiratory waveform is input into a digital high-pass filter to high-pass filter the raw respiratory waveform with the digital high-pass filter.
Preferably, processing the phase to obtain an original respiration waveform includes:
and unwrapping the phase to obtain an original respiration waveform.
Preferably, band-pass filtering is performed on each FFT result, including:
and inputting the FFT result into a corresponding digital band-pass filter so as to carry out band-pass filtering on the FFT result by utilizing the digital band-pass filter.
Preferably, after the waveforms are combined to obtain the respiratory waveform of the human body to be tested, the method further includes:
and obtaining the respiratory frequency of the human body to be tested according to the respiratory waveform.
A human respiratory waveform determining apparatus comprising:
the intermediate frequency signal obtaining module is used for transmitting frequency-modulated electromagnetic waves to a human body to be detected, receiving the frequency-modulated electromagnetic waves reflected by the human body to be detected and obtaining intermediate frequency signals through receiving and transmitting the frequency-modulated electromagnetic waves;
the calculation module is used for extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, carrying out FFT (fast Fourier transform) calculation on each sampling point, and carrying out band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body;
the processing module is used for extracting a phase from the signal and processing the phase to obtain an original respiratory waveform;
and the signal matching module is used for carrying out signal matching on the original respiratory waveform to determine a waveform matched with human body respiration, and combining the waveforms to obtain the respiratory waveform of the human body to be detected.
A human respiratory waveform determining apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of the human breathing waveform determining method as claimed in any one of the preceding claims when executing the computer program.
A computer readable storage medium having stored therein a computer program which when executed by a processor performs the steps of the human breathing waveform determining method of any of the preceding claims.
The application provides a method, a device, equipment and a computer readable storage medium for determining human breathing waveforms, wherein the method comprises the following steps: transmitting the frequency-modulated electromagnetic wave to the human body to be detected, receiving the frequency-modulated electromagnetic wave reflected by the human body to be detected, and obtaining an intermediate frequency signal through receiving and transmitting the frequency-modulated electromagnetic wave; extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT (fast Fourier transform) calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body; extracting a phase from the signal and processing the phase to obtain an original respiration waveform; and carrying out signal matching on the original breathing waveform to determine a waveform matched with human breathing, and combining the waveforms to obtain a breathing waveform of the human body to be detected.
According to the technical scheme, the intermediate frequency signal is obtained by emitting the frequency-modulated electromagnetic wave to the human body to be detected and receiving the frequency-modulated electromagnetic wave reflected by the human body to be detected, the preset number of sampling points are extracted from the intermediate frequency signal, the signal conforming to the respiratory frequency of the human body is obtained after FFT calculation and bandpass filtering, the phase of the obtained signal is processed to obtain the original respiratory waveform, the original respiratory waveform is subjected to signal matching to determine the waveform matching with the respiratory of the human body, and the determined waveforms are combined to obtain the respiratory waveform of the human body to be detected.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flowchart of a method for determining a respiratory waveform of a human body according to an embodiment of the present application;
fig. 2 is a frequency characteristic diagram of a digital high-pass filter according to an embodiment of the present application;
fig. 3 is a frequency characteristic diagram of a digital band-pass filter according to an embodiment of the present application;
FIG. 4 is a schematic diagram of performing zero crossing statistics on waveforms to obtain frequencies according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a human breathing waveform determining device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a human breathing waveform determining apparatus according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Referring to fig. 1, which is a flowchart illustrating a method for determining a respiratory waveform of a human body according to an embodiment of the present application, the method for determining a respiratory waveform of a human body according to an embodiment of the present application may include:
s11: transmitting the frequency-modulated electromagnetic wave to the human body to be detected, receiving the frequency-modulated electromagnetic wave reflected by the human body to be detected, and obtaining the intermediate frequency signal through receiving and transmitting the frequency-modulated electromagnetic wave.
When the human body to be detected needs to be breathed, the radar sensor can be installed at a position which is at a certain distance from the human body to be detected, wherein the human body to be detected needs to be located in an effective detection area of the radar sensor, that is, the radar sensor does not need to accurately specify the installation position and the distance from the human body to be detected, only the effective detection area of the radar sensor is needed or the human body to be detected is located in the effective detection area of the radar sensor, so that installation, debugging and scheduling can be greatly reduced. After the radar sensor is installed, the radar sensor can be used for continuously transmitting frequency-modulated electromagnetic waves to a human body to be detected, the radar sensor is used for receiving the frequency-modulated electromagnetic waves reflected by the human body to be detected, and meanwhile, the frequency-modulated electromagnetic waves transmitted by the radar sensor and the received frequency-modulated electromagnetic waves can be subjected to declining treatment to obtain mixed intermediate frequency signals, so that the human body to be detected can be conveniently breathed and detected according to the intermediate frequency signals.
The above-mentioned frequency-modulated electromagnetic wave may be specifically a chirped electromagnetic wave, and of course, may be other types of frequency-modulated electromagnetic waves, and the types of the frequency-modulated electromagnetic waves are not limited in any way.
S12: extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of the human body.
After obtaining the intermediate frequency signal, a preset number of sampling points may be extracted from the intermediate frequency signal at preset time intervals (4 ms to several tens of ms, of course, which may be modified as needed), for example: 256 sampling points or 512 sampling points or other number of sampling points can be extracted from the intermediate frequency signal, wherein the preset number of sampling points contains information of each target which is located in the effective detection range of the radar sensor and is detected by the radar sensor. Taking the example that the human body to be measured is located indoors, the targets mentioned herein may include tables, chairs, beds, sofas, fans, air conditioners, windows, and the like, in addition to the human body to be measured.
After a preset number of sampling points are extracted each time, FFT (fast Fourier transform ) computation may be performed on each sampling point to obtain FFT results corresponding to each sampling point, respectively. The FFT result obtained by carrying out FFT calculation on each sampling point is complex number of a preset number of points, each point corresponds to a frequency, each frequency corresponds to an FFT result index, the modulus value of each point complex number is the amplitude characteristic of the frequency, and the corresponding phase can be calculated through the real part and the imaginary part of each point complex number.
Considering that the distance between the stationary target and the radar sensor does not change along with time, correspondingly, the FFT result of the corresponding sampling points does not change along with time, the weak fluctuation motion of the chest and other parts of the body of the human body to be detected is caused by the actions of respiration, heartbeat, nerve motion and the like, therefore, each measurement can cause the change of the distance between the human body to be detected and the radar sensor, the FFT result of the corresponding sampling points also changes along with time, based on the change, a preset number of sampling points can be extracted from the intermediate frequency signal at preset time intervals, each sampling point can be subjected to FFT calculation, each FFT result can be subjected to bandpass filtering, specifically, each FFT result corresponding to the sampling points can be respectively filtered, 256 sampling points are extracted each time, the FFT result corresponding to the 1 st sampling point can be filtered, the FFT result corresponding to the 2 nd sampling point is filtered … … to filter the interference of the stationary target through the bandpass filtering, and filter out partial high-frequency, and other irrelevant signals such as the heartbeat and nerve motion, so as to obtain signals which accord with the human body respiration frequency (the number of the preset respiratory signals can accord with the human body respiration frequency).
S13: the phase is extracted from the signal and processed to obtain the original respiration waveform.
Considering that the phase in the signals can reflect the breathing characteristics of the person more truly, after step S12 is performed, the phases can be extracted from the signals respectively, so as to obtain the breathing characteristics of the person to be tested according to the phases of the signals.
In addition, considering that the phase directly extracted may not be the phase corresponding to the respiratory waveform of the human body to be detected, the phase may be processed to obtain the real phase, so that the original respiratory waveform is obtained according to the signal and through the processing of the phase.
S14: and carrying out signal matching on the original breathing waveform to determine a waveform matched with human breathing, and combining the waveforms to obtain a breathing waveform of the human body to be detected.
After the original respiration waveform is obtained, considering that other targets which are in the same position with the human body to be detected possibly affect the original respiration waveform, signal matching can be performed on the original respiration waveform to determine the waveform matching the human body respiration. Specifically, the pre-original respiratory waveform may be input into a matched filter to perform signal matching on the original respiratory waveform by using the matched filter, or a preset number of sampling points may be all passed through the matched algorithm by using the signal matching algorithm and then output, and it is determined which original respiratory waveform accords with the respiratory waveform of the human body, wherein the higher the coincidence degree, the higher the credibility.
The principle of the signal matching algorithm is as follows:
an ideal respiratory waveform is preset, 4096 points are acquired (the number of the points can be set according to specific conditions), and the expression is X 1 ＝{x 1 ,x 2 ,x 3 ,x 4 ,x 5 …x 4095 ,x 4096 }；
Assuming the ith point of the preset number of sampling points, waiting to collect 4096 points (4096 times are extracted and 4096 times are performed with FFT calculation), and then comparing with X 1 Matching, wherein the expression of the ith point is as follows: y is Y i ＝{y 1 ,y 2 ,y 3 ,y 4 ,y 5 …y 4095 ,y 4096 }；
Using Euclidean distance as X 1 And Y i Judgment of similarity (matching degree):if D i If the value is smaller than the set threshold value, determining the ith point as a waveform matched with human breathing;
and sequentially calculating and judging the preset number of sampling points, and outputting the value of each of the preset number of sampling points meeting the threshold requirement, wherein the smaller the value is, the higher the reliability is. Assuming that K (a value set in advance according to the breathing condition of the human body) sampling points in the preset number of sampling points meet the requirement, the waveform of the breathing of the human body is met.
After determining the waveform matching the human breath, the waveform matching the human breath may be combined by an addition to obtain a respiratory waveform of the human to be measured. After the respiratory waveform of the human body to be detected is obtained, the respiratory waveform of the human body to be detected can be output, so that the respiratory condition, the health state, the sleep quality and the like of the human body to be detected can be known through the respiratory waveform.
According to the method, the respiration detection of the human body to be detected can be realized by transmitting the frequency-modulated electromagnetic wave to the human body to be detected and receiving the frequency-modulated electromagnetic wave transmitted by the human body to be detected and through the processing, and the non-contact respiration detection of the human body to be detected can be realized, so that the influence on the human body to be detected due to the respiration detection can be reduced, and the experience degree of the user for the respiration detection can be improved.
According to the technical scheme, the intermediate frequency signal is obtained by emitting the frequency-modulated electromagnetic wave to the human body to be detected and receiving the frequency-modulated electromagnetic wave reflected by the human body to be detected, the preset number of sampling points are extracted from the intermediate frequency signal, the signal conforming to the respiratory frequency of the human body is obtained after FFT calculation and bandpass filtering, the phase of the obtained signal is processed to obtain the original respiratory waveform, the original respiratory waveform is subjected to signal matching to determine the waveform matching with the respiratory of the human body, and the determined waveforms are combined to obtain the respiratory waveform of the human body to be detected.
The method for determining the respiratory waveform of the human body provided in the embodiment of the application may further include, after processing the phase to obtain the original respiratory waveform:
the original respiratory waveform is high pass filtered to filter out the baseline value.
When the human body to be detected is subjected to breath detection, baseline drift can occur to the breath waveforms due to irregular chest movements, circuit changes of the detection device and the like caused by environmental factors and human breath, and the preset number of original breath waveforms obtained in the step S13 can be respectively subjected to high-pass filtering to filter the baseline values so as to obtain waveforms which are stably changed around 0 amplitude, so that the accuracy of human breath detection is conveniently improved.
The method for determining the respiratory waveform of the human body provided by the embodiment of the application, before merging the waveforms to obtain the respiratory waveform of the human body to be detected, may further include:
calculating waveform similarity among waveforms, and classifying the waveforms with continuous FFT result indexes and waveform similarity larger than a preset value into a group;
correspondingly, the waveform is combined to obtain the breathing waveform of the human body to be detected, which can comprise:
and combining the waveforms in each group to obtain the breathing waveforms of the human body to be tested.
After the waveforms of the matched human body respiration are determined and before the determined waveforms of the matched human body respiration are combined to obtain the respiration waveforms of the human body to be detected, waveform similarity calculation can be performed between the determined waveforms of the matched human body respiration, specifically, similarity between every two waveforms is calculated, and as the more similar waveforms are higher, waveforms with waveform similarity larger than a preset value and continuous FFT result indexes can be classified into one group, and then the waveforms contained in each group can be combined by addition to correspondingly obtain the respiration waveforms of the human body to be detected.
That is, the present application not only can realize the human breath detection, but also can distinguish the breath waveforms of each human to be detected when there are a plurality of human to be detected, so as to obtain the breath waveforms of different human to be detected respectively.
The method for determining the human respiratory waveform provided by the embodiment of the application carries out high-pass filtering on the original respiratory waveform, and may include:
the raw respiratory waveform is input into a digital high-pass filter to high-pass filter the raw respiratory waveform with the digital high-pass filter.
The number of the digital high-pass filters can be preset, and the number of the digital high-pass filters can be specifically equal to the number of sampling points extracted each time, and the preset number of the digital high-pass filters corresponds to the sampling points one by one. Accordingly, when the original respiratory waveform is subjected to high-pass filtering, the original respiratory waveform can be correspondingly input into the digital high-pass filter, so that the output original respiratory waveform is subjected to high-pass filtering by the digital high-pass filter, and the baseline value is filtered. Referring specifically to fig. 2, a frequency characteristic diagram of the digital high-pass filter provided in the embodiment of the present application is shown, where an abscissa indicates a signal frequency and an ordinate indicates a signal amplitude.
Taking 256 sampling points as an example, the number of the digital high-pass filters can be 256, the original respiration waveform corresponding to the 1 st sampling point can be input into the digital high-pass filter corresponding to the 1 st sampling point, the original respiration waveform corresponding to the 2 nd sampling point can be input into the digital high-pass filter corresponding to the 2 nd sampling point, and the original respiration waveform corresponding to the 256 th sampling point of … … can be input into the digital high-pass filter corresponding to the 256 th sampling point, so that the digital high-pass filter is utilized to carry out high-pass filtering on the corresponding original respiration waveform.
The method for determining the respiratory waveform of the human body provided by the embodiment of the application processes the phase to obtain an original respiratory waveform, and may include:
the phase is unwrapped to obtain the original respiration waveform.
When the phase extracted from the signal is processed to obtain an original respiratory waveform, the phase can be specifically unwrapped to obtain a waveform with accurate change of direction, so that an original respiratory waveform with higher accuracy is obtained, and further the accuracy of human respiratory detection is conveniently improved.
The method for determining the respiratory waveform of the human body provided by the embodiment of the application carries out band-pass filtering on each FFT result, and can comprise the following steps:
the FFT result is input to a corresponding digital band-pass filter to band-pass filter the FFT result with the digital band-pass filter.
A preset number of digital band-pass filters may be preset, which corresponds to the sampling points one by one. Accordingly, when each FFT result is subjected to band-pass filtering, the FFT result corresponding to the sampling point can be input into a digital band-pass filter corresponding to the sampling point, so that the input FFT result is subjected to band-pass filtering by the digital band-pass filter, and interference of incoherent signals is filtered, and signals conforming to the respiratory frequency of a human body are obtained. Referring specifically to fig. 3, a frequency characteristic diagram of the digital band-pass filter provided in the embodiment of the present application is shown, where an abscissa indicates a signal frequency and an ordinate indicates a signal amplitude.
Taking 256 sampling points as an example, the number of the digital band-pass filters can be 256, the FFT result corresponding to the 1 st sampling point can be input into the digital band-pass filter corresponding to the 1 st sampling point, the FFT result corresponding to the 2 nd sampling point can be input into the digital band-pass filter corresponding to the 2 nd sampling point, and the FFT result corresponding to the … … 256 th sampling point can be input into the digital band-pass filter corresponding to the 256 th sampling point.
The method for determining the respiratory waveform of the human body provided by the embodiment of the application may further include, after merging the waveforms to obtain the respiratory waveform of the human body to be tested:
and obtaining the respiratory frequency of the human body to be tested according to the respiratory waveform.
After the respiratory waveform of the human body to be detected is obtained, the frequency of the respiratory waveform, namely the respiratory frequency, can be calculated by using a zero-crossing statistical algorithm, so that the health condition, the sleep condition and the like of the human body to be detected can be conveniently obtained according to the respiratory frequency.
The principle of zero crossing statistics can be specifically described with reference to fig. 4, where fig. 4 shows a schematic diagram of performing zero crossing statistics on a waveform to obtain a frequency, where the amplitude value of the waveform changes from a negative value to a positive value, and is recorded as 1 zero crossing, the amplitude value of the waveform changes from a positive value to a negative value, and is recorded as 1 zero crossing, and the number of zero crossings in one minute is counted, and then divided by 2, and the breathing frequency is 1 minute.
The embodiment of the application also provides a device for determining the respiratory waveform of the human body, referring to fig. 5, which shows a schematic structural diagram of the device for determining the respiratory waveform of the human body provided in the embodiment of the application, may include:
the intermediate frequency signal obtaining module 51 is configured to transmit a frequency-modulated electromagnetic wave to a human body to be detected, receive the frequency-modulated electromagnetic wave reflected by the human body to be detected, and obtain an intermediate frequency signal by mixing the received and transmitted frequency-modulated electromagnetic wave;
the calculating module 52 is configured to extract a preset number of sampling points from the intermediate frequency signal at preset time intervals, perform FFT calculation on each sampling point, and perform bandpass filtering on each FFT result to obtain a signal that accords with the respiratory rate of the human body;
a processing module 53, configured to extract a phase from the signal and process the phase to obtain an original respiration waveform;
the signal matching module 54 is configured to perform signal matching on the original respiratory waveform to determine a waveform matching the respiration of the human body, and combine the waveforms to obtain a respiratory waveform of the human body to be tested.
The embodiment of the application provides a human respiratory waveform determining device, which may further include:
the high-pass filtering module is used for carrying out high-pass filtering on the original breathing waveform after the phase is processed to obtain the original breathing waveform so as to filter the baseline value.
The embodiment of the application provides a human respiratory waveform determining device, which may further include:
the grouping module is used for calculating the similarity of waveforms among the waveforms before the waveforms are combined to obtain the breathing waveforms of the human body to be detected, and grouping the waveforms with continuous FFT result indexes and the similarity of the waveforms being larger than a preset value into a group;
accordingly, the signal matching module 54 may include:
and the merging unit is used for merging the waveforms in each group respectively to obtain the breathing waveforms of the human bodies to be detected.
The embodiment of the application provides a human respiratory waveform determining device, the high pass filtering module may include:
and the high-pass filtering unit is used for inputting the original breathing waveform into the digital high-pass filter so as to carry out high-pass filtering on the original breathing waveform by using the digital high-pass filter.
The embodiment of the present application provides a human respiratory waveform determining device, and the processing module 53 may include:
and the unwrapping processing unit is used for unwrapping the phase to obtain an original breathing waveform.
The embodiment of the present application provides a human respiratory waveform determining apparatus, the calculating module 52 may include:
and the band-pass filtering unit is used for inputting the FFT result into the corresponding digital band-pass filter so as to carry out band-pass filtering on the FFT result by using the digital band-pass filter.
The embodiment of the application provides a human respiratory waveform determining device, which may further include:
the respiratory frequency obtaining module is used for obtaining the respiratory frequency of the human body to be detected according to the respiratory waveform after combining the waveforms to obtain the respiratory waveform of the human body to be detected.
The embodiment of the application also provides a human respiratory waveform determining device, referring to fig. 6, which shows a schematic structural diagram of the human respiratory waveform determining device provided by the embodiment of the application, may include:
a memory 61 for storing a computer program;
the processor 62, when executing the computer program stored in the memory 61, may implement the following steps:
transmitting the frequency-modulated electromagnetic wave to the human body to be detected, receiving the frequency-modulated electromagnetic wave reflected by the human body to be detected, and obtaining an intermediate frequency signal by mixing the received and transmitted frequency-modulated electromagnetic wave; extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT (fast Fourier transform) calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body; extracting a phase from the signal and processing the phase to obtain an original respiration waveform; and carrying out signal matching on the original breathing waveform to determine a waveform matched with human breathing, and combining the waveforms to obtain a breathing waveform of the human body to be detected.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps can be realized:
transmitting the frequency-modulated electromagnetic wave to the human body to be detected, receiving the frequency-modulated electromagnetic wave reflected by the human body to be detected, and obtaining an intermediate frequency signal by mixing the received and transmitted frequency-modulated electromagnetic wave; extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT (fast Fourier transform) calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body; extracting a phase from the signal and processing the phase to obtain an original respiration waveform; and carrying out signal matching on the original breathing waveform to determine a waveform matched with human breathing, and combining the waveforms to obtain a breathing waveform of the human body to be detected.
The description of the relevant parts in the apparatus, the device and the computer readable storage medium for determining a human respiratory waveform provided in the embodiments of the present application may refer to the detailed description of the corresponding parts in the method for determining a human respiratory waveform provided in the embodiments of the present application, which is not repeated here.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements is inherent to. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. In addition, the parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of the corresponding technical solutions in the prior art, are not described in detail, so that redundant descriptions are avoided.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A method of determining a respiratory waveform of a human body, comprising:
transmitting frequency-modulated electromagnetic waves to a human body to be detected, receiving the frequency-modulated electromagnetic waves reflected by the human body to be detected, and obtaining intermediate frequency signals by mixing the received and transmitted frequency-modulated electromagnetic waves;
extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, performing FFT (fast Fourier transform) calculation on each sampling point, and performing band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body;
extracting a phase from the signal and processing the phase to obtain an original respiration waveform;
performing signal matching on the original respiratory waveform to determine a waveform matched with human respiration, and combining the waveforms to obtain a respiratory waveform of the human to be detected;
before the waveforms are combined to obtain the respiratory waveform of the human body to be tested, the method further comprises the following steps:
calculating waveform similarity among the waveforms, and classifying the waveforms with continuous FFT result indexes and waveform similarity larger than a preset value into a group;
correspondingly, the waveforms are combined to obtain the breathing waveform of the human body to be detected, which comprises the following steps:
and combining the waveforms in each group to obtain the breathing waveforms of the human body to be tested.
2. The method of claim 1, further comprising, after processing the phase to obtain an original respiratory waveform:
the raw respiratory waveform is high pass filtered to filter out baseline values.
3. The method of claim 2, wherein high pass filtering the raw respiratory waveform comprises:
the raw respiratory waveform is input into a digital high-pass filter to high-pass filter the raw respiratory waveform with the digital high-pass filter.
4. The method of claim 1, wherein processing the phase to obtain an original respiratory waveform comprises:
and unwrapping the phase to obtain an original respiration waveform.
5. The method of determining a respiration waveform of a person according to claim 1, wherein band-pass filtering each FFT result comprises:
and inputting the FFT result into a corresponding digital band-pass filter so as to carry out band-pass filtering on the FFT result by utilizing the digital band-pass filter.
6. The method according to any one of claims 1 to 5, further comprising, after combining the waveforms to obtain the respiratory waveform of the human body to be measured:
and obtaining the respiratory frequency of the human body to be tested according to the respiratory waveform.
7. A human respiratory waveform determining apparatus, comprising:
the intermediate frequency signal obtaining module is used for transmitting frequency-modulated electromagnetic waves to a human body to be detected, receiving the frequency-modulated electromagnetic waves reflected by the human body to be detected and obtaining intermediate frequency signals by mixing the received and transmitted frequency-modulated electromagnetic waves;
the calculation module is used for extracting a preset number of sampling points from the intermediate frequency signal at preset time intervals, carrying out FFT (fast Fourier transform) calculation on each sampling point, and carrying out band-pass filtering on each FFT result to obtain a signal conforming to the respiratory frequency of a human body;
the processing module is used for extracting a phase from the signal and processing the phase to obtain an original respiratory waveform;
the signal matching module is used for carrying out signal matching on the original breathing waveform to determine a waveform matched with the breathing of the human body, and combining the waveforms to obtain a breathing waveform of the human body to be detected;
further comprises:
the grouping module is used for calculating waveform similarity among the waveforms before the waveforms are combined to obtain the respiratory waveforms of the human body to be detected, and grouping the waveforms with continuous FFT result indexes and waveform similarity larger than a preset value into a group;
accordingly, the signal matching module includes:
and the merging unit is used for merging the waveforms in each group respectively to obtain the breathing waveforms of the human body to be detected.
8. A human respiratory waveform determining apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the human breathing waveform determining method according to any one of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when executed by a processor, implements the steps of the human breathing waveform determining method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010704894.8A CN111766442B (en) | 2020-07-21 | 2020-07-21 | Method, device and equipment for determining human respiratory waveform and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010704894.8A CN111766442B (en) | 2020-07-21 | 2020-07-21 | Method, device and equipment for determining human respiratory waveform and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111766442A CN111766442A (en) | 2020-10-13 |
| CN111766442B true CN111766442B (en) | 2023-07-18 |
Family
ID=72726822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010704894.8A Active CN111766442B (en) | 2020-07-21 | 2020-07-21 | Method, device and equipment for determining human respiratory waveform and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111766442B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012059167A (en) * | 2010-09-13 | 2012-03-22 | Mitsubishi Chemicals Corp | Information processing method, information processing system, information processor, display device, information processing program and recording medium capable of reading computer which records the program |
| WO2017200355A1 (en) * | 2016-05-20 | 2017-11-23 | 다담마이크로 주식회사 | Noncontact respiratory measurement device, monitoring system using same, and feedback system using same |
| JP2019013479A (en) * | 2017-07-07 | 2019-01-31 | 国立大学法人電気通信大学 | Heart beat and respiration measuring system, and heart beat and respiration measuring method |
| WO2020004721A1 (en) * | 2018-06-27 | 2020-01-02 | 유메인주식회사 | Method for measuring vital information by using ultra-wideband impulse radar signal |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102749615B (en) * | 2012-07-11 | 2015-08-05 | 天津理工大学 | A kind of method of Signal analysis |
| CN105161093B (en) * | 2015-10-14 | 2019-07-09 | 科大讯飞股份有限公司 | A kind of method and system judging speaker's number |
| CN105476602B (en) * | 2015-11-25 | 2018-11-06 | 方姝阳 | Contactless humanbody life sign measurement method and device |
| CN106405542A (en) * | 2016-10-10 | 2017-02-15 | 康佳集团股份有限公司 | Life sign detection processing method and system based on continuous waves, and life detector |
| CN106772382A (en) * | 2016-12-16 | 2017-05-31 | 航天恒星科技有限公司 | Multi-targets recognition matching method and system and Anticollision Radar |
| CN106821347B (en) * | 2016-12-20 | 2020-05-05 | 中国人民解放军第三军医大学 | FMCW broadband life detection radar respiration and heartbeat signal extraction algorithm |
| WO2018220701A1 (en) * | 2017-05-30 | 2018-12-06 | 株式会社ソシオネクスト | Information processing device and detection device |
| CN108784704A (en) * | 2018-07-05 | 2018-11-13 | 广州和普乐健康科技有限公司 | A kind of breathing obstruction detection method |
| CN110464320A (en) * | 2019-08-22 | 2019-11-19 | 四川长虹电器股份有限公司 | Multiple target human heart rate and respiratory rate measuring system and method |
-
2020
- 2020-07-21 CN CN202010704894.8A patent/CN111766442B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012059167A (en) * | 2010-09-13 | 2012-03-22 | Mitsubishi Chemicals Corp | Information processing method, information processing system, information processor, display device, information processing program and recording medium capable of reading computer which records the program |
| WO2017200355A1 (en) * | 2016-05-20 | 2017-11-23 | 다담마이크로 주식회사 | Noncontact respiratory measurement device, monitoring system using same, and feedback system using same |
| JP2019013479A (en) * | 2017-07-07 | 2019-01-31 | 国立大学法人電気通信大学 | Heart beat and respiration measuring system, and heart beat and respiration measuring method |
| WO2020004721A1 (en) * | 2018-06-27 | 2020-01-02 | 유메인주식회사 | Method for measuring vital information by using ultra-wideband impulse radar signal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111766442A (en) | 2020-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108416367B (en) | Sleep staging method based on multi-sensor data decision-level fusion | |
| CN112401856B (en) | Nursing home monitoring method and system based on millimeter wave radar | |
| CN112998701A (en) | Vital sign detection and identity recognition system and method based on millimeter wave radar | |
| US20230043406A1 (en) | Noninvasive method and system for sleep apnea detection | |
| CN105249925B (en) | A kind of traditional Chinese medical pulse manifestation collecting device and noise reduction system and noise-reduction method | |
| CN101991418A (en) | Method for improving respiratory rate detection accuracy | |
| CN109700450A (en) | A kind of heart rate detection method and electronic equipment | |
| WO2022116467A1 (en) | Millimeter-wave radar-based non-contact identity recognition method and system | |
| WO2019080728A1 (en) | Method for detecting heart rate abnormality on the basis of wi-fi signal | |
| CN111142102A (en) | Respiratory data calculation method and related equipment | |
| CN114897011A (en) | Signal separation and denoising method and system for Doppler radar physiological signal detection | |
| CN112998690A (en) | Pulse wave multi-feature fusion-based respiration rate extraction method | |
| KR20210001575A (en) | Real-time cardiac rate detection apparatus in noisy environment and method thereof | |
| CN109009087A (en) | A kind of rapid detection method of R wave of electrocardiosignal | |
| CN114638272A (en) | Identity recognition method and device based on fingertip pulse wave signals | |
| CN106419884B (en) | A kind of rate calculation method and system based on wavelet analysis | |
| CN111766442B (en) | Method, device and equipment for determining human respiratory waveform and readable storage medium | |
| Haibing et al. | A method of continuous wavelet transform for qrs wave detection in ecg signal | |
| Quanyu | Pulse signal analysis based on deep learning network | |
| Fedotov | A robust method for detecting the QRS complex of the ECG signal | |
| Shcherbakova et al. | Determination of characteristic points of electrocardiograms using multi-start optimization with a wavelet transform | |
| CN110269642A (en) | Doppler's heart rate estimation method based on Fourier Transform of Fractional Order and wavelet transformation | |
| CN114947850A (en) | Mental load grade objective detection method based on pulse Bouss model characteristics | |
| Long et al. | A novel automatic pulse segmentation approach and its application in PD-induced electromagnetic wave detection | |
| Fedotov et al. | A QRS-complex detector of the electrocardiogram signal for the long-term monitoring of the patient’s condition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |