CN113331819A - Pulmonary function monitoring method, system, equipment and storage medium - Google Patents
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Abstract
The invention provides a pulmonary function monitoring method, a system, equipment and a storage medium, which are used for simultaneously monitoring pulmonary functions of a plurality of users and comprise the following steps: acquiring a plurality of data streams corresponding to lung functions of a plurality of users through a plurality of antennas; extracting CSI data according to the plurality of data streams, and processing the CSI data to obtain a plurality of respiratory signals and an expiration phase of each respiratory signal; according to the breathing signals and the preset azimuth information of each user, the breathing signals and the users are in one-to-one correspondence; and processing to obtain a lung function monitoring result of each user according to the expiration stage of the respiratory signal. According to the lung function monitoring method, the system, the equipment and the storage medium, provided by the invention, the lung function indexes of a user are collected, the artificial interference is eliminated, and the early symptoms of respiratory system diseases can be found in time; the pulmonary function monitoring of a plurality of users can be carried out simultaneously, and the pulmonary function monitoring results are in one-to-one correspondence with the users; the health standards of all users can be distinguished, so that the monitoring result is more accurate.
Description
Technical Field
The invention relates to the technical field of respiratory monitoring, in particular to a pulmonary function monitoring method, a system, equipment and a storage medium.
Background
The respiratory state of a human body is an important index of the health state of the human body, and in daily life, disease diagnosis and abnormal state monitoring of the human body can be realized by monitoring the respiratory state of the human body.
However, in the actual use process, the respiration detection system based on the acceleration sensor needs to be worn for use, which is easy to cause discomfort, so that the user can not obtain a real respiration state due to disordered respiration; the lung function detection system based on the camera has high requirements on light and is easy to expose privacy; the lung function detection system based on acoustics is easily influenced by environmental noise, and has low precision and limited perception range; although the radio frequency-based respiration detection system has high precision, hardware equipment is expensive, and popularization and use are not facilitated; the existing lung function detection system is mostly used for single person detection, the detection efficiency is low, only one respiratory frequency value can be obtained, and apnea is alarmed, but most of the respiratory diseases (such as chronic obstructive pulmonary disease) are chronic, abnormality needs to be found and actively treated in the early stage, and the respiratory frequency value cannot be used for judging the health degree of the respiratory system.
In summary, the lung function detection system in the prior art has the problems that multiple persons cannot be detected simultaneously, the detection precision is low, and the like.
Disclosure of Invention
In view of the above disadvantages of the prior art, it is an object of the present invention to provide a pulmonary function monitoring method, system, device and storage medium, so as to solve the technical problems of the prior art, such as poor ventilation function of hatracks, high manufacturing cost, etc.
To achieve the above and other related objects, the present invention provides a lung function monitoring method for simultaneously monitoring lung functions of a plurality of users located in a monitoring area of a fresnel model, the lung function monitoring method comprising:
acquiring a plurality of data streams corresponding to lung functions of a plurality of users through a plurality of antennas;
extracting CSI data according to the plurality of data streams, and processing the CSI data to obtain a plurality of respiratory signals and an expiration phase of each respiratory signal;
according to the breathing signals and preset azimuth information of each user, the breathing signals and the users are in one-to-one correspondence;
and processing to obtain a lung function monitoring result of each user according to the expiration stage of the respiratory signal.
In an embodiment of the present invention, the step of extracting CSI data according to a plurality of data streams, and processing the CSI data to obtain a plurality of respiratory signals and an expiratory phase of each respiratory signal includes:
screening out a target data stream with continuous signals from the acquired data stream;
extracting CSI data of the target data stream, and preprocessing the CSI data;
carrying out independent component analysis on the preprocessed CSI data, and solving to obtain a plurality of mixing matrixes and corresponding respiratory signals containing a plurality of complex numbers;
for each respiratory signal:
filtering, and constructing a real matrix according to real parts and imaginary parts of a plurality of complex numbers;
performing principal component analysis on the real matrix;
and extracting a first principal component waveform obtained after principal component analysis, and determining the expiratory phase of the respiratory signal according to the principal component waveform.
In an embodiment of the present invention, the step of extracting CSI data of the target data stream and preprocessing the CSI data includes:
extracting CSI data of the target data stream by using a CSI tool;
extracting complex values in the amplitude sequence of the CSI data;
and subtracting a preset average amplitude value from the complex value to obtain the preprocessed CSI data.
In an embodiment of the present invention, the step of performing one-to-one correspondence between the respiratory signal and the user according to the respiratory signal and the preset orientation information of each user includes:
for each of the respiratory signals;
obtaining the position information of the respiratory signal according to the complex number of the column vector in the corresponding mixed matrix;
and taking the position information as the input of the KNN classifier, and taking the azimuth information of the user as a classification label to obtain the azimuth information of the user corresponding to the position information.
In an embodiment of the present invention, the step of processing the lung function monitoring result of each user according to the expiratory phase of the respiratory signal includes:
for each of the respiratory signals:
extracting the motion characteristics in the expiration stage, and performing adaptive calibration;
inputting the motion characteristics after the self-adaptive calibration into a neural network to obtain a lung function index;
according to the pre-stored personal information of the user, a subgroup corresponding to the user and the health index of the subgroup are obtained through matching;
and processing to obtain a lung function monitoring result according to the health index and the lung function index.
In an embodiment of the invention, the motion characteristics comprise at least chest wall motion speed and chest wall displacement.
In an embodiment of the present invention, the step of extracting the motion feature in the expiratory phase and performing adaptive calibration includes:
extracting the motion characteristics from the expiration phase of the current respiration signal;
adaptively calibrating chest wall displacement in the motion characteristics using the following formula:
wherein: d (t) represents the distance between the user's chest wall and the antenna at time t; d' (t) represents the displacement of the chest wall after adaptive calibration at the time t; dendExpressed as the distance of the user's chest wall from the antenna at the end of the current expiratory phase; dstartRepresenting the distance between the chest wall of the user and the antenna at the starting moment of the current expiration phase; t is tstartExpressed as the starting moment of the current expiratory phase; t is tendExpressed as the end of the current expiratory phase; and T is the total duration of the current respiratory signal.
The invention also discloses a pulmonary function monitoring system, which adopts the pulmonary function monitoring method and comprises the following steps:
a data stream acquisition module for acquiring a plurality of data streams corresponding to lung functions of a plurality of users through a plurality of antennas;
the respiratory signal processing module is used for extracting and obtaining CSI data according to the plurality of data streams and processing the CSI data to obtain a plurality of respiratory signals and an expiration phase of each respiratory signal;
the user corresponding module is used for carrying out one-to-one correspondence on the breathing signals and the users according to the breathing signals and preset azimuth information of each user;
and the lung function monitoring result processing module is used for processing the breathing signal according to the expiration stage to obtain the lung function monitoring result of each user.
The invention also discloses a lung function monitoring device, which comprises a processor, wherein the processor is coupled with a memory, the memory stores program instructions, and the program instructions stored in the memory are executed by the processor to realize the lung function monitoring method.
The present invention also discloses a computer-readable storage medium containing a program which, when run on a computer, causes the computer to execute the above lung function monitoring method.
In summary, the lung function monitoring method, system, device and storage medium provided by the present invention not only use the data of chest wall motion, but also obtain the lung function index of the user through the neural network model, which not only eliminates the possible human interference, but also can find the early symptoms of respiratory system diseases in time, thereby performing early warning; the pulmonary function monitoring of a plurality of users can be carried out simultaneously, and the obtained pulmonary function monitoring results can be in one-to-one correspondence with the users; according to the pre-collected personal information of the users, the health standards of the users are distinguished, so that the monitoring results are more realistic.
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 description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 shows a system flow diagram of a lung function monitoring method according to the present invention.
Fig. 2 is a schematic structural diagram of the lung function monitoring system according to the present invention.
Fig. 3 is a schematic structural diagram of the lung function monitoring device according to the present invention.
Description of the element reference numerals
100. A pulmonary function monitoring system; 110. a data stream acquisition module; 120. a respiratory signal processing module; 130. a user corresponding module; 140. a lung function monitoring result processing module; 200. a pulmonary function monitoring device; 210. a processor; 220. a memory.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. It is also to be understood that the terminology used in the examples is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention. Test methods in which specific conditions are not specified in the following examples are generally carried out under conventional conditions or under conditions recommended by the respective manufacturers.
Please refer to fig. 1 to 3. It should be understood that the structures, ratios, sizes, and the like shown in the drawings are only used for matching the disclosure of the present disclosure, and are not used for limiting the conditions of the present disclosure, so that the present disclosure is not limited to the technical essence, and any modifications of the structures, changes of the ratios, or adjustments of the sizes, can still fall within the scope of the present disclosure without affecting the function and the achievable purpose of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
When numerical ranges are given in the examples, it is understood that both endpoints of each of the numerical ranges and any value therebetween can be selected unless the invention otherwise indicated. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs and the description of the present invention, and any methods, apparatuses, and materials similar or equivalent to those described in the examples of the present invention may be used to practice the present invention.
The lung function is an important index reflecting the human health, and for some patients with the hidden danger of lung function diseases, the respiratory state of the patient is continuously monitored in the sleeping process, so that the threat of sudden lung function diseases to the life of the patient can be effectively reduced; however, for some chronic respiratory diseases, such as chronic obstructive pulmonary disease, effective prevention cannot be achieved by monitoring the respiratory state alone.
The Fresnel zone is defined as a difference in the path length between a straight path and a broken path of an electric wave between a plurality of antennas
An ellipsoid having the antenna position as a focal point and the linear path as an axis, formed by the break point (reflection point) of (a). The region where n is 1 is the main for the signalThe contributing region is called a first fresnel region, also called an effective region, in the embodiment, the fresnel region is used to represent the monitoring region of the user, and the antenna uses the information received from the fresnel region as effective respiration information of the user, so as to perform analysis according to the effective respiration information to monitor the lung function status of a plurality of users.
The fresnel region model in this embodiment is constructed using the following formula:
wherein, P1Indicating the location of the antenna receiving the signal; p2Indicating the location of the antenna transmitting the signal; qnIndicating the boundary of the nth fresnel zone and lambda indicates the given wavelength.
Referring to fig. 1, the present embodiment provides a lung function monitoring method for simultaneously monitoring lung functions of a plurality of users located in a monitoring area of a fresnel model, the lung function monitoring method includes:
step S100, a plurality of data streams corresponding to lung functions of a plurality of users are collected through a plurality of antennas;
based on the Fresnel area model, the number of the antennae which are not less than the total number of the users is preset in the monitoring area of the users, preferably, the antennae based on wifi can continuously receive and transmit signals in a preset time period, and each antenna receives a data stream to reflect the respiratory information of a plurality of users received from different angles.
Step S200, CSI data are extracted and obtained according to a plurality of data streams and a plurality of respiratory signals and the expiration phase of each respiratory signal are obtained through processing;
the Channel State Information (CSI) is a Channel property of the communication link, and data streams received by the antennas all include CSI data.
Specifically, the method comprises the following steps:
screening out a target data stream with continuous signals from the acquired data stream, extracting CSI data of the target data stream, and preprocessing the CSI data; carrying out independent component analysis on the preprocessed CSI data, and solving to obtain a plurality of mixing matrixes and corresponding respiratory signals containing a plurality of complex numbers; for each respiratory signal: filtering, and constructing a real matrix according to real parts and imaginary parts of a plurality of complex numbers; performing principal component analysis on the real matrix; and extracting a first principal component waveform obtained after principal component analysis, and determining the expiratory phase of the respiratory signal according to the principal component waveform.
Selecting one data stream with continuous, most stable and best signals from all data streams acquired by a plurality of antennas as a target data stream;
on the premise that no user exists in the monitoring area, all antennas respectively collect reference data streams of static environment background noise with fixed time length, amplitude sequences of a plurality of reference CSI data are linearly combined according to the reference CSI data contained in the reference data streams to obtain a reference amplitude sequence, and the average value of the reference amplitude sequence in the fixed time length is calculated to obtain an average amplitude.
Further, the CSI data of the target data stream extracted from the wifi physical layer includes 30 subcarriers, the center frequencies of the subcarriers are different, which causes different sizes of fresnel diffraction models of different subcarriers, and all subcarriers are considered comprehensively, from which a relatively sensitive subcarrier can be selected to improve the lung function detection capability in this embodiment; preferably, the subcarrier with the largest variance is used.
Further, extracting a complex value in the amplitude sequence of the CSI data of the target data stream, and subtracting the average amplitude from the complex value to obtain the preprocessed CSI data; complex values in the amplitude sequence are extracted to obtain a data stream with time-varying phase offset eliminated, and CSI data with the time-varying phase offset and static background signals eliminated can be obtained by subtracting the average amplitude from the extracted complex values; in a preferred embodiment, the average amplitude value may be updated according to a predetermined frequency.
In signal processing, independent component analysis is a computational method for separating a multivariate signal into additive subcomponents; preferably, in this embodiment, a RobustICA algorithm is used, m pairs of complex numbers are extracted from the amplitude sequence of the preprocessed CSI data, and are used as inputs of independent component analysis, an independent component analysis method is executed, a mixing matrix is obtained by solving, and m pairs of complex numbers input are linearly changed, so as to obtain m respiratory signals.
Principal Component Analysis (PCA), a statistical method that transforms a set of variables that may have a correlation into a set of linearly uncorrelated variables by orthogonal transformation, the set of transformed variables being called Principal components; in this embodiment, the filtering processing is performed on the respiratory signal by the Hampel filter method to filter an abnormal value (i.e. a noise point) which is significantly different from the respiratory signal, so that the respiratory signal after the noise is removed is smoother; then, constructing a real matrix according to the real parts and the imaginary parts of the plurality of complex numbers of the respiratory signals after the noise is removed; and taking the real matrix as the input of the principal component analysis, executing a principal component analysis method, outputting a waveband image, and intercepting a first principal component waveform of the waveband image for subsequent processing.
Determining the expiratory phase of the user according to the intercepted first principal component waveform, and preferably determining the starting time and the ending time of the expiratory phase according to the following three criteria: the starting time and the ending time are in pair correspondence, and the starting time is positioned before the ending time; the average chest wall displacement during the expiratory phase should be higher than 90% of the maximum chest wall displacement during the expiratory phase; if there are multiple pairs of start and end times, the pair with the greatest chest wall displacement is selected to determine the unique expiratory phase.
Step S300, according to the breathing signals and the preset azimuth information of each user, the breathing signals and the users are in one-to-one correspondence;
for each respiratory signal, according to the complex number of the column vector in the corresponding mixed matrix, the position information of the respiratory signal can be obtained through processing; preferably, for each antenna, the azimuth information of the user may be an angular direction, a distance, and the like of the user relative to the antenna; and a KNN classifier is adopted, the position information of the breathing signals is used as input, the azimuth information of the user is used as a classification label, and the corresponding result of each position information and the azimuth information of the user is output, so that the breathing signals corresponding to the position information and the users corresponding to the azimuth information are in one-to-one correspondence.
And S400, processing according to the expiration phase of the respiratory signal to obtain the lung function monitoring result of each user.
Specifically, the method comprises the following steps:
for each respiratory signal: extracting the motion characteristics in the expiration stage, and performing adaptive calibration; inputting the motion characteristics after the self-adaptive calibration into a neural network to obtain a lung function index; according to the pre-stored personal information of the user, a subgroup corresponding to the user and the health index of the subgroup are obtained through matching; and processing according to the health index and the lung function index to obtain a lung function monitoring result.
Specifically, the motion characteristics include at least chest wall motion velocity and chest wall displacement.
And in the first principal component waveform, calculating the motion characteristic, and performing adaptive calibration on the chest wall displacement in the motion characteristic by adopting the following formula:
wherein: d (t) represents the distance between the chest wall of the user and the antenna at the time t; d' (t) represents the displacement of the chest wall after adaptive calibration at the time t; dendExpressed as the distance between the user's chest wall and the antenna at the end of the current expiratory phase; dstartExpressed as the distance between the chest wall of the user and the antenna at the starting moment of the current expiratory phase; t is tstartExpressed as the starting moment of the current expiratory phase; t is tendExpressed as the end of the current expiratory phase; and T is the total duration of the current respiratory signal.
And calculating the maximum chest wall movement speed in the expiration phase, the first second chest wall movement from the starting moment of the expiration phase and the maximum chest wall movement according to the self-adaptive chest wall movement and the chest wall movement speed.
Preferably, in this embodiment, a bayesian neural network regression model may be established by using lung function data acquired in daily physical examination, in the bayesian neural network regression model, three hidden layers are used, the number of neurons in each layer decreases as the network deepens, and the network precision may be balanced by continuously adjusting the number of neurons; preferably, the number of neurons in the three hidden layers is 12, 10 or 8.
Further, inputting the obtained maximum chest wall motion speed, the first second chest wall displacement from the initial moment of the expiration stage and the maximum chest wall displacement into a Bayes neural network regression model to obtain a lung function index of the user; preferably, the output results include four lung function indicators: peak Expiratory Flow (PEF), Forced expiratory volume in one second (FEV 1), Forced Vital Capacity (FVC), and FEV 1/FVC; PEF is the lowest airflow rate during exhalation, and average PEF for normal, healthy men and women is about 10L/S and 8L/S, respectively, while PEF for asthmatic patients is as low as 5L/S; FEV1 is the volume of air exhaled the first second of expiration, and is used to represent the resistance of the airway to breathing, and in general, the average FEV1 for healthy people is about 3.75L/S, and the FEV1 for chronic obstructive pulmonary disease is as low as 2L/S; FVC is the total volume of air exhaled, generally, a drop in FVC means a worsening of the condition, with average FVC for healthy men and women being approximately 5.25L/S and 3.75L/S, while FVC for chronic obstructive pulmonary disease will be as low as 2.5L/S; FEV1/FVC is used as a reference ratio, and in general, the ratio of healthy population should be more than 80%, while asthma patients will be as low as 50% -60%.
Specifically, PEF corresponds to the maximum chest wall motion velocity during the expiratory phase, FEV1 corresponds to the first second of chest wall displacement from the start of the expiratory phase, and FVC corresponds to the maximum chest wall displacement.
In order to further guarantee the accuracy of lung function monitoring, different health standards are referred according to the age, sex, race and the like of a user, so that different subgroups are set, each subgroup is classified according to the age, sex, race and the like, and corresponding health indexes are distributed in advance; in this embodiment, the user is matched with the information of a plurality of subgroups according to personal information pre-stored by the user, and after the matching is successful, the health index corresponding to the matched subgroup is retrieved to determine the current lung function status of the user.
Preferably, the health indicators of each subgroup may be health thresholds for PEF, FEV1, FVC, FEV 1/FVC.
If the real-time lung function index of the user reaches the health threshold of the subgroup, the lung function monitoring result of the user is that no abnormality exists in the lung function;
and if the real-time lung function index of the user does not reach the health threshold value of the subgroup, the lung function monitoring result of the user is abnormal lung function.
In a preferred embodiment, when the lung function monitoring result of the user is abnormal lung function, different types of alarms are given according to different users with abnormal lung function, so as to prompt the user that the lung function is abnormal.
The manner of performing the alarm in different manners is not described herein, but should be included in the scope of the present disclosure.
Referring to fig. 2, the present embodiment further provides a lung function monitoring system 100, and with the above lung function monitoring method, the lung function monitoring system includes:
a data stream acquisition module 110 configured to acquire a plurality of data streams corresponding to lung functions of a plurality of users through a plurality of antennas;
the respiratory signal processing module 120 is configured to extract CSI data according to the multiple data streams, and process the CSI data to obtain multiple respiratory signals and an expiratory phase of each respiratory signal;
the user corresponding module 130 is used for corresponding the breathing signals to the users one by one according to the breathing signals and the preset azimuth information of each user;
and the lung function monitoring result processing module 140 is configured to process the expiratory phase of the respiratory signal to obtain a lung function monitoring result of each user.
Referring to fig. 3, the present embodiment further provides a lung function monitoring device 200, where the lung function monitoring device 200 includes a processor 210 and a memory 220, the processor 210 is coupled to the memory 220, the memory 220 stores program instructions, and when the program instructions stored in the memory 220 are executed by the processor 210, the lung function monitoring method is implemented. The Processor 210 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; or a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component; the Memory 220 may include a Random Access Memory (RAM), and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory. The Memory 220 may also be an internal Memory of Random Access Memory (RAM) type, and the processor 210 and the Memory 220 may be integrated into one or more independent circuits or hardware, such as: application Specific Integrated Circuit (ASIC). It should be noted that the computer program in the memory 220 can be implemented in the form of software functional units and stored in a computer readable storage medium when the computer program is sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention.
The present embodiment also proposes a computer-readable storage medium storing computer instructions for causing a computer to execute the above-mentioned lung function monitoring method. The storage medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or a propagation medium. The storage medium may also include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Optical disks may include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-RW), and DVD.
In summary, the lung function monitoring method, system, device and storage medium provided by the present invention not only use the data of chest wall motion, but also obtain the lung function index of the user through the neural network model, which not only eliminates the possible human interference, but also can find the early symptoms of respiratory system diseases in time, thereby performing early warning; the pulmonary function monitoring of a plurality of users can be carried out simultaneously, and the obtained pulmonary function monitoring results can be in one-to-one correspondence with the users; according to the pre-collected personal information of the users, the health standards of the users are distinguished, so that the monitoring results are more realistic. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A pulmonary function monitoring method for simultaneously monitoring pulmonary functions of a plurality of users located in a monitoring area of a fresnel model, the pulmonary function monitoring method comprising:
acquiring a plurality of data streams corresponding to lung functions of a plurality of users through a plurality of antennas;
extracting CSI data according to the plurality of data streams, and processing the CSI data to obtain a plurality of respiratory signals and an expiration phase of each respiratory signal;
according to the breathing signals and preset azimuth information of each user, the breathing signals and the users are in one-to-one correspondence;
and processing to obtain a lung function monitoring result of each user according to the expiration stage of the respiratory signal.
2. The method of claim 1, wherein the step of extracting CSI data from the plurality of data streams and processing the plurality of respiratory signals and the expiratory phase of each respiratory signal comprises:
screening out a target data stream with continuous signals from the acquired data stream;
extracting CSI data of the target data stream, and preprocessing the CSI data;
performing independent component analysis on the preprocessed CSI data, and solving to obtain a mixing matrix and a plurality of respiratory signals respectively comprising a plurality of complex numbers;
for each respiratory signal:
filtering, and constructing a real matrix according to real parts and imaginary parts of a plurality of complex numbers;
performing principal component analysis on the real matrix;
and extracting a first principal component waveform obtained after principal component analysis, and determining the expiratory phase of the respiratory signal according to the principal component waveform.
3. The lung function monitoring method according to claim 2, wherein the step of extracting CSI data of the target data stream and preprocessing the CSI data comprises:
extracting CSI data of the target data stream by using a CSI tool;
extracting complex values in the amplitude sequence of the CSI data;
and subtracting a preset average amplitude value from the complex value to obtain the preprocessed CSI data.
4. The lung function monitoring method according to claim 2, wherein the step of performing one-to-one correspondence between the respiratory signal and each user according to the respiratory signal and preset orientation information of each user comprises:
for each of the respiratory signals;
obtaining the position information of the respiratory signal according to the complex number of the column vector in the corresponding mixed matrix;
and taking the position information as the input of the KNN classifier, and taking the azimuth information of the user as a classification label to obtain the azimuth information of the user corresponding to the position information.
5. The method of claim 1, wherein the step of processing the obtained lung function monitoring result for each user according to the expiratory phase of the respiratory signal comprises:
for each of the respiratory signals:
extracting the motion characteristics in the expiration stage, and performing adaptive calibration;
inputting the motion characteristics after the self-adaptive calibration into a neural network to obtain a lung function index;
according to the pre-stored personal information of the user, a subgroup corresponding to the user and the health index of the subgroup are obtained through matching;
and processing to obtain a lung function monitoring result according to the health index and the lung function index.
6. The method of claim 5, wherein the motion characteristics include at least chest wall motion velocity and chest wall displacement.
7. The method of claim 6, wherein the step of extracting the motion features during the expiratory phase and performing adaptive calibration comprises:
extracting the motion characteristics from the expiration phase of the current respiration signal;
adaptively calibrating chest wall displacement in the motion characteristics using the following formula:
wherein: d (t) represents the distance between the user's chest wall and the antenna at time t; d' (t) represents the displacement of the chest wall after adaptive calibration at the time t; dendExpressed as the distance of the user's chest wall from the antenna at the end of the current expiratory phase; dstartRepresenting the distance between the chest wall of the user and the antenna at the starting moment of the current expiration phase; t is tstartExpressed as the starting moment of the current expiratory phase; t is tendExpressed as the end of the current expiratory phase; and T is the total duration of the current respiratory signal.
8. A pulmonary function monitoring system using the pulmonary function monitoring method according to any one of claims 1 to 7, the pulmonary function monitoring system comprising:
a data stream acquisition module for acquiring a plurality of data streams corresponding to lung functions of a plurality of users through a plurality of antennas;
the respiratory signal processing module is used for extracting and obtaining CSI data according to the plurality of data streams and processing the CSI data to obtain a plurality of respiratory signals and an expiration phase of each respiratory signal;
the user corresponding module is used for carrying out one-to-one correspondence on the breathing signals and the users according to the breathing signals and preset azimuth information of each user;
and the lung function monitoring result processing module is used for processing the breathing signal according to the expiration stage to obtain the lung function monitoring result of each user.
9. A pulmonary function monitoring device comprising a processor coupled to a memory, the memory storing program instructions that, when executed by the processor, implement the pulmonary function monitoring method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized by comprising a program which, when run on a computer, causes the computer to execute the lung function monitoring method according to any one of claims 1 to 7.
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