Millimeter-wave-based multi-degree-of-freedom lung function monitoring method, device and storage medium

Abstract

The present invention provides a multi-degree-of-freedom lung function monitoring method, device and storage medium based on millimeter waves, and the method comprises: a millimeter wave sensing terminal transmits a millimeter wave radar signal in real time, and obtains a frequency-modulated continuous wave signal of at least one channel; the frequency-modulated continuous wave signal is processed to obtain a chest and abdomen respiratory motion signal of at least one channel; the chest and abdomen respiratory motion signal is processed to obtain respiratory data and phase data; the respiratory data and phase data are sent to a computing terminal in real time, and the computing terminal generates a single-channel respiratory waveform in real time based on the respiratory data, and performs real-time period positioning on the respiratory waveform, and obtains lung function parameters by real-time calculation; a pre-trained chest and abdomen asynchrony model is obtained, and the phase data is input into the chest and abdomen asynchrony model to obtain chest and abdomen asynchrony parameters; the problem that it is difficult to deploy lung function monitoring equipment in a home environment, which leads to difficulties in lung function monitoring, can be solved; and multi-degree-of-freedom lung function monitoring can be realized at the same time, and the accuracy of lung function assessment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human cardiopulmonary function monitoring, and in particular to a millimeter wave-based multi-degree-of-freedom lung function monitoring method, device and storage medium. Background Art

[0002] Among lung diseases, chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Therefore, daily lung function monitoring is of great significance for patients and potential patients with this type of lung disease.

[0003] Traditional lung function monitoring methods include: lung function monitoring based on gas flow, mechanical movement or optical principles.

[0004] However, traditional lung function monitoring methods require expensive and bulky instruments, and have relatively demanding requirements on the deployment environment of the instruments. At the same time, during the monitoring process, the subjects need to wear corresponding measuring devices. For example, monitoring methods based on gas flow require wearing a breathing mask, and monitoring methods based on mechanical movement require wearing sensors close to the body. It is difficult to deploy lung function monitoring equipment in environments such as home, which leads to difficulties in lung function monitoring. Summary of the invention

[0005] In view of this, the embodiments of the present invention provide a multi-degree-of-freedom lung function monitoring method, device and storage medium based on millimeter waves to eliminate or improve one or more defects in the prior art. It can solve the problem that it is difficult to deploy lung function monitoring equipment in environments such as home, which makes lung function monitoring difficult.

[0006] One aspect of the present invention provides a multi-degree-of-freedom lung function monitoring method based on millimeter waves, which is applied to a millimeter wave sensing terminal. The method comprises the following steps:

[0007] In response to the radar setting sent by the computing terminal, a millimeter wave radar signal is transmitted in real time, and a frequency modulated continuous wave signal of at least one channel is acquired in real time; the frequency modulated continuous wave signal is obtained by reflecting the millimeter wave radar signal from the chest and abdomen of the subject;

[0008] Processing the frequency modulated continuous wave signal to obtain a chest and abdomen respiratory motion signal of at least one channel;

[0009] Perform multi-channel fusion on chest and abdomen respiratory motion signals, and perform phase calculation and phase unwrapping to obtain respiratory data;

[0010] performing phase calculation and phase unwrapping on chest and abdomen respiratory motion signals in sequence to obtain phase data of at least one channel;

[0011] The respiratory data and phase data are sent to the computing terminal in real time, so that the computing terminal can calculate the lung function parameters based on the respiratory data and the chest and abdomen asynchrony parameters based on the phase data.

[0012] Optionally, the frequency modulated continuous wave signal is processed to obtain a chest and abdomen respiratory motion signal of at least one channel, including:

[0013] Filtering static information from a frequency modulated continuous wave signal by time iteration;

[0014] Performing distance and fast Fourier transform on the FMCW signal after filtering static information to generate a distance profile;

[0015] Determine a target distance profile in the distance profile; the target distance profile refers to a distance profile including chest and abdomen respiratory motion signals;

[0016] determining a target receiver corresponding to the target contour and generating a hot spot area for the target receiver;

[0017] The target receiver performs beamforming processing on the hot spot area to obtain a range angle map;

[0018] Perform time averaging processing on the distance angle map to obtain a distance angle feature map;

[0019] The effective breathing index is used to locate the center point of the chest and abdomen respiratory motion in combination with the distance angle map and the distance angle characteristic map; the effective breathing index is used to indicate the similarity between the chest and abdomen respiratory motion signal and the preset breathing pattern;

[0020] Through the effective breathing index, the exact position of the center point of chest and abdominal breathing movement is determined;

[0021] Based on the central point of the chest and abdomen respiratory movement, the preset range is expanded outward, and the signal within the range is retained to obtain the chest and abdomen respiratory movement signal.

[0022] Another aspect of the present invention provides a multi-degree-of-freedom lung function monitoring method based on millimeter waves, which is applied to a computing terminal. The method comprises the following steps:

[0023] Respiratory data and phase data are received in real time; respiratory data refers to the chest and abdominal respiratory motion signals obtained by the millimeter wave sensing terminal after processing the frequency modulated continuous wave signal to obtain the chest and abdominal respiratory motion signals, performing multi-channel fusion, and performing phase calculation and phase expansion on the chest and abdominal respiratory motion signals; phase data refers to the chest and abdominal respiratory motion signals obtained by the millimeter wave sensing terminal performing phase calculation and phase expansion in sequence;

[0024] Generate a single-channel respiratory waveform in real time based on respiratory data;

[0025] Perform real-time period positioning of the respiratory waveform and calculate lung function parameters in real time;

[0026] Obtain a pre-trained thoracoabdominal asynchrony model, where the thoracoabdominal asynchrony model is used to extract differential parameters of the thoracic cavity and the abdominal cavity;

[0027] The phase data is input into the thoracoabdominal asynchrony model to obtain the thoracoabdominal asynchrony parameters.

[0028] Optionally, the respiratory waveform is periodically positioned in real time, and lung function parameters are calculated in real time, including:

[0029] For the respiratory waveform, the peaks and troughs of the respiratory waveform are identified in real time through the second-order difference algorithm, and the respiratory cycle is obtained by division;

[0030] The respiratory waveform data of each respiratory cycle is divided into an exhalation segment and an inhalation segment, and the lung function parameters are calculated based on the exhalation segment and the inhalation segment.

[0031] Optionally, the pulmonary function parameters include: respiratory rate, inspiratory time, expiratory time, respiratory cycle time, inspiratory expiratory time ratio, inspiratory cycle time ratio, tidal breathing inspiratory peak flow, tidal breathing expiratory peak flow, fastest inspiratory expiratory rate ratio, inspiratory peak time, expiratory peak time, inspiratory peak time ratio, inspiratory peak time ratio, expiratory peak time ratio, mid-inspiratory flow, mid-expiratory flow, or the ratio of mid-expiratory flow to mid-inspiratory flow.

[0032] Optionally, for the respiratory waveform, the peaks and troughs of the respiratory waveform are identified in real time by a second-order difference algorithm, and after the respiratory cycle is obtained by dividing, the method further includes:

[0033] Derivative the respiratory waveform data of each cycle to obtain the corresponding cycle flow data;

[0034] The respiratory waveform data and the periodic flow data are combined to draw a respiratory loop image, and the respiratory loop image and the respiratory waveform are displayed on a display interface.

[0035] Optionally, before receiving the respiratory data and phase data in real time, the method further includes:

[0036] Create a data processing thread and an interface thread; the data processing thread is used to obtain lung function parameters and chest and abdominal asynchrony parameters; the interface thread is used to draw respiratory waveforms and respiratory ring images in real time, and display the respiratory waveforms and respiratory ring images in real time in the display interface.

[0037] Optionally, after inputting the phase data into the thoracoabdominal asynchrony model to obtain the thoracoabdominal asynchrony parameters, the method further includes:

[0038] Obtaining pre-built relationship models between parameters and health status;

[0039] Based on the chest-abdomen asynchrony parameters, lung function parameters and relationship models, prompt information is generated and displayed; the prompt information is used to indicate abnormal parameters or predict disease information.

[0040] Another aspect of the present invention provides an electronic device, including a memory, a controller, and a computer program stored in the memory and executable on the controller. When the controller executes the computer program, the steps of the above-mentioned millimeter-wave-based multi-degree-of-freedom lung function monitoring method are implemented.

[0041] Another aspect of the present invention provides a computer-readable storage medium, in which a program is stored. When the program is executed by a processor, it is used to implement the steps of the above-mentioned millimeter-wave-based multi-degree-of-freedom lung function monitoring method.

[0042] The millimeter-wave-based multi-degree-of-freedom lung function monitoring method, device and storage medium of the present invention can observe the changes of various lung function parameters in real time through the signal processing flow corresponding to the millimeter-wave sensing terminal and the computing terminal and the corresponding visualization program, which can solve the problem of difficulty in lung function monitoring; there is no need to deploy expensive and large measuring instruments, and it can be deployed in multiple scenarios such as hospitals / homes to achieve long-term real-time monitoring of lung function in multiple scenarios; at the same time, the computing terminal analyzes the respiratory waveform and calculates lung function parameters, including respiratory frequency, inhalation time, exhalation time, respiratory cycle time, inhalation-exhalation time ratio, inhalation cycle time ratio, tidal breathing inspiratory peak flow and other parameters, and inputs the phase data of at least one channel into the chest-abdominal asynchrony model, and the chest-abdominal asynchrony model outputs the chest-abdominal asynchrony parameters to achieve multi-degree-of-freedom lung function monitoring, which can improve the accuracy of evaluating lung health status and respiratory function abnormalities.

[0043] In addition, by setting the millimeter wave sensing terminal at a preset distance from the subject, the subject does not need to wear a breathing mask or other facilities close to the body, which can improve the comfort of the subject. Especially in the case of long-term monitoring, it can reduce the discomfort and stress of the subject, reduce the complexity of operation, and improve the convenience of operation.

[0044] In addition, through the pre-constructed relationship model between parameters and health status, real-time abnormal lung function parameter detection can be achieved based on the obtained lung function parameters and chest and abdominal asynchrony parameters, and corresponding feedback and possible lung diseases can be given in time when abnormal lung function parameters occur.

[0045] In addition, through the multi-threaded processing method, the data processing thread is used to obtain lung function parameters and chest and abdominal asynchrony parameters; the interface thread is used to draw the respiratory waveform and respiratory ring image in real time, and display the respiratory waveform and respiratory ring image in real time in the display interface, so as to realize the real-time update of the display interface, so that the user can observe the lung function status in time.

[0046] In addition, close-fitting equipment may be affected by factors such as the subject's breathing and movement, resulting in signal interference or distortion. Setting the millimeter wave sensing terminal at a preset distance can reduce such interference and improve data accuracy and reliability.

[0047] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0048] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0050] Figure 1 A schematic diagram of a millimeter wave-based multi-degree-of-freedom lung function monitoring system provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a millimeter wave-based multi-degree-of-freedom lung function monitoring system provided by an embodiment of the present invention;

[0052] Figure 3 A flowchart of a multi-degree-of-freedom lung function monitoring method based on millimeter waves provided in another embodiment of the present invention;

[0053] Figure 4 A schematic diagram of a display interface provided by another embodiment of the present invention;

[0054] Figure 5 A block diagram of a millimeter wave-based multi-degree-of-freedom lung function monitoring device provided by another embodiment of the present invention;

[0055] Figure 6 A block diagram of a millimeter wave-based multi-degree-of-freedom lung function monitoring device provided by another embodiment of the present invention;

[0056] Figure 7 A block diagram of an electronic device provided according to another embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0058] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0059] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0060] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0061] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0062] Figure 1 FIG. 1 is a schematic diagram of a multi-degree-of-freedom lung function monitoring system based on millimeter waves provided by an embodiment of the present application. Figure 1 As shown, the system includes at least: a millimeter wave sensing terminal 110 and a computing terminal 120.

[0063] The millimeter wave sensing terminal 110 is provided with a transmitting antenna (Tx) and a receiving antenna (Rx), wherein the transmitting antenna is used to transmit the millimeter wave radar signal to the chest and abdomen of the subject; the receiving antenna is used to receive the frequency modulated continuous wave signal obtained after the millimeter wave radar signal is reflected by the chest and abdomen of the subject. Optionally, the millimeter wave sensing terminal 110 can be an IWR1443 millimeter wave sensing terminal, an MPC5777C millimeter wave sensing terminal, or an ADF5904 millimeter wave sensing terminal, etc. This embodiment does not limit the implementation method of the millimeter wave sensing terminal 110.

[0064] In this embodiment, the millimeter wave sensing terminal 110 is provided with m transmitting antennas and n receiving antennas, wherein m is an integer greater than 0, and n is an integer greater than 0. For example, the millimeter wave sensing terminal 110 is a 3-transmit 4-receive millimeter wave sensing terminal, that is, the millimeter wave sensing terminal 110 is provided with 3 transmitting antennas and 4 receiving antennas.

[0065] In this embodiment, the distance between the subject and the millimeter wave sensing terminal 110 is within a preset distance, and the side of the millimeter wave sensing terminal 110 provided with the transmitting antenna and the receiving antenna faces the chest and abdomen of the subject.

[0066] Among them, the preset distance refers to the pre-set distance between the subject and the millimeter wave sensing terminal 110, which can be 1 meter, 2 meters or 3 meters, etc. The preset distance between the subject and the millimeter wave sensing terminal 110 can be adjusted according to the frequency, power, antenna design or environmental conditions of the millimeter wave sensing terminal 110. This embodiment does not limit the value of the preset distance.

[0067] In this embodiment, the millimeter wave sensing terminal 110 is set at a preset distance from the subject, and the subject does not need to wear a breathing mask or other facilities close to the body, which can improve the comfort of the subject, especially in the case of long-term monitoring, can reduce the discomfort and pressure of the subject, reduce the complexity of operation, and improve the convenience of operation. At the same time, close-fitting facilities may be affected by factors such as the subject's breathing and movement, resulting in interference or distortion of the signal. Setting the millimeter wave sensing terminal 110 at a preset distance can reduce such interference and improve the accuracy and reliability of the data.

[0068] In this embodiment, after receiving the radar settings sent by the computing terminal 120, the millimeter wave sensing terminal 110 sends millimeter wave radar signals through m transmitting antennas, and obtains frequency modulated continuous wave signals of m×n channels in real time through n receiving antennas, and obtains chest and abdominal respiratory movement signals through a series of signal processing, and then processes the chest and abdominal respiratory movement signals to obtain respiratory data and phase data of m×n channels.

[0069] Specifically, in this embodiment, the millimeter wave sensing terminal 110 is used to: respond to the radar setting sent by the computing terminal, transmit the millimeter wave radar signal in real time, and obtain the frequency modulated continuous wave signal of at least one channel in real time; process the frequency modulated continuous wave signal to obtain the chest and abdomen respiratory motion signal of at least one channel; perform multi-channel fusion on the chest and abdomen respiratory motion signal, and perform phase calculation and phase expansion to obtain respiratory data; perform phase calculation and phase expansion on the chest and abdomen respiratory motion signal in sequence to obtain phase data of at least one channel. Among them, the frequency modulated continuous wave signal is the millimeter wave radar signal reflected by the chest and abdomen of the subject.

[0070] Since the received FM continuous wave signal contains relatively strong static object reflection information, which will interfere with tiny motion information, it is necessary to perform static information removal processing on the FM continuous wave signal and filter out the static information from the underlying original intermediate frequency signal of the FM continuous wave signal through time iteration.

[0071] Then, the distance and fast Fourier transform are used to generate the distance profile of each receiver in the millimeter wave sensing terminal 110. The millimeter wave sensing terminal 110 includes n receivers, which correspond one to one to the n receiving antennas.

[0072] After that, a target range profile that can fully contain the chest and abdomen respiratory motion signals is selected in the range dimension. Next, a hot spot area is generated for the target receiver corresponding to each target range profile, and the range angle map (RM) is generated by the target receiver beamforming, and then the range angle feature map (RFM) is generated by time averaging. In the range angle map and the range angle feature map, the linear superposition items are separated.

[0073] Finally, further group denoising is performed through a two-stage extraction algorithm, including:

[0074] In the first stage, target clustering extraction is performed, and the effective respiratory index (ERI) is used, combined with the distance angle map and the distance angle feature map, to complete the positioning of the center point (RC-AB centroid) of the chest and abdomen respiratory movement in a step-by-step manner.

[0075] First, for the range angle feature map, the points whose signal strength exceeds the threshold are filtered out, where the threshold is the maximum value in the range angle feature map multiplied by the attenuation coefficient.

[0076] Then, for each region, all points in it are mapped back to the range angle feature map, and the phase information of these positions is extracted, and the average effective breathing index between them is calculated.

[0077] Finally, the target cluster with the largest average effective respiratory index was determined and regarded as the RC-AB region, that is, the region where the center point of the thoracic and abdominal respiratory motion was located.

[0078] In this embodiment, the effective breathing index is used to indicate the similarity between the chest and abdomen respiratory motion signal and the preset breathing pattern; the preset breathing pattern refers to a preset breathing pattern, including the minimum breathing rate (brmin), the maximum breathing rate (brmax) and the breathing rate error tolerance (ftolerance). Calculating the effective breathing index includes at least steps S101 to S105:

[0079] Step S101, calculating the Welch spectrum of the chest and abdomen respiratory motion signal by using the Welch Spectrum method.

[0080] Step S102, obtaining the minimum respiratory rate, the maximum respiratory rate and the respiratory rate error tolerance, and determining the normal respiratory rate range and the error range.

[0081] Step S103 , removing the DC component from the Welch spectrum, and calculating the maximum power value in the Welch spectrum: the maximum power value is the peak value in the Welch spectrum.

[0082] Step S104, determining whether the maximum power value is within the normal breathing frequency range. If the maximum power value is not within the defined normal breathing frequency range, the effective breathing index is defined as 0, indicating that the chest and abdomen respiratory motion signal does not conform to the normal breathing pattern.

[0083] Step S105 , when the maximum power value is within the normal respiratory frequency range, the power density is calculated by dividing the power near the peak value and within the respiratory rate error tolerance by the power of other information within the respiratory bandwidth to obtain an effective respiratory index.

[0084] In the second stage, the exact position of the center point of the thoracic and abdominal respiratory movement is determined by the effective respiratory index. After determining the RC-AB area, for each point inside it, they are mapped to the distance angle map, and then the effective respiratory index of each point is calculated. Finally, the point with the largest effective respiratory index is selected as the center point of the thoracic and abdominal respiratory movement.

[0085] A certain range is expanded outward from the center point of the thoracic and abdominal respiratory movement. The range is the thoracic and abdominal respiratory movement signal that needs to be extracted, and the thoracic and abdominal respiratory movement signal outside the range is the thoracic and abdominal respiratory movement signal that needs to be removed. Only the information within the range is retained, and the thoracic and abdominal respiratory movement signal is extracted. The center point of the thoracic and abdominal respiratory movement after filtering still retains m×n dimensions.

[0086] Specifically, a frequency modulated continuous wave signal is processed to obtain a chest and abdominal respiratory motion signal of at least one channel, including: filtering static information from the frequency modulated continuous wave signal by time iteration; performing distance and fast Fourier transform on the frequency modulated continuous wave signal after filtering the static information to generate a distance profile; determining a target distance profile in the distance profile; the target distance profile refers to a distance profile containing the chest and abdominal respiratory motion signal; determining a target receiver corresponding to the target profile, and generating a hot spot area for the target receiver; performing beamforming processing on the hot spot area by the target receiver to obtain a distance angle diagram; performing time averaging processing on the distance angle diagram to obtain a distance angle characteristic diagram; locating the center point of the chest and abdominal respiratory motion by an effective respiratory index combined with the distance angle diagram and the distance angle characteristic diagram; determining the exact position of the center point of the chest and abdominal respiratory motion by the effective respiratory index; expanding a preset range based on the center point of the chest and abdominal respiratory motion, retaining the signal within the range, and obtaining the chest and abdominal respiratory motion signal.

[0087] In addition, in this embodiment, the millimeter wave sensing terminal 110 calculates the correlation between the chest and abdominal respiratory motion signals of multiple channels through principal component analysis (PCA), and constructs the chest and abdominal respiratory motion signals with higher correlation into new signals of the same dimension, thereby separating the chest and abdominal respiratory motion signals from the noise signals, and realizing multi-channel fusion of the chest and abdominal respiratory motion signals.

[0088] After obtaining the respiration data and phase data, such as Figure 2 As shown, the millimeter wave sensing terminal 110 is also used to: send the respiratory data and phase data to the computing terminal 120 in real time, so that the computing terminal 120 calculates the lung function parameters based on the respiratory data and obtains the thoracoabdominal asynchrony parameters (Thoracoabdominal Asynchrony, TAA) based on the phase data.

[0089] The pulmonary function parameters include respiratory rate (RR), inspiratory time (Ti), expiratory time (Te), total cycle time (Ttot), inspiratory and expiratory time ratio (Ti / Te), duty cycle, peak tidal inspiratory flow (PTIF), peak tidal expiratory flow (PTEF), fastest inspiratory and expiratory speed ratio (PTIF / PTEF), time to peak tidal inspiratory flow (TPTIF), time to peak tidal expiratory flow (TPTEF), time to peak tidal expiratory flow (TPTIF / TPTEF), time to peak inspiratory and expiratory flow ratio (TPTIF / TPTEF), time to peak inspiratory and expiratory flow ratio (TPTIF / TI), time to peak expiratory flow ratio (TPTEF / TE), tidal inspiratory flow when 50% of tidal volume remains in the The ratio of tidal expiratory flow and tidal inspiratory flow when 50% of tidal volume remains in the lung, TEF50, is the ratio of tidal expiratory flow and tidal inspiratory flow when 50% of tidal volume remains in the lung, TEF50.

[0090] In this embodiment, a communication connection is established between the computing terminal 120 and the millimeter wave sensing terminal 110, for receiving in real time the respiratory data and phase data sent by the millimeter wave sensing terminal 110, and calculating the lung function parameters based on the respiratory data, and obtaining the chest and abdominal asynchrony parameters based on the phase data. Optionally, the computing terminal 120 can be a mobile phone, a computer, etc., and this embodiment does not limit the implementation method of the computing terminal 120.

[0091] In this embodiment, the computing terminal 120 is used to: receive respiratory data and phase data in real time; generate a single-channel respiratory waveform in real time based on the respiratory data; perform real-time period positioning of the respiratory waveform, and calculate lung function parameters in real time; obtain a pre-trained thoracoabdominal asynchrony model, which is used to extract differential parameters of the thoracic cavity and the abdominal cavity; and input the phase data into the thoracoabdominal asynchrony model to obtain thoracoabdominal asynchrony parameters.

[0092] In this embodiment, the computing terminal 120 includes a display module, which is integrated on the computing terminal 120 and is used to display the respiratory waveform. The display module can be a liquid crystal display, an organic light emitting diode, an electronic ink screen, or an LED display, etc.

[0093] In actual implementation, the display module may also be a module independent of the computing terminal 120 , and connected to the computing terminal 120 through wired or wireless communication. This embodiment does not limit the connection method between the display module and the computing terminal 120 .

[0094] In this embodiment, by placing a millimeter wave sensing terminal in front of the chest and abdomen of the subject, the changes in various lung function parameters can be observed in real time through the signal processing flow corresponding to the millimeter wave sensing terminal and the computing terminal and the corresponding visualization program, which can solve the problem of difficult lung function monitoring; there is no need to deploy expensive and bulky measuring instruments, and it can be deployed in multiple scenarios such as hospitals / homes to achieve long-term real-time monitoring of lung function in multiple scenarios; at the same time, the millimeter wave sensing terminal is set at a position far away from the preset distance of the subject, and the subject does not need to wear facilities such as a breathing mask close to the body, which can improve the comfort of the subject, especially in the case of long-term monitoring, can reduce the discomfort and stress of the subject, reduce the complexity of operation, and improve the convenience of operation.

[0095] In addition, close-fitting equipment may be affected by the subject's breathing, movement and other factors, resulting in signal interference or distortion. Setting the millimeter wave sensing terminal 110 at a preset distance can reduce such interference and improve the accuracy and reliability of the data.

[0096] The millimeter-wave-based multi-degree-of-freedom lung function monitoring method provided by this application is introduced in detail below.

[0097] like Figure 3 As shown, the embodiment of the present application provides a multi-degree-of-freedom lung function monitoring method based on millimeter waves. Figure 1 Taking the system shown in FIG. 1 as an example, the method at least includes steps S201 to S210:

[0098] Step S201: The millimeter wave sensing terminal responds to the radar settings sent by the computing terminal, transmits the millimeter wave radar signal in real time, and acquires the frequency modulated continuous wave signal of at least one channel in real time.

[0099] Step S202: The millimeter wave sensing terminal processes the frequency modulated continuous wave signal to obtain a chest and abdomen respiratory motion signal of at least one channel.

[0100] Specifically, a frequency modulated continuous wave signal is processed to obtain a chest and abdominal respiratory motion signal of at least one channel, including: filtering static information from the frequency modulated continuous wave signal by time iteration; performing distance and fast Fourier transform on the frequency modulated continuous wave signal after filtering the static information to generate a distance profile; determining a target distance profile in the distance profile; the target distance profile refers to a distance profile containing the chest and abdominal respiratory motion signal; determining a target receiver corresponding to the target profile, and generating a hot spot area for the target receiver; performing beamforming processing on the hot spot area by the target receiver to obtain a distance angle map; performing time averaging processing on the distance angle map to obtain a distance angle feature map; locating the center point of the chest and abdominal respiratory motion by ERI, combining the distance angle map and the distance angle feature map; determining the exact position of the center point of the chest and abdominal respiratory motion by ERI; expanding a preset range based on the center point of the chest and abdominal respiratory motion, retaining the signal within the range, and obtaining the chest and abdominal respiratory motion signal.

[0101] Step S203: The millimeter wave sensing terminal performs multi-channel fusion on the chest and abdomen respiratory motion signals, and performs phase calculation and phase unfolding to obtain respiratory data.

[0102] The multi-channel fusion of the chest and abdomen respiratory motion signals includes: realizing the multi-channel fusion of the chest and abdomen respiratory motion signals through principal component analysis (PCA).

[0103] Step S204: The millimeter wave sensing terminal performs phase calculation and phase unfolding on the chest and abdomen respiratory motion signals in sequence to obtain phase data of at least one channel.

[0104] Step S205: the millimeter wave sensing terminal sends the breathing data and phase data to the computing terminal in real time.

[0105] Step S206: The computing terminal receives the breathing data and phase data in real time.

[0106] Step S207: The computing terminal generates a single-channel respiratory waveform in real time based on the respiratory data.

[0107] Optionally, before generating a single-channel respiratory waveform in real time based on the respiratory data, it also includes: performing pre-movement smoothing filtering or pulse noise elimination processing on the respiratory data to further reduce noise and unnecessary fluctuations in the data, thereby improving the quality and analyzability of the signal.

[0108] Step S208: The computing terminal performs real-time period positioning on the respiratory waveform and obtains lung function parameters by real-time calculation.

[0109] For the respiratory waveform, in this embodiment, the peaks and troughs of the respiratory waveform are identified in real time through second-order difference, and the respiratory cycle is accurately divided. For the respiratory and flow data of each cycle, the exhalation segment and the inhalation segment are further divided, so as to calculate the various time, flow and ratio parameters related to the respiratory cycle, exhalation cycle and inhalation cycle, and obtain the lung function parameters.

[0110] Specifically, the respiratory waveform is positioned in real time for periodicity, and the lung function parameters are calculated in real time, including: for the respiratory waveform, the peaks and troughs of the respiratory waveform are identified in real time by a second-order difference algorithm, and the respiratory cycles are obtained by dividing; for the respiratory waveform data of each respiratory cycle, the exhalation segment and the inhalation segment are divided, and the lung function parameters are calculated based on the exhalation segment and the inhalation segment.

[0111] In actual implementation, in order to better monitor the detailed information of the subject's breathing pattern and lung function, such as breathing rate, depth, rhythm and flow rate, it is necessary to assist medical professionals in evaluating the patient's lung function status and detecting whether there are abnormal breathing patterns or lung function problems. The breathing waveform can be used to generate a breathing ring image in real time and displayed in the display interface of the computing terminal display module. For each cycle of the breathing waveform, the cycle flow data can be obtained by derivation, and the breathing and flow data can be combined to generate a breathing ring image.

[0112] Specifically, for the respiratory waveform, the peaks and troughs of the respiratory waveform are identified in real time through a second-order difference algorithm. After the respiratory cycle is divided, it also includes: differentiating the respiratory waveform data of each cycle to obtain the corresponding cycle flow data; combining the respiratory waveform data and the cycle flow data to draw a respiratory ring image, and displaying the respiratory ring image and the respiratory waveform on the display interface.

[0113] In addition, considering the real-time nature of information display, in this embodiment, multi-threading technology is used, that is, signal processing and lung function parameter calculation are completed by the data processing thread, and the work of drawing and updating the display interface (including changes in respiratory waveforms, respiratory ring images and lung function parameter values, etc.) is completed by the interface thread. Figure 4 As shown, the data of the data processing thread is fed back to the interface thread through the signal-slot mechanism, and the display interface cannot be operated in the child thread.

[0114] Specifically, before receiving the respiratory data and phase data in real time, the method further includes: creating a data processing thread and an interface thread. The data processing thread is used to obtain lung function parameters and chest-abdominal asynchrony parameters; the interface thread is used to draw a respiratory waveform and a respiratory ring image in real time, and display the respiratory waveform and the respiratory ring image in real time in the display interface.

[0115] Step S209: the computing terminal obtains a pre-trained chest-abdomen asynchrony model.

[0116] In this embodiment, the thoracoabdominal asynchrony model is used to extract the differential parameters of the thoracic cavity and the abdominal cavity. It is a thoracoabdominal asynchrony network (TAANet) designed based on the traditional convolutional neural network (CNN) structure, and is mainly composed of a spatial feature extraction block and a temporal dimensionality reduction block. The spatial domain of the original input is preliminarily activated by the feature activation block, and then several spatial feature extraction blocks are used to extract deep features. Each spatial feature extraction block is divided into 4 stages. Each stage begins with a temporal dimensionality reduction block, and then a conventional fully connected layer is used to generate thoracoabdominal asynchrony parameters.

[0117] Specifically, a pre-trained thoraco-abdominal asynchrony model is obtained, including: obtaining sample phase data; inputting the training phase data into the initial thoraco-abdominal asynchrony network to obtain training results; inputting the real thoraco-abdominal asynchrony parameters corresponding to the training results and the sample phase data into the Smooth-L1 loss function to obtain loss results; training the initial thoraco-abdominal asynchrony network based on the loss results to reduce the difference between the training results and the real thoraco-abdominal asynchrony parameters until the initial thoraco-abdominal asynchrony network converges to obtain the thoraco-abdominal asynchrony network.

[0118] Step S210: The computing terminal inputs the phase data into the thoracoabdominal asynchrony model to obtain thoracoabdominal asynchrony parameters.

[0119] After obtaining the lung function parameters and chest-abdomen asynchrony parameters, the lung function parameters and chest-abdomen asynchrony parameters are fed back to the interface thread through the signal-slot mechanism, such as Figure 4 As shown, the pulmonary function parameters and chest-abdominal asynchrony parameters are updated in real time in the display interface by the interface thread.

[0120] In addition, since people of different age groups and genders have different normal ranges for various lung function parameters and thoracic and abdominal asynchrony parameters, in this embodiment, a relationship model between various parameters and health status is established based on this normal range, and the abnormalities of different parameters are combined with the correspondence with different respiratory diseases to predict potential respiratory disease information, and generate prompt information to be displayed on the display interface to prompt the subject or relevant medical staff.

[0121] Specifically, after inputting the phase data into the thoraco-abdominal asynchrony model and obtaining the thoraco-abdominal asynchrony parameters, it also includes: obtaining a pre-constructed relationship model between the parameters and the health status; generating and displaying prompt information based on the thoraco-abdominal asynchrony parameters, lung function parameters and the relationship model; the prompt information is used to indicate abnormal parameters or predict disease information.

[0122] It is worth noting that in this embodiment, steps S207 to S208 can be executed before steps S209 to S210, or after steps S209 to S210, or synchronously with steps S209 to S210. This embodiment does not limit the execution order of steps S207 to S208 and steps S209 to S210.

[0123] In this embodiment, the changes in various lung function parameters are observed in real time through the signal processing flow corresponding to the millimeter wave sensing terminal and the computing terminal and the corresponding visualization program, which can solve the problem of difficulty in lung function monitoring; there is no need to deploy expensive and large measuring instruments, and it can be deployed in multiple scenarios such as hospitals / homes to achieve long-term real-time monitoring of lung function in multiple scenarios; at the same time, the computing terminal analyzes the respiratory waveform and calculates lung function parameters, including respiratory frequency, inhalation time, exhalation time, respiratory cycle time, inhalation-exhalation time ratio, inhalation cycle time ratio, tidal breathing inspiratory peak flow and other parameters, and inputs the phase data of m×n channels into the chest-abdominal asynchrony model, and the chest-abdominal asynchrony model outputs the chest-abdominal asynchrony parameters to achieve multi-degree-of-freedom lung function monitoring, which can improve the accuracy of assessing lung health and respiratory function abnormalities.

[0124] In addition, by setting the millimeter wave sensing terminal at a preset distance from the subject, the subject does not need to wear a breathing mask or other facilities close to the body, which can improve the comfort of the subject. Especially in the case of long-term monitoring, it can reduce the discomfort and stress of the subject, reduce the complexity of operation, and improve the convenience of operation.

[0125] In addition, through the pre-constructed relationship model between parameters and health status, real-time abnormal lung function parameter detection can be achieved based on the obtained lung function parameters and chest and abdominal asynchrony parameters, and corresponding feedback and possible lung diseases can be given in time when abnormal lung function parameters occur.

[0126] In addition, through the multi-threaded processing method, the data processing thread is used to obtain lung function parameters and chest and abdominal asynchrony parameters; the interface thread is used to draw the respiratory waveform and respiratory ring image in real time, and display the respiratory waveform and respiratory ring image in real time in the display interface, so as to realize the real-time update of the display interface, so that the user can observe the lung function status in time.

[0127] This embodiment provides a multi-degree-of-freedom lung function monitoring device based on millimeter waves, such as Figure 5 This embodiment uses the device to Figure 1 In the millimeter wave sensing terminal shown, the device includes at least the following modules: a signal acquisition module 510 , a signal processing module 520 , a breathing data acquisition module 530 , a phase data acquisition module 540 and a data sending module 550 .

[0128] The signal acquisition module 510 is used to acquire a frequency modulated continuous wave signal of at least one channel in real time in response to the radar setting sent by the computing terminal.

[0129] The signal processing module 520 is used to process the frequency modulated continuous wave signal to obtain a chest and abdomen respiratory motion signal of at least one channel.

[0130] The respiratory data acquisition module 530 is used to perform multi-channel fusion on the chest and abdomen respiratory motion signals, and perform phase calculation and phase unwrapping to obtain respiratory data.

[0131] The phase data acquisition module 540 is used to perform phase calculation and phase expansion on the chest and abdomen respiratory motion signals in sequence to obtain phase data of at least one channel.

[0132] The data sending module 550 is used to send the respiratory data and phase data to the computing terminal in real time, so that the computing terminal can calculate the lung function parameters based on the respiratory data and the chest-abdomen asynchrony parameters based on the phase data.

[0133] For relevant details, refer to the above methods and embodiments.

[0134] This embodiment provides a multi-degree-of-freedom lung function monitoring device based on millimeter waves, such as Figure 6 This embodiment uses the device to Figure 1 In the computing terminal shown, the device includes at least the following modules: a data receiving module 610 , a waveform generating module 620 , a parameter generating module 630 , a model acquiring module 640 and a parameter generating module 650 .

[0135] The data receiving module 610 is used to receive respiratory data and phase data in real time; respiratory data refers to the chest and abdominal respiratory motion signals obtained by the millimeter wave sensing terminal after processing the frequency modulated continuous wave signal to obtain the chest and abdominal respiratory motion signals, performing multi-channel fusion, and performing phase calculation and phase expansion on the chest and abdominal respiratory motion signals; phase data refers to the chest and abdominal respiratory motion signals obtained by the millimeter wave sensing terminal performing phase calculation and phase expansion in sequence.

[0136] The waveform generation module 620 is used to generate a single-channel respiratory waveform in real time based on the respiratory data.

[0137] The parameter generation module 630 is used to perform real-time period positioning on the respiratory waveform and calculate the lung function parameters in real time.

[0138] The model acquisition module 640 is used to acquire a pre-trained thoraco-abdominal asynchrony model, and the thoraco-abdominal asynchrony model is used to extract differential parameters of the thoracic cavity and the abdominal cavity.

[0139] The parameter generation module 650 is used to input the phase data into the thoracoabdominal asynchrony model to obtain thoracoabdominal asynchrony parameters.

[0140] For relevant details, refer to the above methods and embodiments.

[0141] It should be noted that: the millimeter-wave-based multi-degree-of-freedom pulmonary function monitoring device provided in the above embodiment only uses the division of the above functional modules as an example when performing millimeter-wave-based multi-degree-of-freedom pulmonary function monitoring. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the millimeter-wave-based multi-degree-of-freedom pulmonary function monitoring device is divided into different functional modules to complete all or part of the functions described above. In addition, the millimeter-wave-based multi-degree-of-freedom pulmonary function monitoring device provided in the above embodiment and the millimeter-wave-based multi-degree-of-freedom pulmonary function monitoring method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0142] This embodiment provides an electronic device, such as Figure 7 The electronic device at least includes a processor 701 and a memory 702 .

[0143] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 801 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0144] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is used to be executed by the processor 701 to implement the millimeter wave-based multi-degree-of-freedom lung function monitoring method provided in the method embodiment of the present application.

[0145] In some embodiments, the electronic device may further optionally include: a peripheral device interface and at least one peripheral device. The processor 701, the memory 702 and the peripheral device interface may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface via a bus, a signal line or a circuit board. Schematically, the peripheral devices include but are not limited to: a radio frequency circuit, a touch display screen, an audio circuit, and a power supply.

[0146] Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

[0147] The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-degree-of-freedom lung function monitoring method based on millimeter waves are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0148] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the aforementioned millimeter-wave-based multi-degree-of-freedom lung function monitoring method.

[0149] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0150] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0151] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-degree-of-freedom lung function monitoring method based on millimeter waves, characterized in that: Applied to a millimeter wave sensing terminal, the method includes: In response to the radar setting sent by the computing terminal, a millimeter wave radar signal is transmitted in real time, and a frequency modulated continuous wave signal of at least one channel is acquired; the frequency modulated continuous wave signal is obtained by reflecting the millimeter wave radar signal from the chest and abdomen of the subject; Processing the frequency modulated continuous wave signal to obtain a chest and abdomen respiratory motion signal of at least one channel; Performing multi-channel fusion on the chest and abdomen respiratory motion signals, and performing phase calculation and phase unwrapping to obtain respiratory data; performing phase calculation and phase unwrapping on the chest and abdomen respiratory motion signals in sequence to obtain phase data of at least one channel; The respiratory data and the phase data are sent to the computing terminal in real time, so that the computing terminal calculates the lung function parameters based on the respiratory data and obtains the chest-abdomen asynchrony parameters based on the phase data.

2. The millimeter wave-based multi-degree-of-freedom lung function monitoring method according to claim 1, characterized in that: The step of processing the frequency modulated continuous wave signal to obtain a chest and abdomen respiratory motion signal of at least one channel includes: filtering static information from the frequency modulated continuous wave signal in a time-iterative manner; Performing range-fast Fourier transform on the frequency modulated continuous wave signal after filtering the static information to generate a range profile; Determining a target distance profile in the distance profile; the target distance profile refers to a distance profile including the chest and abdomen respiratory motion signal; Determining a target receiver corresponding to the target range profile, and generating a hot spot area for the target receiver; Performing beamforming processing on the hot spot area by the target receiver to obtain a distance-angle diagram; Performing time averaging processing on the distance-angle graph to obtain a distance-angle feature graph; The center point of the thoracoabdominal respiratory motion is located by combining the effective respiratory index with the distance-angle graph and the distance-angle characteristic graph; the effective respiratory index is used to indicate the degree of similarity between the thoracoabdominal respiratory motion signal and the thoracoabdominal respiratory motion signal corresponding to a preset respiratory mode; the preset respiratory mode includes a preset minimum respiratory rate, a maximum respiratory rate and a respiratory rate error tolerance; Determine the exact position of the center point of the chest and abdomen respiratory movement through the effective respiratory index; Expanding a preset range based on the center point of the thoracic and abdominal respiratory movement, retaining the signal within the range, and obtaining the thoracic and abdominal respiratory movement signal; Calculating the effective breathing index includes: calculating the Welch spectrum of the thoracic and abdominal respiratory motion signal by using the Welch spectrum method; obtaining the minimum respiratory rate, the maximum respiratory rate and the respiratory rate error tolerance, and determining the normal respiratory rate range and the error range; removing the DC component from the Welch spectrum, and calculating the maximum power value in the Welch spectrum; the maximum power value is the peak value in the Welch spectrum; determining whether the maximum power value is within the normal respiratory frequency range, and if the maximum power value is not within the defined normal respiratory frequency range, defining the effective breathing index as 0; if the maximum power value is within the normal respiratory frequency range, calculating the power density by dividing the power near the peak and within the respiratory rate error tolerance by the power of other information within the respiratory bandwidth to obtain the effective breathing index.

3. A multi-degree-of-freedom lung function monitoring method based on millimeter waves, characterized in that: Applied to a computing terminal, the method comprises: Respiratory data and phase data are received in real time; the respiratory data refers to the chest and abdominal respiratory motion signals obtained by the millimeter wave sensing terminal after processing the frequency modulated continuous wave signal to obtain the chest and abdominal respiratory motion signals, performing multi-channel fusion on the chest and abdominal respiratory motion signals, and performing phase calculation and phase expansion; the phase data refers to the chest and abdominal respiratory motion signals obtained by the millimeter wave sensing terminal performing phase calculation and phase expansion in sequence; Generating a single-channel respiratory waveform in real time based on the respiratory data; Performing real-time period positioning on the respiratory waveform and calculating lung function parameters in real time; Acquire a pre-trained thoracoabdominal asynchrony model, wherein the thoracoabdominal asynchrony model is used to extract differential parameters of the thoracic cavity and the abdominal cavity; The phase data is input into the thoracoabdominal asynchrony model to obtain thoracoabdominal asynchrony parameters.

4. The multi-degree-of-freedom lung function monitoring method based on millimeter waves according to claim 3, characterized in that: The real-time period positioning of the respiratory waveform and the real-time calculation of the lung function parameters include: For the respiratory waveform, the peaks and troughs of the respiratory waveform are identified in real time by a second-order difference algorithm, and the respiratory cycles are obtained by dividing the peaks and troughs; The respiratory waveform data of each respiratory cycle is divided into an exhalation segment and an inhalation segment, and the lung function parameter is calculated based on the exhalation segment and the inhalation segment.

5. The millimeter wave-based multi-degree-of-freedom lung function monitoring method according to any one of claims 3 to 4, characterized in that: The pulmonary function parameters include: respiratory rate, inhalation time, expiratory time, respiratory cycle time, inhalation-expiratory time ratio, inhalation cycle time ratio, tidal breathing inspiratory peak flow, tidal breathing expiratory peak flow, fastest inhalation-expiratory speed ratio, inhalation peak time, expiratory peak time, inhalation-expiratory peak time ratio, inhalation peak time ratio, expiratory peak time ratio, mid-inspiratory flow, mid-expiratory flow, or the ratio of mid-expiratory flow to mid-inspiratory flow.

6. The multi-degree-of-freedom lung function monitoring method based on millimeter waves according to claim 4, characterized in that: The method further includes: for the respiratory waveform, using a second-order difference algorithm to identify the peaks and troughs of the respiratory waveform in real time, and dividing the respiratory cycle to obtain the respiratory cycle; Derivative the respiratory waveform data of each cycle to obtain the corresponding cycle flow data; The respiratory waveform data and the periodic flow data are combined to draw a respiratory ring image, and the respiratory ring image and the respiratory waveform are displayed on a display interface.

7. The millimeter wave-based multi-degree-of-freedom lung function monitoring method according to claim 6, characterized in that: Before receiving the breathing data and the phase data in real time, the method further includes: A data processing thread and an interface thread are created; the data processing thread is used to obtain the lung function parameters and the chest-abdomen asynchrony parameters; the interface thread is used to draw the respiratory waveform and the respiratory ring image in real time, and display the respiratory waveform and the respiratory ring image in real time in the display interface.

8. An electronic device, characterized in that: The device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement the millimeter wave-based multi-degree-of-freedom lung function monitoring method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, which, when executed by a processor, is used to implement the millimeter-wave-based multi-degree-of-freedom lung function monitoring method as described in any one of claims 1 to 7.

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