Visual respiratory function training monitoring method and system
By collecting and processing breathing flow data on the breathing trainer, dynamically displaying the user's lung expansion and contraction process, and calculating the respiratory function intensity index, the problem of lack of monitoring and feedback in traditional breathing training is solved, and the training efficiency and effect are improved.
Patent Information
- Application Number
- CN202510249870.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-04
- Publication Date
- 2025-06-20
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional breathing training method lacks an effective monitoring and feedback mechanism, which leads to inability to understand their breathing status and training effects in a timely manner during the training process, and is inefficient.
The sensor collects the breathing air flow data of the user when using the breathing trainer and processes it at the back end, dynamically displays the expansion and contraction process of the user's lungs, and calculates and synchronizes the respiratory function intensity index.
It realizes the display of the lung training process in a dynamic and intuitive way, helping users obtain immediate feedback, and improving training effect and targetedness.
Smart Images
Figure CN120168928A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of medical rehabilitation, and particularly relates to a visualization method and system for monitoring respiratory function training. Background Art
[0002] With the increasing attention to health and the improvement of people's awareness, respiratory training, as an important means to improve respiratory function and enhance lung health, has gradually attracted attention. A respiratory trainer helps users improve respiratory efficiency, enhance lung function, and plays a significant role in rehabilitation therapy by controlling and regulating respiratory airflow, pressure, and frequency. Especially in scenarios such as lung disease rehabilitation, athlete training, and altitude adaptation, the respiratory trainer demonstrates its important application value. However, traditional respiratory training methods often lack effective monitoring and feedback mechanisms. Many users cannot timely understand their own respiratory status and training effects when using a respiratory trainer, which leads to low efficiency during the training process. Summary of the Invention
[0003] The object of the present invention is to provide a visualization method and system for monitoring respiratory function training to solve the deficiencies in the prior art, and to be able to display the training process of the lungs in a dynamic and intuitive manner, help users obtain immediate feedback during training, and improve the training effect and pertinence.
[0004] An embodiment of the present application provides a visualization method for monitoring respiratory function training, and the method includes:
[0005] Collecting relevant respiratory airflow data of a user during respiratory function training using a respiratory trainer through a sensor;
[0006] Sending the relevant respiratory airflow data to the backend for processing, simulating and dynamically displaying the expansion and contraction process of the lungs of the user during respiratory function training on a display terminal, wherein the sensor and the display terminal are respectively communicatively connected to the backend;
[0007] Determining relevant respiratory indexes of the user during respiratory function training according to the relevant respiratory airflow data;
[0008] Calculating a respiratory function intensity index of the user based on the relevant respiratory airflow data and the relevant respiratory indexes, and synchronously displaying the relevant respiratory indexes and the respiratory function intensity index on the display terminal.
[0009] Optionally, the relevant respiratory indexes at least include: forced vital capacity FVC, forced expiratory volume in one second FEV1, one-second rate FEV1 / FVC, vital capacity VC.
[0010] Optionally, the calculation formula of the respiratory function intensity index is:
[0011]
[0012] Wherein, S is the respiratory function intensity index, SFVC is the index quantization score of forced vital capacity, SFEV1 is the index quantization score of forced expiratory volume in the first second, is the index quantization score of one-second rate, SVC is the index quantization score of vital capacity, and w1, w2, w3, w4 are corresponding weight factors.
[0013] Optionally, sending the relevant respiratory airflow data to the backend for processing, simulating and dynamically displaying the expansion and contraction process of the lungs when the user performs respiratory function training on the display terminal, including:
[0014] Generating a three-dimensional lung model of the user using a statistical model based on the user's physiological parameters, and performing meshing processing on the three-dimensional lung model to generate a polygon mesh structure suitable for real-time rendering;
[0015] Mapping the real-time relevant respiratory airflow data to the volume change of the three-dimensional lung model, using finite element analysis to simulate the mechanical effect of the airflow on the lung tissue, and determining the expansion and contraction of the alveoli;
[0016] Implementing dynamic rendering of the three-dimensional lung model using a GPU-accelerated rendering engine, and realizing dynamic changes in the color of the lung surface through shader programming to simulate physiological changes during breathing;
[0017] Dynamically adjusting the color and transparency of the lung model according to the airflow velocity and volume change and displaying it in real time on the display terminal, wherein color gradients are used to represent different breathing intensities, and transparency changes represent the degree of inflation of the lungs.
[0018] Another embodiment of the present application provides a visual respiratory function training monitoring system, and the system includes:
[0019] An acquisition module, configured to acquire relevant respiratory airflow data when the user performs respiratory function training using a respiratory trainer through a sensor;
[0020] A simulation module, configured to send the relevant respiratory airflow data to the backend for processing, simulate and dynamically display the expansion and contraction process of the lungs when the user performs respiratory function training on the display terminal, wherein the sensor and the display terminal are respectively communicatively connected to the backend;
[0021] A determination module, configured to determine relevant respiratory indexes when the user performs respiratory function training according to the relevant respiratory airflow data;
[0022] A display module, configured to calculate a respiratory function intensity index of the user based on the relevant respiratory airflow data and the relevant respiratory metrics, and synchronously display the relevant respiratory metrics and the respiratory function intensity index on the display terminal.
[0023] Another embodiment of the present application provides a storage medium, in which a computer program is stored, and the computer program is configured to execute the method described in any one of the above when running.
[0024] Another embodiment of the present application provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method described in any one of the above.
[0025] Compared with the prior art, a visualized respiratory function training monitoring method provided by the present invention collects relevant respiratory airflow data of a user during respiratory function training using a respiratory trainer through a sensor; sends the relevant respiratory airflow data to the backend for processing, simulates and dynamically displays the expansion and contraction process of the lungs of the user during respiratory function training on a display terminal; determines relevant respiratory metrics of the user during respiratory function training according to the relevant respiratory airflow data; calculates a respiratory function intensity index of the user based on the relevant respiratory airflow data and the relevant respiratory metrics, and synchronously displays the relevant respiratory metrics and the respiratory function intensity index on the display terminal, so as to be able to display the training process of the lungs in a dynamic and intuitive manner, help the user obtain immediate feedback during training, and improve the training effect and pertinence. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 It is a hardware structure block diagram of a computer terminal of a visualized respiratory function training monitoring method provided by an embodiment of the present invention;
[0027] Figure 2 It is a flowchart of a visualized respiratory function training monitoring method provided by an embodiment of the present invention;
[0028] Figure 3 It is a structural diagram of a visualized respiratory function training monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0029] The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.
[0030] An embodiment of the present invention first provides a visualized respiratory function training monitoring method, which can be applied to an electronic device, such as a computer terminal, specifically, such as an ordinary computer, etc.
[0031] The following takes running on a computer terminal as an example for detailed description. Figure 1 The following is a hardware structure block diagram of a computer terminal for a visual breathing function training monitoring method provided by an embodiment of the present invention. As Figure 1 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory may include a non-volatile storage medium and an internal memory.
[0032] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any visual breathing function training monitoring method.
[0033] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
[0034] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any visual breathing function training monitoring method.
[0035] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 1 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.
[0036] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0037] Referring to Figure 2 , an embodiment of the present invention provides a visual breathing function training monitoring method, which may include the following steps:
[0038] S201. Collect relevant respiratory airflow data through sensors when the user performs respiratory function training using a breathing trainer.
[0039] In this method, high-sensitivity airflow sensors are used to collect the user's respiratory airflow data in real time when using the breathing trainer. Specifically, these sensors are installed in the intake and exhaust channels of the breathing trainer and can accurately record the user's inhalation and exhalation behaviors during training. Each time the user takes a breath, the airflow sensor detects parameters such as airflow rate, gas volume, and pressure changes. These parameters continuously generate a data stream, capturing the dynamic changes of each inhalation and exhalation of the user. For example, the sensor can monitor the maximum inhalation airflow rate during deep breathing and the gas discharge volume during exhalation. After the data is collected, it will be sent to the backend for further analysis and processing to support subsequent respiratory function assessment and training effect feedback.
[0040] By accurately collecting the user's respiratory airflow data, it can provide a scientific evaluation basis for both the user and medical professionals. This real-time monitoring not only enables the user to intuitively understand their own respiratory status and training progress but also provides objective and quantifiable data support for doctors, helping to evaluate the patient's lung function and training effect. By analyzing the collected respiratory airflow data, the user can identify their respiratory strengths and weaknesses, thereby better formulating targeted training programs to achieve the effects of optimizing lung health, enhancing respiratory function, and improving overall physical fitness.
[0041] In the specific implementation process, the design of the breathing trainer includes high-sensitivity airflow sensors, which are placed in the inhalation and exhalation channels. When the user performs breathing training, they first need to place the breathing trainer at the mouth and perform breathing exercises according to the training requirements. When the user inhales, the airflow sensor records the rate and volume of the inhaled gas and also measures the pressure change caused by the airflow passing through the sensor. At this time, the sensor converts these real-time data into electrical signals and continuously sends them to the microprocessor embedded in the breathing device, and the microprocessor will monitor and record these data in real time.
[0042] Once the user starts to exhale, the airflow changes generated during the process will also be captured by the sensor, recording the relevant parameters of the exhaled gas. These data will gradually accumulate over time to form a dataset with a series of timestamps. To ensure the accuracy of the data, the system calibrates the sensor before the start of data collection to eliminate the influence of environmental air pressure and temperature.
[0043] After the data collection is completed, these airflow data will be transmitted in real time to the backend processing system through a built-in wireless module (such as Bluetooth or Wi-Fi). The backend system cleans, filters, and analyzes the received data. During the data analysis process, the system extracts key respiratory metrics, such as forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1), and calculates a respiratory function intensity index related to the user's physiology. Based on these metrics, the system also generates a personalized feedback report for the user to help them understand their respiratory health status. Finally, these data will be dynamically presented to the user through a connected display terminal for them to monitor the training effect in real time.
[0044] S202, send the relevant respiratory airflow data to the backend for processing, simulate and dynamically display the expansion and contraction process of the lungs when the user is performing respiratory function training on the display terminal, wherein the sensor and the display terminal are respectively communicatively connected to the backend;
[0045] In this method, first, relevant respiratory airflow data of the user during the use of the breathing trainer is collected in real time by a sensor. These data include the speed and volume changes of the airflow during inhalation and exhalation. The sensor transmits these data to the backend processing system in real time. After receiving the data, the backend system analyzes and processes it to simulate the expansion and contraction process of the user's lungs, and transmits the result to the display terminal through the communication connection for dynamic display in front of the user. This real-time monitoring and feedback mechanism not only improves the user's training experience but also ensures the accuracy and timeliness of the displayed information.
[0046] The significance of this process is to provide the user with a highly interactive and visual breathing training experience. Through real-time dynamic display, the user can intuitively understand their breathing status and training effect, thus motivating them to conduct more effective training. At the same time, the analysis and feedback of real-time data can not only help the user adjust their breathing skills but also serve as an important reference for medical professionals to monitor the user's respiratory function, thereby improving the overall respiratory health management.
[0047] Specifically, a three-dimensional lung model of the user can be generated based on the user's physiological parameters using a statistical model, and the three-dimensional lung model is meshed to generate a polygon mesh structure suitable for real-time rendering;
[0048] In this step, the system applies a statistical model to generate a personalized three-dimensional lung model according to the user's basic physiological parameters (such as age, gender, height, and weight, etc.). This model characterizes the shape, size, and physiological characteristics of the user's lungs. After the model construction is completed, the subsequent meshing process transforms it into a structure composed of polygons to ensure that the visual effect calculations can be efficiently performed during real-time rendering.
[0049] The core of this step lies in laying a solid foundation for subsequent dynamic display. The generated personalized 3D model not only makes the visualization of the lungs more realistic and credible, but also enhances the user's sense of participation, enabling them to see a lung model that conforms to their physiological characteristics, which provides important support for effective breathing function training.
[0050] In actual implementation, the system first obtains the user's basic information and collects physiological data using questionnaires or health devices. Then, the system will reference an established statistical model, such as a linear regression model, to estimate the volume and shape of the user's lungs through historical sample data, thereby generating a 3D model of the lungs. This model will then be meshed using computer graphics techniques, and the surface of the model will be subdivided into tens of thousands of small triangles using a triangulation algorithm to improve the computational efficiency and visual details during rendering. During this process, 3D modeling tools (such as Blender or Maya) can also be used to further optimize the model to ensure its authenticity and vividness during presentation.
[0051] During implementation, the system will first collect the user's basic physiological data through a user interface (UI). These data include age (e.g., 30 years old), gender (male), height (175 cm), and weight (75 kg). After collecting this information, the system will use an established statistical model, such as a multiple linear regression model, to predict the volume and shape of the user's lungs. For example, assuming the prediction model knows the influence of gender, height, and weight on lung volume, it calculates the predicted lung volume of this user to be 5.2 liters.
[0052] Subsequently, using computer-aided design (CAD) software, the system will generate a 3D lung model based on the above volume data. This model may start with a standard lung shape (e.g., represented by an ellipsoid) and then be adjusted according to physiological parameters to make it more conform to individual characteristics. After completing the model, the system will mesh it in 3D software using a triangulation algorithm (such as Delaunay triangulation) to divide its surface into thousands of small triangles, forming a polygonal mesh structure. In this way, the final 3D model can support real-time rendering in terms of details and overall effects and is suitable for subsequent dynamic display.
[0053] Map real-time relevant respiratory airflow data to the volume change of the 3D lung model, use finite element analysis to simulate the mechanical action of the airflow on lung tissue, and determine the expansion and contraction of alveoli;
[0054] In this step, the system correlates the real-time collected respiratory airflow data with the generated three-dimensional lung model. Specifically, according to the user's inhalation and exhalation processes, the airflow data will be used to adjust the volume changes of the model. Through finite element analysis (FEA), the system can simulate the mechanical effects exerted by the airflow on the lung tissue, thereby accurately calculating how the alveoli expand and contract during inhalation and exhalation.
[0055] The significance of this step lies in achieving a realistic simulation of the breathing process, enabling the user to intuitively feel the physiological changes of their own breathing. This dynamic simulation not only has educational significance but also helps users better master breathing techniques, improve training effects, and enhance their understanding and response abilities to their own physiological states during the training process.
[0056] During implementation, the system first preprocesses the real-time monitored respiratory airflow data to extract information about airflow velocity and volume changes. Then, using finite element analysis software (such as ANSYS or COMSOL) for calculation, the system maps the airflow data onto the three-dimensional lung model to analyze the mechanical effects of the airflow on the lung tissue. During the simulation, the alveoli are regarded as elastic bodies, and using the mechanical equations in physics, the system can calculate the specific changes of the alveoli during inhalation and exhalation through simulation. Finally, these dynamic changes will be updated in real time to the three-dimensional model, so that what the user sees on the display terminal is the dynamic performance of the lungs that conforms to their breathing state.
[0057] In this implementation step, the system first monitors the user's respiratory airflow data in real time. For example, during a complete inhalation process, the monitored airflow rate is 0.5 liters per second and the duration is 4 seconds. Then, the user inhales 2 liters of gas during this process. Next, the system maps these real-time data to the already generated three-dimensional lung model.
[0058] The specific operation is to import the real-time airflow data into the model using finite element analysis (FEA) software (such as ANSYS) and simulate the mechanical effects exerted by the airflow on the lung tissue through numerical calculation. During this process, the alveoli are regarded as elastic bodies to simulate how the entry of the airflow causes the alveoli to expand. Suppose the analysis results show that the alveoli expand their volume by 30% during inhalation. Based on these data, the system updates the three-dimensional model to show how the alveoli change with the size of the airflow during breathing.
[0059] Finally, the model is updated in real time to show the dynamic changes in the lung structure. For example, during inhalation, the volume of the left and right sides of the lungs gradually increases, reflecting the flow of gas and the expansion of the alveoli. These changes will be retrieved and updated to the user's display terminal, enabling the user to visually observe the dynamics of their own lungs.
[0060] The dynamic rendering of the three-dimensional lung model is achieved using a GPU-accelerated rendering engine, and the dynamic change of the lung surface color is realized through shader programming to simulate the physiological changes during breathing;
[0061] In this step, the system uses a rendering engine accelerated by a Graphics Processing Unit (GPU) to perform dynamic rendering on the three-dimensional lung model. Through efficient shader programming, the color change on the model surface will reflect the physiological changes during the breathing process, such as the inflation and deflation states of the alveoli. In this way, users can more intuitively observe the real-time changes in their breathing state.
[0062] The importance of this process lies in that it not only enhances the realism of the visual effect but also strengthens the interaction between the user and the device. The dynamic color change is not only a manifestation of visual stimulation but also subtly educates the user to be aware of the physiological changes during the breathing process, thus promoting more effective training.
[0063] In the specific implementation, the system will select a rendering engine that supports GPU acceleration, such as Unity3D or UnrealEngine, for development. The rules for color and transparency changes of the lung model in different breathing states can be set. For example, during inhalation, the color of the lung model may change from light blue to bright blue, and during exhalation, it gradually changes from bright blue to gray. To this end, the corresponding shader program can be written to synchronize the dynamic color effect with the airflow change with the help of a graphics API (such as OpenGL or DirectX). Combining with the changes in real-time data, the rendering engine will continuously update the visual effect of the model, so as to ensure that what the user sees on the display terminal is a constantly changing and visually impactful lung state.
[0064] When implementing this step, the developer selects Unity3D as the rendering engine and creates a dynamic rendering scene for the three-dimensional lung model. First, the developer imports the just-generated meshed lung model and makes settings in Unity. For the display effect of the model, the developer writes the corresponding shader program so that the model surface can present different colors in different breathing states.
[0065] For example, when the airflow rate reaches a certain value, indicating an increase in the inhalation intensity, the shader will change the color of the lung surface from cyan to bright blue; during the exhalation phase, its color changes to light blue or gray. In addition, the developer also applies a dynamic light source to the surface of the model to enhance the three-dimensional sense and realism.
[0066] Specifically, developers write shader code using the ShaderLab programming language to achieve the mapping of colors and airflow parameters through conditional judgments. For example, if the airflow rate is greater than 1 liter per second, the shader automatically sets the color to blue and adjusts the transparency according to the gas flow rate to show the degree of lung inflation. Finally, this dynamic rendering effect will be presented in real time on the user's display terminal, and the user can intuitively see the changes in the lungs during each breath.
[0067] Dynamically adjust the color and transparency of the lung model according to the airflow speed and volume changes and display them in real time on the display terminal. Among them, color gradients are used to represent different breathing intensities, and transparency changes represent the degree of lung inflation.
[0068] In the last step, the system dynamically adjusts the color and transparency of the three-dimensional lung model according to the real-time monitored airflow speed and volume changes. This operation aims to further enhance the visualization effect, enabling users to clearly see the breathing intensity and the inflation state of the lungs through the changes in color and transparency during breathing training. This feedback mechanism enables users to timely understand their own breathing status and then make corresponding adjustments.
[0069] This process of dynamic adjustment not only improves the richness and layering of visualization but also enhances the user's sense of participation and experience satisfaction. Users can directly feel the changes in the intensity of their own breathing through visual feedback, thereby performing breathing training more accurately and achieving better results.
[0070] In specific implementation, the system constructs a dynamic mapping rule based on the real-time monitored airflow speed and volume changes. For example, when the airflow speed increases, the system adjusts the color of the lung model to a darker tone, indicating an increase in the intensity of inhalation. At the same time, the transparency will decrease accordingly to reflect the degree of lung inflation. This process can be achieved by retrieving the color gradient library, and each breathing intensity and inflation degree corresponds to a specific color and transparency value. Finally, the results of these adjustments will be displayed on the user's terminal through the real-time rendering engine to ensure that users always maintain an understanding and control of their own status during training.
[0071] When implementing this step, the system will make dynamic adjustments according to the real-time airflow monitoring data. For example, assume that during a training session, the inhalation airflow rate of the user is 0.8 liters per second and the inhaled gas volume is 1.6 liters. At this time, the system will calculate the corresponding color and transparency change ranges.
[0072] Specifically, the system sets a dynamic mapping rule: when the air flow speed is between 0.5 liters per second and 1 liter per second, the color of the lung model should gradually change from green to blue, and the transparency gradually decreases from 80% to 50%. In this case, the system will dynamically generate color gradients and set the transparency according to the monitored air flow speed. Assuming the air flow rate is 0.8 liters per second, the surface color of the lung is set to dark blue, and the transparency is set to 60%.
[0073] In the implementation process, developers will configure the real-time update mechanism in Unity, continuously monitor the air flow data through an Update function, and update the color and transparency of the lung model every time the data changes. These changes will be presented in real time on the user's display terminal, enabling the user to clearly perceive their current breathing intensity and lung inflation status through the changes in color depth and transparency. Finally, after the training is completed, the system will save the information displayed by these dynamic data for subsequent data analysis and utilization.
[0074] S203, determine the relevant respiratory indexes of the user during the respiratory function training according to the relevant respiratory air flow data;
[0075] In this step of the method, the system determines the relevant respiratory indexes of the user during the respiratory function training by analyzing the user's respiratory air flow data collected by the sensor. These indexes usually include forced vital capacity (FVC), forced expiratory volume in one second (FEV1), one-second rate (FEV1 / FVC), and vital capacity (VC). FVC reflects the user's maximum exhalation ability, FEV1 represents the exhaled volume of the user within the first second, and the one-second rate is the ratio of FEV1 to FVC, indicating the magnitude of air flow resistance. In addition, vital capacity (VC) describes the total amount of air that the user can inhale and exhale under normal circumstances. Through the calculation of these indexes, the system can comprehensively evaluate the user's lung function and thus judge their respiratory health status.
[0076] Determining the user's relevant respiratory indexes is crucial for evaluating and monitoring respiratory function. These indexes can not only help users understand their own respiratory capabilities, identify potential health problems, but also provide strong data support for professional medical staff to design personalized training programs and provide health guidance. Through the real-time monitoring of these indexes, users can obtain feedback, thereby adjusting the training intensity and methods to improve the overall training effect and the improvement of respiratory function.
[0077] In the specific implementation process, the system first collects the user's respiratory airflow data through sensors. The data includes the rates and volumes of inhalation and exhalation. Suppose in a training session, the user's exhalation airflow data is recorded as follows: the exhaled volume in the first second is 2.5 liters, and the total exhaled volume during the entire exhalation process is 5 liters. The system inputs this data into the backend processing module. The backend module first calculates the forced vital capacity (FVC), which is the user's maximum exhaled volume. In this example, the FVC is 5 liters. Next, the system calculates the forced expiratory volume in one second (FEV1). In this case, the FEV1 is known to be 2.5 liters. Then, it calculates the one-second ratio (FEV1 / FVC), and the result is 0.5, indicating that the gas exhaled by the user in the first second accounts for 50% of their forced vital capacity. Finally, it calculates the vital capacity (VC). Suppose the recorded VC at this time is 6 liters. These analysis results will be updated in real-time and fed back to the user's display terminal. The user can see these key respiratory indicators during the training process, which helps them understand their own respiratory capacity.
[0078] Among them, the relevant respiratory indicators include at least but are not limited to: forced vital capacity FVC, forced expiratory volume in one second FEV1, one-second ratio FEV1 / FVC, vital capacity VC.
[0079] The relevant respiratory indicators include forced vital capacity (FVC), forced expiratory volume in one second (FEV1), one-second ratio (FEV1 / FVC), and vital capacity (VC). The forced vital capacity (FVC) refers to the total volume of gas exhaled by an individual in one go with maximum effort and is an important indicator for evaluating lung function. The forced expiratory volume in one second (FEV1) represents the volume of gas that the user can exhale within the first second of exhalation and is usually used to evaluate airway patency. The one-second ratio (FEV1 / FVC) is the ratio of FEV1 to FVC, which reflects the degree of airway obstruction. The vital capacity (VC) describes the total volume of gas that the user can exhale after a maximum inhalation and provides important information about the overall health of the lungs.
[0080] The calculation and monitoring of these respiratory indicators are of great significance in respiratory health assessment and training. By obtaining and analyzing these data in real-time, users can not only understand their own respiratory conditions but also adjust their training strategies during the training process to improve lung function. For medical professionals, they can evaluate the lung health of patients through these indicators and formulate personalized rehabilitation plans and training programs. The changing trends of these indicators also help doctors monitor the progress or rehabilitation effect of diseases, so as to provide necessary adjustments and suggestions in a timely manner to ensure the health and safety of users.
[0081] S204, based on the relevant respiratory airflow data and the relevant respiratory indicators, calculate the respiratory function intensity index of the user, and synchronously display the relevant respiratory indicators and the respiratory function intensity index on the display terminal.
[0082] In this step, the system calculates the user's respiratory function intensity index (S) based on the relevant respiratory airflow data and corresponding respiratory metrics collected during the breathing training process. This process first involves analyzing the user's respiratory airflow data, including information such as the airflow rate and volume during inhalation and exhalation. These data can help evaluate the user's respiratory condition. By calculating a series of key respiratory metrics (such as forced vital capacity FVC, forced expiratory volume in the first second FEV1, etc.), the system can quantitatively score the user's respiratory ability. Then, the system will use these scores and, through weighted summation, combined with preset weight factors, calculate a comprehensive respiratory function intensity index. Finally, these relevant respiratory metrics and the calculated respiratory function intensity index will be synchronously and real-time displayed on the user's display terminal, enabling the user to intuitively understand their training effect and respiratory status.
[0083] The significance of calculating the respiratory function intensity index is to provide an intuitive way to measure the user's respiratory ability and its training effect. This index not only provides a tool for the user to evaluate their training effect but also helps the user formulate a more effective training plan. By continuously monitoring and analyzing the user's respiratory metrics, the user can adjust the training strategy in real-time to more effectively achieve the goal of exercising lung function. At the same time, medical professionals can also use this index as an important reference basis to help them evaluate and track the user's health condition. This real-time feedback mechanism will greatly enhance the user's exercise enthusiasm and effect, thereby improving their overall respiratory health.
[0084] In specific implementation, the system first collects the user's respiratory airflow data through sensors, which reflect the airflow rate and volume changes during the user's training. The sensors transmit this information to the backend processing unit, and the backend uses data processing algorithms to analyze this raw data and extract key respiratory metrics such as forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), etc. After obtaining these metrics, the algorithm calculates the corresponding quantitative scores for each metric, and these scores will be used for subsequent comprehensive calculations. Then, the system introduces preset weight factors, which reflect the importance of each metric in evaluating the user's respiratory function. By means of weighted summation, the scores are combined to finally generate the user's respiratory function intensity index. This process can be automatically completed by a data analysis model to ensure the accuracy and timeliness of the calculation. Finally, all relevant metrics and the calculated respiratory function intensity index will be synchronized and real-time displayed on the user's display terminal, presented through a graphical interface, enabling the user to clearly observe their training effect and thus motivating them to carry out subsequent training.
[0085] Specifically, a calculation formula for the respiratory function intensity index can be:
[0086]
[0087] Among them, the S is the respiratory function intensity index, which is a comprehensive index reflecting the overall respiratory function state of the user. The higher this index, the better the user's respiratory ability and training effect. The S_{FVC} is the index quantization score of the forced vital capacity, representing the maximum amount of gas that the user can exhale in a single forced exhalation, and is an important parameter for measuring the lung expansion ability. The S_{FEV1} is the index quantization score of the forced expiratory volume in the first second, indicating the amount of gas that the user can exhale within the first second, and is usually used to evaluate the patency of the airway. The is the index quantization score of the one-second rate, reflecting the patency of the respiratory tract and being an important index for judging whether the lung function is normal. The S_{VC} is the index quantization score of the vital capacity, which represents the maximum amount of gas that the user can inhale and exhale in a single breath and is a key index for evaluating the overall health status of the lungs. The w_1, the w_2, the w_3, and the w_4 are the corresponding weight factors.
[0088] Overall, by comprehensively weighting multiple important respiratory indexes, the formula makes the calculation result more comprehensive and scientific, and can better reflect the actual comprehensive lung function of the user.
[0089] It can be seen that relevant respiratory airflow data of the user during respiratory function training using a respiratory trainer are collected through a sensor; the relevant respiratory airflow data are sent to the backend for processing, and the expansion and contraction process of the lungs during the user's respiratory function training is simulated and dynamically displayed on a display terminal; based on the relevant respiratory airflow data, relevant respiratory indexes of the user during respiratory function training are determined; based on the relevant respiratory airflow data and the relevant respiratory indexes, the respiratory function intensity index of the user is calculated, and the relevant respiratory indexes and the respiratory function intensity index are synchronously displayed on the display terminal, so as to be able to display the training process of the lungs in a dynamic and intuitive manner, help the user obtain immediate feedback during training, and improve the training effect and pertinence.
[0090] Another embodiment of the present invention provides a visual respiratory function training monitoring system. Refer to Figure 3 , the system may include:
[0091] An acquisition module 301, configured to collect relevant respiratory airflow data of the user during respiratory function training using a respiratory trainer through a sensor;
[0092] The simulation module 302 is configured to send the relevant respiratory airflow data to the backend for processing, simulate and dynamically display on the display terminal the expansion and contraction process of the lungs when the user performs respiratory function training, wherein the sensor and the display terminal are respectively communicatively connected to the backend;
[0093] The determination module 303 is configured to determine relevant respiratory indexes of the user when performing respiratory function training according to the relevant respiratory airflow data;
[0094] The display module 304 is configured to calculate a respiratory function intensity index of the user based on the relevant respiratory airflow data and the relevant respiratory indexes, and synchronously display the relevant respiratory indexes and the respiratory function intensity index on the display terminal.
[0095] It can be seen that relevant respiratory airflow data of the user when performing respiratory function training using a respiratory trainer is collected by the sensor; the relevant respiratory airflow data is sent to the backend for processing, and the expansion and contraction process of the lungs when the user performs respiratory function training is simulated and dynamically displayed on the display terminal; relevant respiratory indexes of the user when performing respiratory function training are determined according to the relevant respiratory airflow data; a respiratory function intensity index of the user is calculated based on the relevant respiratory airflow data and the relevant respiratory indexes, and the relevant respiratory indexes and the respiratory function intensity index are synchronously displayed on the display terminal, so that the training process of the lungs can be displayed in a dynamic and intuitive manner, helping the user obtain immediate feedback during training and improving the training effect and pertinence.
[0096] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps in any one of the above method embodiments when running.
[0097] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for executing the following steps:
[0098] S201, collecting relevant respiratory airflow data of the user when performing respiratory function training using a respiratory trainer by the sensor;
[0099] S202, sending the relevant respiratory airflow data to the backend for processing, simulating and dynamically displaying on the display terminal the expansion and contraction process of the lungs when the user performs respiratory function training, wherein the sensor and the display terminal are respectively communicatively connected to the backend;
[0100] S203, determining relevant respiratory indexes of the user when performing respiratory function training according to the relevant respiratory airflow data;
[0101] S204. Calculate the respiratory function intensity index of the user based on the relevant respiratory airflow data and the relevant respiratory metrics, and synchronously display the relevant respiratory metrics and the respiratory function intensity index on the display terminal.
[0102] It can be seen that relevant respiratory airflow data of the user during respiratory function training using a respiratory trainer is collected by a sensor; the relevant respiratory airflow data is sent to the backend for processing, and the expansion and contraction process of the lungs during the user's respiratory function training is simulated and dynamically displayed on the display terminal; based on the relevant respiratory airflow data, relevant respiratory metrics of the user during respiratory function training are determined; the respiratory function intensity index of the user is calculated based on the relevant respiratory airflow data and the relevant respiratory metrics, and the relevant respiratory metrics and the respiratory function intensity index are synchronously displayed on the display terminal, so as to be able to display the training process of the lungs in a dynamic and intuitive manner, help the user obtain immediate feedback during training, and improve the effect and pertinence of training.
[0103] An embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.
[0104] Specifically, the above electronic device may further include a transmission device and an input / output device. Among them, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.
[0105] Specifically, in this embodiment, the above processor may be configured to execute the following steps through a computer program:
[0106] S201. Collect relevant respiratory airflow data of the user during respiratory function training using a respiratory trainer by a sensor;
[0107] S202. Send the relevant respiratory airflow data to the backend for processing, simulate and dynamically display the expansion and contraction process of the lungs during the user's respiratory function training on the display terminal, where the sensor and the display terminal are respectively communicatively connected to the backend;
[0108] S203. Determine relevant respiratory metrics of the user during respiratory function training based on the relevant respiratory airflow data;
[0109] S204. Calculate the respiratory function intensity index of the user based on the relevant respiratory airflow data and the relevant respiratory metrics, and synchronously display the relevant respiratory metrics and the respiratory function intensity index on the display terminal.
[0110] It can be seen that relevant respiratory airflow data of a user during respiratory function training using a respiratory trainer is collected by a sensor; the relevant respiratory airflow data is sent to the backend for processing, and the expansion and contraction process of the lungs of the user during respiratory function training is simulated and dynamically displayed on a display terminal; relevant respiratory indexes of the user during respiratory function training are determined according to the relevant respiratory airflow data; based on the relevant respiratory airflow data and the relevant respiratory indexes, a respiratory function intensity index of the user is calculated, and the relevant respiratory indexes and the respiratory function intensity index are synchronously displayed on the display terminal, so that the training process of the lungs can be displayed in a dynamic and intuitive manner, helping the user obtain instant feedback during training and improving the training effect and pertinence.
[0111] The structure, features and function effects of the present invention have been described in detail based on the embodiments shown in the drawings. The above is only the preferred embodiment of the present invention, but the present invention is not limited to the scope defined by the drawings. Any changes made according to the concept of the present invention, or equivalent embodiments modified into equivalent changes, still within the spirit covered by the specification and the drawings, should be within the protection scope of the present invention.
Claims
1. A visual respiratory function training monitoring method, characterized in that: The method comprises: The sensor is used to collect relevant respiratory airflow data when the user uses the respiratory trainer to perform respiratory function training; The relevant respiratory airflow data is sent to the back-end for processing, and the expansion and contraction process of the lungs of the user during respiratory function training is simulated and dynamically displayed on the display terminal, wherein the sensor and the display terminal are respectively connected to the back-end for communication; Determining, according to the relevant respiratory airflow data, relevant respiratory indicators when the user performs respiratory function training; Based on the relevant respiratory airflow data and the relevant respiratory index, the respiratory function intensity index of the user is calculated, and the relevant respiratory index and the respiratory function intensity index are synchronously displayed on the display terminal.
2. The method according to claim 1, characterized in that: The relevant respiratory indicators include at least: forced vital capacity FVC, forced expiratory volume in one second FEV1, one-second rate FEV1 / FVC, and vital capacity VC.
3. The method according to claim 2, characterized in that The calculation formula of the respiratory function intensity index is: Wherein, S is the respiratory function strength index, S_{FVC} is the index quantitative score of forced vital capacity, S_{FEV1} is the index quantitative score of forced expiratory volume in the first second, is the quantitative score of rate per second, S_{VC} is the quantitative score of vital capacity, and w_1, w_2, w_3, and w_4 are corresponding weight factors.
4. The method according to claim 3, characterized in that The relevant respiratory airflow data is sent to the back end for processing, and the expansion and contraction process of the lungs when the user performs respiratory function training is simulated and dynamically displayed on the display terminal, including: Based on the physiological parameters of the user, a statistical model is used to generate a three-dimensional lung model of the user, and the three-dimensional lung model is meshed to generate a polygonal mesh structure suitable for real-time rendering; Mapping the real-time relevant respiratory airflow data to the volume changes of the three-dimensional lung model, using finite element analysis to simulate the mechanical effects of airflow on lung tissue to determine the expansion and contraction of alveoli; Using a GPU-accelerated rendering engine to achieve dynamic rendering of the three-dimensional lung model, and using shader programming to achieve dynamic changes in the color of the lung surface to simulate physiological changes during breathing; According to the changes in airflow velocity and volume, the color and transparency of the lung model are dynamically adjusted and displayed in real time on the display terminal, where color gradients are used to represent different breathing intensities, and transparency changes represent the degree of inflation of the lungs.
5. A visual respiratory function training monitoring system, characterized in that: The system comprises: A collection module, used to collect relevant respiratory airflow data when a user uses a respiratory trainer to perform respiratory function training through a sensor; A simulation module, used for sending the relevant respiratory airflow data to the back end for processing, simulating and dynamically displaying on the display terminal the expansion and contraction process of the lungs when the user performs respiratory function training, wherein the sensor and the display terminal are respectively connected to the back end in communication; A determination module, used to determine relevant breathing indicators of the user when performing respiratory function training according to the relevant respiratory airflow data; The display module is used to calculate the user's respiratory function intensity index based on the relevant respiratory airflow data and the relevant respiratory index, and synchronously display the relevant respiratory index and the respiratory function intensity index on the display terminal.
6. The system according to claim 5, characterized in that The relevant respiratory indicators include at least: forced vital capacity FVC, forced expiratory volume in one second FEV1, one-second rate FEV1 / FVC, and vital capacity VC.
7. The system according to claim 6, characterized in that The calculation formula of the respiratory function intensity index is: Wherein, S is the respiratory function strength index, S_{FVC} is the index quantitative score of forced vital capacity, S_{FEV1} is the index quantitative score of forced expiratory volume in the first second, is the quantitative score of rate per second, S_{VC} is the quantitative score of vital capacity, and w_1, w_2, w_3, and w_4 are corresponding weight factors.
8. The system according to claim 7, characterized in that The simulation module is specifically used for: Based on the physiological parameters of the user, a statistical model is used to generate a three-dimensional lung model of the user, and the three-dimensional lung model is meshed to generate a polygonal mesh structure suitable for real-time rendering; Mapping the real-time relevant respiratory airflow data to the volume changes of the three-dimensional lung model, using finite element analysis to simulate the mechanical effects of airflow on lung tissue to determine the expansion and contraction of alveoli; Using a GPU-accelerated rendering engine to achieve dynamic rendering of the three-dimensional lung model, and using shader programming to achieve dynamic changes in the color of the lung surface to simulate physiological changes during breathing; According to the changes in airflow velocity and volume, the color and transparency of the lung model are dynamically adjusted and displayed in real time on the display terminal, where color gradients are used to represent different breathing intensities, and transparency changes represent the degree of inflation of the lungs.
9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.
10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.