CN113555082B - Intelligent guiding training method for respiratory function - Google Patents

Abstract

The invention relates to the field of big data processing, in particular to an intelligent guiding training method for respiratory functions. Step one: collecting data of basic physiological indexes related to respiratory functions of a current user; step two: inquiring a breath training scheme database according to real-time monitoring data of various basic physiological indexes of the current user to obtain a breath training scheme which is most matched with the current user; step three: when a user executes a respiratory training scheme, acquiring the state change of respiratory airflow of the mouth and nose of the user and the state change of respiratory muscles; converting the state changes of respiratory airflow and respiratory muscles into real-time respiratory training detection amounts of the current user; step four: and displaying the requirements and the execution conditions of the breathing training scheme in an interactive mode of graphic display and voice prompt, and guiding the user. The invention solves the problems that the existing respiratory function training effect is poor, and a patient cannot intuitively control the training process.

Description

An intelligent guided training method for respiratory function

technical field

The invention relates to the field of big data processing, in particular to an intelligent guidance training method for respiratory function.

Background technique

Respiratory function training is a routine rehabilitation method for patients with certain lung diseases or respiratory dysfunction; through regular respiratory function training, the purpose of improving or restoring the patient's respiratory function can be achieved. The effects of respiratory function training include: improving lung ventilation; transforming abnormal breathing patterns into normal breathing patterns; increasing the activity of the thoracic cage, etc. For some patients before surgery, breathing training can also help patients improve the effectiveness of coughing; thereby ensuring the recovery of patients after surgery, and avoiding the inability to effectively excrete sputum to affect the recovery of the disease after surgery.

Existing respiratory function training is mainly guided by medical staff, and patients are required to perform regular breathing training, and the training and supervision of the breathing process are completed by the patients themselves. It is impossible to carry out differentiated requirements and training for different types of patients. At the same time, in most cases, patients can only try according to the doctor's advice, and they can't intuitively understand how to do it, and they don't know whether the execution of their own breathing training meets the requirements; these all lead to poor breathing training effects for patients.

Contents of the invention

Based on this, in order to solve the problem that the existing respiratory function training is not effective and patients cannot intuitively control the training process; the present invention provides an intelligent guided training method for respiratory function.

The present invention provides a kind of intelligent guidance training method of breathing function, and this method comprises the following steps:

Step 1: Collect the data of the current user's basic physiological indicators related to respiratory function: basic physiological indicators include: indications, gender, age, height, weight, body fat percentage, blood pressure, average heart rate, and average blood oxygen saturation.

Step 2: Query a breathing training program database according to the real-time monitoring data of various basic physiological indicators of the current user, and obtain the breathing training program that best matches the current user; the pre-designed "basic physiological indicators-breathing training program" is stored in the breathing training program database. Training Program Comparison Table".

Define the multiple exhalation and inhalation actions that need to be completed continuously in the breathing training program as a training task unit; the breathing training program contains at least one training task unit. The index reflecting the execution state of the breathing training program is defined as the target breathing training index. Target breathing training metrics include:

a. The instantaneous air flow of a single exhalation or inhalation action.

b. The total air flow of a single exhalation or inhalation action.

c. The duration of a single exhalation or inhalation.

d. The number of exhalation or inhalation movements in a single training task unit, and the tempo of exhalation and inhalation movements.

Step 3: When the user executes the respiratory training program, obtain the state changes of the respiratory airflow of the user's mouth and nose and the state changes of the respiratory muscles; convert the state changes of the respiratory airflow and respiratory muscles into the current user's real-time respiratory training detection; real-time The detection amount of breathing training is the measured value of the target breathing training index; the detection amount of real-time breathing training is obtained by detecting the change of airflow in the mouth and nose and the state change of respiratory muscles. The calculation method of the detection amount of real-time breathing training is as follows:

X _real = α · X _gas + β · X _muscle

In the above formula, X _actually represents the value of a certain real-time respiratory training detection value; X _gas represents the value of a certain real-time respiratory training detection value calculated by the change of respiratory airflow; X _muscle represents a certain value calculated by the change of respiratory muscles. The value of real-time respiratory training detection; α represents the impact weight of respiratory airflow changes in the current item index on real-time respiratory training detection, and β represents the impact weight of respiratory muscle changes in the current item index on real-time respiratory training detection; where α and β are expert experience values related to various real-time breathing training detection quantities, and α+β=1.

Step 4: Display the requirements and implementation of the breathing training program in an interactive way of graphic display and voice prompts, and guide the user; the specific process is as follows:

(1) All the training task units included in the breathing training program are displayed in the form of the first graphic, and the first graphic reflecting the breathing training program is in the order of the time axis; at least the target breathing training indicators are displayed in the first graphic various target values.

(2) Voice prompts are issued at the start time and end time of the execution of the breathing training program, and when the exhalation action and the inhalation action are switched.

(3) Generate a second graphic for reflecting the execution status of the breathing training program according to the acquired values of various real-time breathing training detection quantities when the user executes the breathing training program. The second graph is in the order of the time axis; the second graph at least reflects the measured values of various real-time breathing training detection quantities in the actual execution state.

(4) Comparing and displaying the first graph and the second graph in graphs, the graph comparison at least reflects the deviation between each target value of the breathing training program and each measured value of the actual execution state.

(5) Calculate the execution completion rate of the breathing training program, and display the execution completion rate through graphic display and/or voice prompt interactive mode.

Further, among the basic physiological indicators, the indication refers to the disease type of the user currently performing breathing training. Indications, gender, and age are collected by manual input, or obtained by collecting the user's recent medical data. Gender data is valid for a long time, and age data is valid for less than one year; among the basic physiological indicators, the values of height, weight, body fat percentage, blood pressure, average heart rate, and average blood oxygen saturation are obtained from the physical examination data actually detected by the user in the near future , and the data is valid for no more than seven days.

Further, the "Basic Physiological Index-Respiratory Training Program Comparison Table" establishes the corresponding relationship between different basic physiological indicators and different breathing training programs; The user matches the best breathing training program; the types of training task units included in the breathing training program include: rapid inhalation and rapid exhalation process, rapid inhalation and slow exhalation process, slow inhalation and rapid exhalation process, slow inhalation and slow exhalation process; in the breathing training program Including any combination of multiple training task units of the same or different types.

Further, in Step 3, the instantaneous air flow of a single exhalation or inhalation action is obtained by comprehensively measuring the state changes of respiratory air flow and respiratory muscles. The total air flow of a single exhalation or inhalation action is also obtained by comprehensively measuring the state changes of respiratory airflow and respiratory muscles. The duration of a single exhalation or inhalation action is only measured by the state change of the respiratory airflow, that is: in this item, the value of α is 1, and the value of β is 0; the exhalation or inhalation action in a single training task unit The quantity of , and the tempo of exhalation and inspiratory action can only be obtained by any one of the changes in respiratory airflow and respiratory muscle state, that is: in this item, α=0, β=1; or α=1, β=0.

Further, the method of calculating the value of a certain real-time breathing training detection value calculated by the change of respiratory airflow is as follows: real-time monitoring of the real-time state changes of the airflow in the user's mouth and nose, and judging according to the direction of the airflow that the current breathing action is breathing. Pneumatic action or inhalation action. And with the duration of the breathing action detected in real time, the airflow pressure of the mouth and nose position detected during the breathing action, and the user's basic physiological indicators as output, the instantaneous breathing action of the current user is output through the neural network-based respiratory volume prediction algorithm The predicted value of the airflow, and the predicted value of the total airflow of the user's current respiratory action based on the instantaneous airflow. The breathing volume prediction algorithm uses the real basic physiological indicators and breathing characteristic data of different users to complete the training process.

Further, the method of calculating the value of a certain real-time breathing training detection value calculated by the state change of the respiratory muscles is as follows: by measuring the state change of the user's respiratory muscles, obtain the pull value of the current user in the waist and abdomen circumference direction during the breathing process; and then Calculate the value of the balance pressure in the abdominal cavity corresponding to the current user during the breathing process; finally establish the corresponding relationship between the change of the balance pressure value of the current user and the inspiratory volume in the chest cavity according to the basic physiological indicators of the current user, and use the detected The state changes of the user's respiratory muscles can be used to obtain the predicted values of the instantaneous air flow and the total air flow of the user's exhalation and inhalation actions.

In one of the embodiments of the present invention, the first graph in step 4 is displayed using a peak diagram with time as the abscissa and the instantaneous airflow of breathing as the ordinate; each waveform in the peak diagram reflects an exhalation action or inhalation action. The waveform reflecting the exhalation action is located in the first quadrant of the coordinates of the peak diagram, and the waveform reflecting the inhalation action is located in the fourth quadrant of the coordinates of the peak diagram. The ordinate of each point on each waveform represents the instantaneous air flow at that moment, the length of the abscissa corresponding to the waveform reflects the duration of the exhalation or inhalation action, and the enclosed area of each waveform and axis reflects the exhalation or inhalation. The total air flow of the pneumatic action. Each training task unit is represented by a continuous peak map on the time axis.

Wherein, the second graph and the first graph are overlapped and displayed according to the same time axis, and the elements of points, lines, and planes in the second graph are displayed in colors different from those of the corresponding elements in the first graph; The process gradually fills in the first graphic, thereby realizing the contrast between the first graphic and the second graphic.

In another embodiment of the present invention, the first graph is displayed above a continuous time axis using a striped bubble graph. Each bubble in the first graph represents an exhalation action or an inhalation action, and the colors of the bubbles reflecting the inhalation action and the exhalation action are different; the area filled inside the bubble represents the total air flow of the inhalation action or exhalation action Size; the duration of the area-filling process in the bubble characterizes the duration of a single exhalation or inhalation. The center of the bubble displays the size of the instantaneous air flow of the current exhalation action or inhalation action through constantly changing numbers. Each training task unit is represented by multiple bubbles arranged consecutively on the time axis.

The second graph and the first graph are displayed on the same time axis; and the second graph is gradually filled below the time axis during the generation process, thereby forming a contrast between the first graph and the second graph.

Further, the calculation method of the execution completion rate in step 4 is as follows:

(i) Obtaining target breathing training indicators and real-time breathing training detection quantities during the execution of the breathing training program in sequence.

(ii) sort the breathing actions in the breathing training program according to the exhalation action and inhalation action respectively, and obtain an exhalation action task queue and an inhalation action task array.

(Ⅲ) Calculate the completion rate of each exhalation action or inhalation action in the exhalation action task queue and inhalation action task queue; then obtain the arithmetic mean completion rate of all exhalation actions in the exhalation action task queue, and The arithmetic average completion rate of all inhalation actions in the task queue; the calculation formula is as follows:

i＝1……n；

i=1...n;

In the above formula, V _exhale means the arithmetic average completion rate of all exhalation actions in the exhalation action task queue; V _inhalation means the arithmetic average completion rate of all inhalation actions in the inhalation action task queue; The number of exhalation actions, or the number of inspiratory actions in the inspiratory task queue; q _{real-time i} represents the real-time breathing training detection amount of the i-th exhalation action or the instantaneous air flow in the inhalation action; q _{item i} means the i-th The value of the target breathing training index of the instantaneous air flow in the exhalation action or the inhalation action; Q _{real-time i} represents the real _- time breathing training detection amount of the total air flow in the ith exhalation action or inhalation action; The value of the target breathing training index of the total gas flow in an exhalation action or an inhalation action; t _{real i} represents the real-time breathing training detection amount of the ith exhalation action or the duration of an inhalation action; t _{item i} represents the ith The value of the target breathing training index of the duration of the exhalation or inhalation action; N _indicates the number of exhalation or inhalation actions actually completed in the current breathing training program, and N _indicates the target breathing training index in the current breathing training program The number of exhalations or inhalations required to be completed.

(ⅳ) Carry out a weighted average of the arithmetic average completion rates of the two parts in the above step to obtain the execution completion rate of the breathing training program. The calculation formula is as follows:

V _hold = (a · V _exhale + b · V _inhale ) · 100%

In the above formula, V _represents the execution completion rate of the current breathing training plan; a represents the influence weight of the completion rate of exhalation action on the execution completion rate of the current breathing training plan; b represents the completion rate of inhalation action on the execution of the current breathing training plan The influence weight of the completion rate, and a+b=1.

Further, the execution completion rate is displayed by a numerical value of a percentage, or in the form of a fractional value of a percentage system.

The intelligent guidance training method of respiratory function provided by the present invention has the following beneficial effects: the present invention collects the current basic physiological indicators of the user, and matches the best physiological index for the user according to the different physiological states and indications of the user. Breathing training plan; the breathing training plan has detailed quantitative index requirements for each stage of the user's breathing training process. Therefore, the solution of the present invention is very targeted and has strong operability.

The present invention comprehensively considers the breathing airflow of the mouth and nose of the user and the expansion and contraction movement of the waist and abdomen to detect the breathing effect of the user in real time during the user's execution of the breathing training program. Since the present invention integrates the change of the airflow and the state change of the respiratory muscles, it is more accurate to obtain the respiratory state data.

At the same time, during the user's execution of the program, the method displays the breathing training program in a visualized form, and compares it with the user's real-time breathing state data, so as to guide the user to perform breathing training. This method is very intuitive especially when the user needs to use different types of breathing methods for breathing training. Moreover, when the user's execution effect is not good, the method of the present invention can also allow the user to find out in time and adjust in time, so as to meet the final training requirement.

Description of drawings

The above and other objects, features and advantages of the present invention will become more apparent through a more specific description of preferred embodiments of the present invention shown in the accompanying drawings.

Fig. 1 is the flow chart of a kind of respiratory function intelligent guidance training method in embodiment 1 of the present invention;

Fig. 2 is the flow chart of the presentation method of the execution process of the breathing training program in Embodiment 1 of the present invention;

FIG. 3 is a flow chart of a calculation method for the execution completion rate of the breathing training program in Embodiment 1 of the present invention.

Detailed ways

The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the present invention.

Example 1

The present embodiment provides a kind of intelligent guidance training method of respiratory function, as shown in Figure 1, the method comprises the following steps:

Step 1: Collect the data of the current user's basic physiological indicators related to respiratory function: basic physiological indicators include: indications, gender, age, height, weight, body fat percentage, blood pressure, average heart rate, and average blood oxygen saturation.

When performing respiratory function training for patients, the physiological state of each user is inconsistent. At the same time, due to the different indications, it is not possible to use the same breathing training requirements for each user, but it must be tailored according to the specific situation of the user. sex training. For example, for some preoperative users, it is mainly hoped that the user can perform high-intensity breathing training, so that the chest cavity can be fully expanded and the respiratory muscles can be fully exercised. Only in this way can effective expectoration be carried out after the operation. For users whose respiratory function or lung function is recovering, the breathing training strategy should be completely different; such customers should first perform low-intensity assisted breathing training, so that the user can achieve spontaneous breathing, and then according to the patient's The self-recovery state gradually increases the intensity of breathing training, and gradually restores the patient's normal breathing function.

The factors proposed in this embodiment that affect various indicators during the breathing training process of the user specifically include indications, gender, age, height, weight, body fat percentage, blood pressure, average heart rate, and average blood oxygen saturation. Among the basic physiological indicators collected in this embodiment, the indication refers to the disease type of the user who is currently performing breathing training. This is the most important factor affecting the user's breathing training metrics. Age and gender are another important indicator. The physiological state of people of different ages is significantly different. At the same time, considering the different physiological conditions of men and women, the gender of users should also be effectively distinguished. Usually, the indication, gender, and age are collected by manual input, or obtained by collecting the user's recent medical data. That is to say, users can directly input or select specific indications, as well as age and gender information. Considering that most users who perform respiratory function training are patients with physical health problems, relevant content can also be filled in through the patient's medical information in the hospital. The gender data is valid for a long time, and the age data is valid for less than one year.

Among other basic physiological indicators, the values of height, weight, body fat percentage, blood pressure, average heart rate, and average blood oxygen saturation are obtained from the physical examination data actually detected by the user recently, and the validity period of the data does not exceed seven days. Height, weight, body fat percentage, blood pressure, average heart rate and average blood oxygen saturation, these indicators also have a great correlation with the user's breathing function, so this embodiment also takes into account the influence of these indicators. Considering that the state of such indicators is easy to change, and at the same time, the difficulty of obtaining data is relatively small, and the acquisition cost is relatively low. In this embodiment, the validity period of the above indicators is set to be relatively short, and at the same time, real-time measurement should be performed before each respiratory function training during use.

Step 2: Query a breathing training program database according to the real-time monitoring data of various basic physiological indicators of the current user, and obtain the breathing training program that best matches the current user; the pre-designed "basic physiological indicators-breathing training program" is stored in the breathing training program database. Training Program Comparison Table".

In this embodiment, the corresponding relationship between different basic physiological indicators and different breathing training programs is established in the "basic physiological indicators-breathing training program comparison table"; the "basic physiological indicators-breathing training program comparison table" is used for different types of basic physiological The user of the indicator matches the optimal breathing training regimen. Respiratory training programs are designed by professional medical staff for different types of patients based on experience. Different basic physiological indicators usually correspond to different breathing training programs. In this embodiment, the "Basic Physiological Indicators-Respiratory Training Program Comparison Table" is a comparison table established based on expert experience; the best breathing training program corresponding to each patient can be found by looking up the table. The number of breathing training programs in the "Basic Physiological Indicators-Breathing Training Program Comparison Table" is limited, usually a dozen to dozens, and the degree of refinement of the breathing training programs is related to the number of types of users who actually apply the method. The wider the scope of application of this method, the higher the degree of refinement of the breathing training program that needs to be designed. Each different breathing training program corresponds to each different data segment of the basic physiological index. And so that users who meet any basic physiological index can get a corresponding breathing training program.

In this embodiment, multiple exhalation and inhalation actions that need to be completed continuously in the breathing training program are defined as a training task unit; the breathing training program includes at least one training task unit.

The types of training task units included in the breathing training program include: rapid inhalation and rapid exhalation process, rapid inhalation and slow exhalation process, slow inhalation and rapid exhalation process, slow inhalation and slow exhalation process; the respiratory training program includes any number of the same or different types A combination of training task units.

The breathing training program actually reflects what kind of breathing strategy the user should adopt. In order to quantify various requirements in the user's breathing training program, this embodiment defines a concept of a target breathing training index. In this embodiment, the index reflecting the execution status of the breathing training program is defined as the target breathing training index. Target breathing training metrics include:

a. The instantaneous air flow of a single exhalation or inhalation action.

b. The total air flow of a single exhalation or inhalation action.

c. The duration of a single exhalation or inhalation.

d. The number of exhalation or inhalation movements in a single training task unit, and the tempo of exhalation and inhalation movements.

A complete breathing training process can be described through the above indicators. Different types of breathing strategies are reflected in the above target breathing training indicators. For example: in the breathing training task of a certain user, it is stipulated that the current user should first breathe in and out quickly for 1 minute, then breathe in and out slowly for 5 minutes, and finally breathe in and out slowly for 10 minutes. In the process of rapid inhalation and exhalation, it is necessary to meet the total air flow of a single exhalation action to reach a certain fixed value, and to specify how many times the user should inhale and exhale within 1 minute, and so on. At the same time, this method can quantify the user's breathing training process.

Step 3: When the user executes the respiratory training program, obtain the state changes of the respiratory airflow of the user's mouth and nose and the state changes of the respiratory muscles; convert the state changes of the respiratory airflow and respiratory muscles into the current user's real-time respiratory training detection; real-time The detection amount of breathing training is the measured value of the target breathing training index; the detection amount of real-time breathing training is obtained by detecting the change of airflow in the mouth and nose and the state change of respiratory muscles. The calculation method of the detection amount of real-time breathing training is as follows:

X _real = α · X _gas + β · X _muscle

In the above formula, X _actually represents the value of a certain real-time respiratory training detection value; X _gas represents the value of a certain real-time respiratory training detection value calculated by the change of respiratory airflow; X _muscle represents a certain value calculated by the change of respiratory muscles. The value of real-time respiratory training detection; α represents the impact weight of respiratory airflow changes in the current item index on real-time respiratory training detection, and β represents the impact weight of respiratory muscle changes in the current item index on real-time respiratory training detection; where α and β are expert experience values related to various real-time breathing training detection quantities, and α+β=1.

In this embodiment, considering that the user's actual breathing state data is difficult to detect, a comprehensive algorithm with higher reliability is used for calculation. The overall calculation idea is: on the one hand, detect the airflow pressure of the user's exhalation and inhalation during the breathing process; and then estimate the air flow of the user during the breathing process according to the user's physiological indicators. On the other hand, it detects the state changes of the respiratory muscles located in the chest and abdomen of the user during the breathing process, and then estimates the air flow of the user during the breathing process according to the state changes of the respiratory muscles. The airflow in this embodiment mainly refers to the instantaneous airflow and the total airflow of a single breathing movement.

In this embodiment, the real-time respiratory training detection quantity includes the instantaneous air flow and total air flow of each breathing action; the duration of a single exhalation or inhalation action, the number of exhalation or inhalation actions in a single training task unit, and the number of exhalation actions. The rhythm of breath and inhalation movements.

Specifically, the instantaneous air flow of a single exhalation or inhalation action is obtained by comprehensively measuring the state changes of respiratory airflow and respiratory muscles. The total air flow of a single exhalation or inhalation action is also obtained by comprehensively measuring the state changes of respiratory airflow and respiratory muscles. The duration of a single exhalation or inhalation action is only measured by the state change of the respiratory airflow, that is: in this item, the value of α is 1, and the value of β is 0; the exhalation or inhalation action in a single training task unit The quantity of , and the tempo of exhalation and inspiratory action can only be obtained by any one of the changes in respiratory airflow and respiratory muscle state, that is: in this item, α=0, β=1; or α=1, β=0.

In this embodiment, the method of calculating the value of a certain real-time respiratory training detection value through the change of respiratory airflow is as follows: monitor the real-time state changes of the airflow in the user's mouth and nose in real time, and judge the current breathing action according to the direction of the airflow Exhalation or inhalation. And with the duration of the breathing action detected in real time, the airflow pressure of the mouth and nose position detected during the breathing action, and the user's basic physiological indicators as output, the instantaneous breathing action of the current user is output through the neural network-based respiratory volume prediction algorithm The predicted value of the airflow, and the predicted value of the total airflow of the user's current respiratory action based on the instantaneous airflow. The breathing volume prediction algorithm uses the real basic physiological indicators and breathing characteristic data of different users to complete the training process.

The calculation of the user's real respiratory airflow through airflow measurement needs to meet relatively high measurement conditions. For example, it may need to be completed in a closed airflow measurement environment to ensure that the respiratory airflow does not dissipate. This is inconsistent with the requirement of real-time measurement during the breathing training process in this embodiment, and it is difficult to directly realize the measurement. Therefore, in this embodiment, a trained neural network-based breathing volume prediction algorithm is used to estimate the instantaneous air volume. Considering that the most relevant factor to the airflow of the breathing action is the airflow pressure of the user's mouth and nose during the breathing process, this embodiment uses this amount as the input of the breathing volume prediction algorithm, and uses the user's basic physiological indicators as the Input, and then obtain the predicted value of the instantaneous air flow of the user's breathing process, based on the instantaneous air flow, the total air flow of the user's single breathing action can be obtained. Since the respiratory volume prediction algorithm is trained by using the real user's breathing action state data, the reliability of the predicted value obtained in this embodiment is relatively high.

On the other hand, this embodiment also calculates the state data of the user's breathing action through the state change of the respiratory muscles. Measure and obtain the pull value of the current user in the waist and abdomen circumference direction during the breathing process; then calculate the corresponding intra-abdominal equilibrium pressure value of the current user during the breathing process; finally establish the balance pressure value of the current user according to the current user's basic physiological indicators The corresponding relationship between the change of the value and the inspiratory volume in the chest cavity, and the predicted value of the instantaneous air flow and the total air flow of the user's exhalation action and inhalation action are obtained by using the detected state change of the user's respiratory muscles.

In this embodiment, the idea of this method is to measure the state changes of the user's respiratory muscles in different breathing states to obtain the pull value in the circumferential direction of the user's waist and abdomen; this value is related to the value of the equilibrium pressure in the user's abdominal cavity Yes, an approximate model of this correlation can be obtained through a large amount of data. After the approximate model is established, the user's expiratory volume or inhalation volume is calculated according to the change of the equilibrium pressure in the abdominal cavity of different users, so that the state data of the user's breathing process required in this embodiment can be obtained.

Step 4: Display the requirements and implementation of the breathing training program in an interactive way of graphic display and voice prompts, and guide the user; as shown in Figure 2, the specific process is as follows:

(1) All the training task units included in the breathing training program are displayed in the form of the first graphic, and the first graphic reflecting the breathing training program is in the order of the time axis; at least the target breathing training indicators are displayed in the first graphic various target values.

(2) Voice prompts are issued at the start time and end time of the execution of the breathing training program, and when the exhalation action and the inhalation action are switched.

(3) Generate a second graphic for reflecting the execution status of the breathing training program according to the acquired values of various real-time breathing training detection quantities when the user executes the breathing training program. The second graph is in the order of the time axis; the second graph at least reflects the measured values of various real-time breathing training detection quantities in the actual execution state.

(4) Comparing and displaying the first graph and the second graph in graphs, the graph comparison at least reflects the deviation between each target value of the breathing training program and each measured value of the actual execution state.

(5) Calculate the execution completion rate of the breathing training program, and display the execution completion rate through graphic display and/or voice prompt interactive mode. The execution completion rate can be displayed as a percentage value; it can also be displayed as a percentage value.

As shown in Figure 3, the calculation method of the execution completion rate in step 4 is as follows:

(i) Obtaining target breathing training indicators and real-time breathing training detection quantities during the execution of the breathing training program in sequence.

(ii) sort the breathing actions in the breathing training program according to the exhalation action and inhalation action respectively, and obtain an exhalation action task queue and an inhalation action task array.

(Ⅲ) Calculate the completion rate of each exhalation action or inhalation action in the exhalation action task queue and inhalation action task queue; then obtain the arithmetic mean completion rate of all exhalation actions in the exhalation action task queue, and The arithmetic average completion rate of all inhalation actions in the task queue; the calculation formula is as follows:

i＝1……n；

i=1...n;

In the above formula, V _exhale means the arithmetic average completion rate of all exhalation actions in the exhalation action task queue; V _inhalation means the arithmetic average completion rate of all inhalation actions in the inhalation action task queue; The number of exhalation actions, or the number of inspiratory actions in the inspiratory task queue; q _{real-time i} represents the real-time breathing training detection amount of the i-th exhalation action or the instantaneous air flow in the inhalation action; q _{item i} means the i-th The value of the target breathing training index of the instantaneous air flow in the exhalation action or the inhalation action; Q _{real-time i} represents the real _- time breathing training detection amount of the total air flow in the ith exhalation action or inhalation action; The value of the target breathing training index of the total gas flow in an exhalation action or an inhalation action; t _{real i} represents the real-time breathing training detection amount of the ith exhalation action or the duration of an inhalation action; t _{item i} represents the ith The value of the target breathing training index of the duration of the exhalation or inhalation action; N _indicates the number of exhalation or inhalation actions actually completed in the current breathing training program, and N _indicates the target breathing training index in the current breathing training program The number of exhalations or inhalations required to be completed.

(ⅳ) Carry out a weighted average of the arithmetic average completion rates of the two parts in the above step to obtain the execution completion rate of the breathing training program. The calculation formula is as follows:

V _hold = (a · V _exhale + b · V _inhale ) · 100%

In the above formula, V _represents the execution completion rate of the current breathing training plan; a represents the influence weight of the completion rate of exhalation action on the execution completion rate of the current breathing training plan; b represents the completion rate of inhalation action on the execution of the current breathing training plan The influence weight of the completion rate, and a+b=1.

In this step, it is mainly to detect the relationship between the real-time breathing state data of the user and the target breathing training index in the breathing training program, and to judge whether the user's breathing training process meets the index requirements of the breathing training program.

One of the cores of this embodiment is to intuitively display the user's real-time breathing state data and target breathing training indicators through the interactive mode of graphic display and voice prompts. While watching the breathing training program that should be executed currently, the user checks whether his own breathing training process meets the requirements, and adjusts his own breathing training process in time. This training method actually achieves a special kind of feedback; the user observes the implementation of his own program in real time, and adjusts the breathing process in time when there is a deviation, so as to finally achieve the goal of precise execution according to the breathing training program.

This interactive mode of graphic display and voice prompts has played a very good guiding effect, ensuring that the user's breathing training program can be better executed. In the following content, this embodiment will illustrate how to present the process of breathing training through graphic interaction in two ways.

In one of the implementations of this embodiment, the first graph in step 4 is displayed using a peak diagram with time as the abscissa and the instantaneous air flow of the breath as the ordinate; each waveform in the peak diagram reflects an exhalation action or inhalation action. The waveform reflecting the exhalation action is located in the first quadrant of the coordinates of the peak diagram, and the waveform reflecting the inhalation action is located in the fourth quadrant of the coordinates of the peak diagram. The ordinate of each point on each waveform represents the instantaneous air flow at that moment, the length of the abscissa corresponding to the waveform reflects the duration of the exhalation or inhalation action, and the enclosed area of each waveform and axis reflects the exhalation or inhalation. The total air flow of the pneumatic action. Each training task unit is represented by a continuous peak map on the time axis.

Wherein, the second graph and the first graph are overlapped and displayed according to the same time axis, and the elements of points, lines, and planes in the second graph are displayed in colors different from those of the corresponding elements in the first graph; The process gradually fills in the first graphic, thereby realizing the contrast between the first graphic and the second graphic.

Specifically, the graphic display scheme in this embodiment is similar to an electrocardiogram. According to the situation reflected in the first graphic, the user observes the breathing training target that needs to be achieved, and then performs the inhalation and exhalation actions at the appropriate moment according to the display process of the image. When the user performs the exhalation action and inhalation action, according to the second The overlapping of the waveform in the graph and the waveform of the first graph can intuitively know whether the current breathing intensity meets the requirement. If the requirements are not met, the breathing intensity can be adjusted the next time the same action is performed, and the rhythm and switching frequency of the breathing action can also be adjusted according to the results displayed in the first and second graphics. Correspondingly, the user can not only observe the completion effect of the completed actions, but also adjust the subsequent actions to achieve negative feedback adjustment. It is also possible to know the index requirements of the subsequent breathing actions to be performed according to the first graphic, that is, the first graphic has the effect of guiding the user to perform breathing training.

In another implementation of this embodiment, the first graph may also be displayed above a continuous time axis using a striped bubble graph. Each bubble in the first graph represents an exhalation action or an inhalation action, and the colors of the bubbles reflecting the inhalation action and the exhalation action are different; the area filled inside the bubble represents the total air flow of the inhalation action or exhalation action Size; the duration of the area-filling process in the bubble characterizes the duration of a single exhalation or inhalation. The center of the bubble displays the size of the instantaneous air flow of the current exhalation action or inhalation action through constantly changing numbers. Each training task unit is represented by multiple bubbles arranged consecutively on the time axis.

The second graph and the first graph are displayed on the same time axis; and the second graph is gradually filled below the time axis during the generation process, thereby forming a contrast between the first graph and the second graph.

In this program, during the execution of the breathing training program, the user can determine whether to exhale or inhale by observing the color of the bubbles, and determine the intensity of breathing according to the size of the bubbles, and determine a single breathing action according to the length of the bubbles on the time axis duration of . At the same time, by observing the difference between the bubbles generated in the second graphic and the bubbles in the first graphic, the gap between the currently executed breathing process and the target breathing scheme can be determined, thereby achieving the purpose of guiding the user to breathe.

The above are only preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Any equivalent structure or equivalent process transformation made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in other related technical fields , are all equally included within the protection scope of the present invention.

Claims (10)

1. An intelligent guidance training method for respiratory function, which is characterized by comprising the following steps:

Step one: collecting data of basic physiological indexes related to respiratory functions of a current user: the basic physiological index comprises: indication, gender, age, height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation;

step two: inquiring a breath training scheme database according to the real-time monitoring data of each basic physiological index of the current user to obtain a breath training scheme which is most matched with the current user; the respiratory training scheme database stores a pre-designed basic physiological index-respiratory training scheme comparison table;

defining a plurality of exhalations and inhalations which need to be continuously completed in the respiratory training scheme as a training task unit; the respiratory training scheme comprises at least one training task unit; defining an index reflecting an execution state of the breath training scheme as a target breath training index, the target breath training index comprising:

a. instantaneous airflow for a single exhalation or inhalation event;

b. total airflow for a single exhalation or inhalation event;

c. duration of a single exhalation or inhalation event;

d. the number of exhalations or inspiration actions in a single training task unit, and the beats of exhalations and inspiration actions;

Step three: when a user executes the respiratory training scheme, acquiring the state change of respiratory airflow of the mouth and nose of the user and the state change of respiratory muscles; converting the state changes of respiratory airflow and respiratory muscles into real-time respiratory training detection amounts of the current user; the real-time breath training detection amount is an actual measurement value of the target breath training index; the real-time breath training detection amount is obtained through detecting the airflow change of the mouth and the nose and the state change of the respiratory muscle, and the calculation method of the real-time breath training detection amount is as follows:

X _{real world} ＝α·X _{Air flow} +β·X _Muscle

In the above, X _{Real world} A value representing an amount of said real-time breath training test; x is X _{Air flow} A value representing the detected amount of real-time respiratory training calculated from the change in respiratory airflow; x is X _Muscle A value representing the detected amount of the real-time respiratory training calculated from the change of respiratory muscle; alpha represents the influence weight of the respiratory airflow variation in the current index on the real-time respiratory training detection amount, and beta represents the influence weight of the respiratory muscle variation in the current index on the real-time respiratory training detection amount; wherein α and β are expert empirical values related to the real-time breath training test amounts described in each item, and α+β=1;

Step four: the requirements and the execution conditions of the respiratory training scheme are displayed in an interactive mode of graphic display and voice prompt, and the user is guided; the specific process is as follows:

(1) Displaying all training task units contained in the respiratory training scheme in a first graph mode, wherein the first graph reflecting the respiratory training scheme is in sequence of a time axis; at least displaying each target value in the target respiratory training indexes in the first graph;

(2) Sending out voice prompts at the starting time and the ending time of the execution of the respiratory training scheme and the moment of switching between the expiration action and the inspiration action;

(3) Generating a second graph for reflecting the executing state of the breathing training scheme according to the acquired values of the real-time breathing training detection amounts when the user executes the breathing training scheme; the second graph is in sequence of a time axis; at least reflecting actual measurement values of each real-time breath training detection quantity in an actual execution state in the second graph;

(4) Comparing and displaying the first graph and the second graph, wherein the graph comparison at least reflects the deviation between each target value of the respiratory training scheme and each actual measured value of the actual execution state;

(5) And calculating the execution completion rate of the respiratory training scheme, and displaying the execution completion rate in an interactive mode of graphic display and/or voice prompt.

2. The method for intelligent guided training of respiratory function of claim 1, wherein: in the basic physiological indexes, the indication refers to the disease type of the user currently executing respiratory training; the indication, sex and age are acquired by manual input or acquired by acquiring recent medical data of the user; the validity period of the data of the gender is long, and the validity period of the data of the age is less than one year; in the basic physiological index, the values of height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation are obtained through physical examination data actually detected recently by a user, and the validity period of the data does not exceed seven days.

3. The method for intelligent guided training of respiratory function of claim 2, wherein: the basic physiological index-respiration training scheme comparison table is provided with corresponding relations between different basic physiological indexes and different respiration training schemes; the basic physiological index-respiratory training scheme comparison table is used for matching the optimal respiratory training scheme aiming at users with different types of basic physiological indexes; types of the training task elements included in the breath training scheme include: a rapid inhalation and rapid respiration process, a rapid inhalation and slow respiration process, a slow inhalation and rapid respiration process and a slow inhalation and slow respiration process; the respiratory training scheme includes any combination of a plurality of the training task elements of the same or different types.

4. A method of intelligent guided training of respiratory function according to claim 3, wherein: in the third step, the instantaneous air flow of single expiration or inspiration action is obtained by comprehensively measuring and calculating the state changes of respiratory air flow and respiratory muscles; the total air flow of single exhalation or inhalation action is also obtained by comprehensively measuring the state changes of respiratory air flow and respiratory muscles; the duration of a single exhalation or inhalation event is measured solely by the change in state of the respiratory airflow, namely: the value of alpha is 1, and the value of beta is 0 in the term; the number of exhalation or inhalation movements in a single training task unit, and the beats of exhalation and inhalation movements are obtained only by any one of the respiratory airflow and the respiratory muscle state changes, namely: in this term α=0, β=1; or α=1, β=0.

5. The intelligent guided training method of respiratory function of claim 4, wherein: the method for calculating the value of the real-time breath training detection value according to the variation of the breath air flow is as follows: monitoring the real-time state change of the air flow of the mouth and the nose of the user in real time, and judging the current breathing action as the exhalation action or the inhalation action according to the direction of the air flow; outputting the predicted value of the instantaneous air flow of the current user breathing action by using the duration of the breathing action detected in real time, the air flow pressure of the mouth and nose positions detected during the breathing action and the basic physiological index of the user as output, and counting the predicted value of the total air flow of the current breathing action by using the predicted value of the instantaneous air flow by using a breathing quantity prediction algorithm based on a neural network; the respiratory quantity prediction algorithm adopts real basic physiological indexes of different users and characteristic data of respiration to complete the training process.

6. The method for intelligent guided training of respiratory function of claim 5, wherein: the method for calculating the value of the real-time breath training detection quantity according to the state change of the respiratory muscle comprises the following steps: the method comprises the steps of obtaining a tension value of a current user in the circumferential direction of the waist and abdomen in the breathing process through measuring the state change of respiratory muscles of the user; further calculating the corresponding value of the balance pressure intensity in the abdominal cavity of the current user in the breathing process; and finally, establishing a corresponding relation between the change of the value of the balance pressure of the current user and the inhalation amount in the chest according to the basic physiological index of the current user, and obtaining predicted values of the instantaneous air flow and the total air flow of the exhalation action and the inhalation action of the user by utilizing the detected state change of the respiratory muscles of the user.

7. The method for intelligent guided training of respiratory function of claim 6, wherein: the first graph is displayed by adopting a peak graph taking time as an abscissa and taking instantaneous respiratory airflow as an ordinate; each waveform in the peak map reflects one of an expiratory motion or an inspiratory motion; the waveform reflecting the exhalation action is positioned in a first quadrant of coordinates where the peak graph is positioned, and the waveform reflecting the inhalation action is positioned in a fourth quadrant of coordinates where the peak graph is positioned; the ordinate of each point on each waveform represents the instantaneous air flow at the current moment, the length of the abscissa corresponding to the waveform reflects the duration of the exhalation or inhalation action, and the enclosing area of each waveform and the axis reflects the total air flow of the exhalation or inhalation action; each training task unit is characterized by a continuous peak graph on a time axis;

The second graph and the first graph are displayed in an overlapping mode according to the same time axis, and elements of points, lines and faces in the second graph are displayed in different colors from corresponding elements in the first graph; the second graph is gradually filled on the first graph along with the generation process, and then the comparison between the first graph and the second graph is realized.

8. The method for intelligent guided training of respiratory function of claim 6, wherein: the first graph is displayed above a continuous time axis by using a strip-shaped bubble chart; each bubble in the first graph represents one expiration action or inspiration action, and the colors of the bubbles reflecting the inspiration action and expiration action are different; the area filled in the bubbles represents the total air flow of the inspiration action or the expiration action; the duration of the area filling process in the bubble characterizes the duration of a single expiratory or inspiratory action; the center of the bubble displays the current instantaneous air flow of the expiration action or inspiration action through the continuously changing number; each training task unit is characterized by a plurality of bubbles which are arranged continuously on a time axis;

The second graph and the first graph are displayed on the same time axis; and the second graph is gradually filled below the time axis along with the generation process, so that the comparison between the first graph and the second graph is formed.

9. The intelligent guided training method of respiratory function of claim 7 or 8, wherein: the execution completion rate calculating method comprises the following steps:

sequentially acquiring target breath training indexes and real-time breath training detection values in the executing process of a breath training scheme;

(ii) sequencing the respiratory actions in the respiratory training scheme according to the expiratory actions and the inspiratory actions respectively to obtain an expiratory action task queue and an inspiratory action task array;

(iii) calculating the respective expiration or inspiration completion rates of the expiration and inspiration task queues; obtaining the arithmetic average completion rate of all the expiratory actions in the expiratory action task queue and the arithmetic average completion rate of all the inspiratory actions in the inspiratory action task queue; the calculation formula is as follows:

in the above, V _{Calling a call} Representing an arithmetic average completion rate of all expiratory actions in the expiratory action task queue; v (V) _{Suction pipe} Representing the arithmetic average completion rate of all the inspiratory actions in the inspiratory action task queue; n represents the number of expiratory actions in the expiratory task queue, or the number of inspiratory actions in the inspiratory task queue; q _{Real i} A real-time respiratory training measurement representing the instantaneous airflow in the ith exhalation or inhalation event; q _{Mesh i} A value of a target respiratory training indicator representing an instantaneous airflow in an ith exhalation or inhalation event; q (Q) _{Real i} A real-time respiratory training measurement representing the total airflow in the ith exhalation or inhalation event; q (Q) _{Mesh i} A value of a target respiratory training index representing a total airflow in an ith exhalation or inhalation event; t is t _{Real i} A real-time breath training test amount representing an ith expiratory motion or inspiratory motion duration; t is t _{Mesh i} A value of a target respiratory training index representing an ith expiratory motion or inspiratory motion duration; n (N) _{Real world} Representing the number of actually completed exhale or inhale events in the current breath training protocol, N _{Order of (A)} Representing the number of exhalation actions or inhalation actions required to be completed by a target respiratory training index in the current respiratory training scheme;

(iv) carrying out weighted average on the arithmetic average completion rate of the two parts in the step, so as to obtain the execution completion rate of the respiratory training scheme, wherein the calculation formula is as follows:

V _Executing ＝(a·V _{Calling a call} +b·V _{Suction pipe} )·100％

In the above, V _Executing Representing the execution completion rate of the current breath training regimen; a represents the weight of the expiration completion rate to the execution completion rate of the current respiratory training scheme, b represents the inspiration completion rate to the current respiratory training schemeThe influence weight of the completion rate is performed, and a+b=1.

10. The method for intelligent guided training of respiratory function of claim 1, wherein: the execution completion rate is shown in the form of a percentage value or a percentage score value.

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