CN113555082B - Intelligent guiding training method for respiratory function - Google Patents
Intelligent guiding training method for respiratory function Download PDFInfo
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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
Technical Field
The invention relates to the field of big data processing, in particular to an intelligent guiding training method for respiratory functions.
Background
Respiratory function training is a routine rehabilitation approach for doctors for patients with certain pulmonary diseases or respiratory dysfunction; the aim of improving or recovering the respiratory function of the patient can be achieved through regular respiratory function training. The respiratory function training functions include: improving lung ventilation; converting the abnormal breathing pattern to a normal breathing pattern; increase the activity of the thorax, etc. For some preoperative patients, respiratory training can also help patients improve the effectiveness of coughing; thereby ensuring the rehabilitation effect of the postoperative patient and avoiding the influence of the postoperative patient on the rehabilitation of diseases due to the failure of effective sputum excretion.
The prior respiratory function training is mainly guided by medical staff, the patient is required to perform regular respiratory training, and the training and supervision of the respiratory process are automatically completed by the patient. The differentiation requirements and training cannot be performed for different types of patients. Meanwhile, the patient can only try according to the doctor's advice under most conditions, and cannot intuitively know how to do so, and whether the execution condition of own breathing training meets the requirement or not; these all result in poor respiratory training of the patient.
Disclosure of Invention
Based on the above, in order to solve the problem that the existing respiratory function training effect is poor, a patient cannot intuitively control the training process; the invention provides an intelligent guiding training method for respiratory function.
The invention provides an intelligent guiding training method for respiratory function, which comprises the following steps:
step one: collecting data of basic physiological indexes related to respiratory functions of a current user: the basic physiological indexes include: 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 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; the respiration training scheme database stores a predesigned basic physiological index-respiration training scheme comparison table.
Defining a plurality of exhalations and inhalations which need to be continuously completed in a respiration training scheme as a training task unit; the respiratory training scheme includes at least one training task unit. An index reflecting the execution state of the respiratory training scheme is defined as a target respiratory training index. The target respiratory training index comprises:
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 exhalation or inhalation events in a single training task element, and the beats of the exhalation and inhalation events.
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; the real-time breath training detection amount is an actual measurement value of a target breath training index; the real-time breath training detection quantity is obtained through detecting the mouth-nose airflow change and the respiratory muscle state change, and the calculation method of the real-time breath training detection quantity is as follows:
X real world ＝α·X Air flow +β·X Muscle
In the above, X Real world A value representing a measure of real-time respiratory training; x is X Air flow A value representing a measure of a real-time respiratory training calculated from the change in respiratory airflow; x is X Muscle A value representing a measure of a real-time respiratory training calculated from the change in 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; where α and β are expert empirical values associated with each real-time breath training test amount, and α+β=1.
Step four: the requirements and the execution conditions of the breathing 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 breathing training scheme in a first graph mode, wherein the first graph reflecting the breathing training scheme is ordered by a time axis; at least each target value in the target respiratory training index is displayed in the first graph.
(2) And sending out voice prompts at the starting time and the ending time of the execution of the respiratory training scheme and the time when the expiration action and the inspiration action are switched.
(3) And generating a second graph for reflecting the execution state of the breathing training scheme according to the acquired values of each real-time breathing training detection quantity when the user executes the breathing training scheme. The second graph is ordered by a time axis; the second graph at least reflects actual measurement values of each real-time breath training measurement value in the actual execution state.
(4) And comparing and displaying the first graph and the second graph in a graph mode, wherein the graph comparison at least reflects 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 breathing training scheme, and displaying the execution completion rate in an interactive mode of graphic display and/or voice prompt.
Further, in the basic physiological index, the indication refers to the disease type of the user currently performing 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 the height, the weight, the body fat rate, the blood pressure, the average heart rate and the average blood oxygen saturation are obtained through physical examination data which are actually detected by a user recently, and the valid period of the data does not exceed seven days.
Further, the corresponding relation between different basic physiological indexes and different respiration training schemes is established in a basic physiological index-respiration training scheme comparison table; the basic physiological index-respiration training scheme comparison table is used for matching the best respiration training scheme for users with different types of basic physiological indexes; types of training task elements included in the respiratory 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 regimen includes any combination of a plurality of training task elements of the same or different types.
Further, in the third step, the instantaneous air flow of a single exhalation or inhalation action is obtained by comprehensively measuring the state changes of the respiratory air flow and the respiratory muscles. The total airflow of a single exhalation or inhalation motion is also obtained by comprehensively measuring the state changes of respiratory airflow 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: in this term, α has a value of 1 and β has a value of 0; 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.
Further, the method for calculating the value of a certain real-time breath training detection value through the change of the breath flow is as follows: the real-time state change of the airflow of the mouth and the nose of the user is monitored in real time, and the current breathing action is judged to be the exhalation action or the inhalation action according to the direction of the airflow. And 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 user breathing action based on 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.
Further, the method for calculating the value of a certain real-time breath training detection value through the state change of the respiratory muscle is as follows: 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.
In one embodiment of the present invention, the first graph of the fourth step is displayed with a peak graph with time as the abscissa and instantaneous respiratory airflow as the ordinate; each waveform in the peak map reflects one expiratory or inspiratory action. The waveform reflecting the exhalation action is located in the first quadrant of the coordinates of the peak map, and the waveform reflecting the inhalation action is located in the fourth quadrant of the coordinates of the peak map. The ordinate of each point on each waveform represents the instantaneous airflow at that moment, the length of the abscissa corresponding to the waveform reflects the duration of the exhalation or inhalation, and the area enclosed by each waveform and the axis reflects the total airflow of the exhalation or inhalation. 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, so that the comparison between the first graph and the second graph is realized.
In another embodiment of the invention, the first pattern is displayed above a continuous time axis using a striped bubble pattern. Each bubble in the first graph represents one breathing action or breathing action, and the colors of the bubbles reflecting the breathing action and the breathing action are different; the area filled inside the bubble characterizes the size of the total air flow of the inspiration or expiration action; the duration of the area filling process in the bubble characterizes the duration of a single exhalation or inhalation motion. The center of the bubble displays the current expiratory or inspiratory instantaneous airflow magnitude by means of a continuously changing number. Each training task element is characterized by a plurality of bubbles arranged in succession 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.
Further, the method for calculating the execution completion rate in the fourth step is as follows:
sequentially acquiring target breath training indexes and real-time breath training detection values in the implementation process of the breath training scheme.
(ii) sequencing the respiratory motion in the respiratory training scheme according to the respiratory motion and the inhalation motion respectively to obtain an exhalation motion task queue and an inhalation motion task array.
(iii) calculating the completion rate of each expiratory motion or inspiratory motion of the expiratory motion task queue and the inspiratory motion task queue; obtaining the arithmetic average completion rate of all the expiration actions in the expiration action task queue and the arithmetic average completion rate of all the inspiration actions in the inspiration 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 that the target respiratory training indicator requires to be completed in the current respiratory training regimen.
(iv) carrying out weighted average on the arithmetic average completion rate of the two parts in the step 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 influence weight of the expiration completion rate on the execution completion rate of the current respiratory training scheme, b represents the influence weight of the inspiration completion rate on the execution completion rate of the current respiratory training scheme, and a+b=1.
Further, the execution completion rate is shown in the form of a percentage value or a percentage score value.
The intelligent guiding training method for the respiratory function has the following beneficial effects: the invention collects the current basic physiological indexes of the user, and matches the optimal respiratory training scheme for the user according to the physiological state and the different indications of the user; the respiration training scheme has detailed quantized index requirements for each stage in the respiration training process of the user. Therefore, the scheme provided by the invention has high pertinence and strong operability.
In the process of executing the breathing training scheme by the user, the breathing effect of the user is detected in real time by comprehensively considering the mouth-nose breathing airflow and the expansion and contraction movements of the waist and the abdomen of the user. The invention integrates the change of air flow and the state change of respiratory muscles, so that the obtained respiratory state data is more accurate.
Meanwhile, in the process of executing the scheme by the user, the method displays the breathing training scheme in a visual form and compares the breathing training scheme with the breathing state data of the user in real time, so that the purpose of guiding the user to perform breathing training is achieved. This approach is very intuitive, especially when the user needs to take different types of breathing patterns for respiratory training. And when the user has poor execution effect, the method can also enable the user to find out and adjust in time, thereby meeting the final training requirement.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention, as illustrated in the accompanying drawings.
FIG. 1 is a flow chart of a method for intelligent guidance training of respiratory function in embodiment 1 of the present invention;
FIG. 2 is a flow chart of a method for presenting the execution of the respiratory training scheme in embodiment 1 of the present invention;
fig. 3 is a flowchart of a method for calculating the execution completion rate of the respiratory training scheme in embodiment 1 of the present invention.
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and specific embodiments so that those skilled in the art may better understand the present invention and practice it, but the examples are not to be construed as limiting the present invention.
Example 1
The embodiment provides an intelligent guiding training method for respiratory function, as shown in fig. 1, comprising the following steps:
step one: collecting data of basic physiological indexes related to respiratory functions of a current user: the basic physiological indexes include: indication, gender, age, height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation.
When the respiratory function training is carried out on patients, the physiological states of all users are inconsistent, and meanwhile, due to different indications, the same respiratory training requirements cannot be adopted for all users, and the targeted training is carried out according to the specific situations of the users. For example, for some preoperative users, it is primarily desirable for the user to be able to perform a high level of respiratory training, so that the chest cavity is fully expanded and the respiratory muscles are fully exercised. So that the sputum can be effectively discharged after the operation. The respiratory training strategy to be adopted is completely different for the user with the respiratory function or the lung function being damaged in the recovery stage; the clients should perform low-intensity auxiliary respiratory training first, so that the users can realize spontaneous breathing, and then gradually increase the intensity of respiratory training according to the recovery state of the patients, and gradually recover the normal respiratory function of the patients.
Factors influencing various indexes in the respiratory training process of the user, which are proposed in the embodiment, specifically include indication, gender, age, height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation. Among the basic physiological indexes collected in this embodiment, the indication refers to the disease type of the user currently performing respiratory training. This is the most important factor affecting the respiratory training index of the user. The age and sex are another important index, and the physiological states of people of different ages have obvious differences, and the sex of users should be effectively distinguished in consideration of different physiological conditions of men and women. Usually, the indication, sex and age are acquired by manual input, or acquired by acquiring recent medical data of the user. That is, the user can directly input or select specific indication, and information of age and sex when using the device. Considering that users who perform respiratory function training belong to patients with physical health problems, relevant contents can be filled through the treatment information of the patients in hospitals. 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 other basic physiological indexes, the values of height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation are obtained through physical examination data which are actually detected by a user recently, and the validity period of the data does not exceed seven days. Height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation, which are also of great relevance to the respiratory function of the user, the present embodiment also takes into account the influence of these indicators. The condition of the index is considered to be easy to change, meanwhile, the data acquisition difficulty is relatively small, and the acquisition cost is low. In this embodiment, the validity period of the above-mentioned index is set to be relatively short, and in the use process, real-time measurement is performed as much as possible before each respiratory function training.
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; the respiration training scheme database stores a predesigned basic physiological index-respiration training scheme comparison table.
In this embodiment, the "basic physiological index-respiration training scheme comparison table" establishes the correspondence between different basic physiological indexes and different respiration training schemes; the "basic physiological index-respiratory training scheme comparison table" is used to match the optimal respiratory training scheme for users of different types of basic physiological indexes. Respiratory training protocols are protocols that specialized medical personnel empirically design for different types of patients, and different basic physiological indices generally correspond to different respiratory training protocols. In this embodiment, the "basic physiological index-respiratory training scheme comparison table" is a comparison table established according to expert experience; the optimal respiratory training scheme corresponding to each patient can be found through table look-up. The number of the breathing training schemes in the basic physiological index-breathing training scheme comparison table is limited, usually tens to tens, the refinement degree of the breathing training schemes is related to the number of types of users who actually apply the method, and the more the range of application of the method is, the higher the refinement degree of the breathing training schemes which need to be designed is. Each different respiratory training regimen corresponds to a data segment of each different underlying physiological index. And the user meeting any basic physiological index can obtain a corresponding respiration training scheme.
In this embodiment, defining a plurality of exhalations and inhalations to be continuously performed in the respiratory training scheme as a training task unit; the respiratory training scheme includes at least one training task unit.
Types of training task elements included in the respiratory 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 regimen includes any combination of a plurality of training task elements of the same or different types.
The breathing training scheme is actually a strategy that reflects what the user should take to breathe. In order to quantify various requirements in the respiratory training scheme of the user, the embodiment defines a concept of a target respiratory training index. The present embodiment defines an index reflecting the execution state of the respiratory training scheme as a target respiratory training index. The target respiratory training index comprises:
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 exhalation or inhalation events in a single training task element, and the beats of the exhalation and inhalation events.
Through the index, a complete respiratory training process can be described. Different types of breathing strategies are reflected by the differences in the target respiratory training indicators described above. For example: in the respiratory training task of a certain user, the current user should breathe for 1min by adopting a mode of urgent inhalation and urgent inhalation, then inhale for 5min slowly, and inhale for 10min slowly at last. During a sudden inhalation, the total airflow required to meet a single exhalation event reaches a certain fixed value, and specifies how many times the user should inhale, exhale, etc. within 1 minute. At the same time, this approach allows the user's respiratory training process to be quantified.
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; the real-time breath training detection amount is an actual measurement value of a target breath training index; the real-time breath training detection quantity is obtained through detecting the mouth-nose airflow change and the respiratory muscle state change, and the calculation method of the real-time breath training detection quantity is as follows:
X real world ＝α·X Air flow +β·X Muscle
In the above, X Real world A value representing a measure of real-time respiratory training; x is X Air flow A value representing a measure of a real-time respiratory training calculated from the change in respiratory airflow; x is X Muscle A value representing a measure of a real-time respiratory training calculated from the change in 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; where α and β are expert empirical values associated with each real-time breath training test amount, and α+β=1.
The present embodiment considers that the state data of the actual breath of the user is difficult to detect, and therefore uses a more highly reliable comprehensive algorithm for calculation. The overall measuring and calculating thought is as follows: on the one hand, detecting the air flow pressure of expiration and inspiration of the user in the breathing process; and then the air flow in the breathing process of the user is estimated according to the physiological index of the user. On the other hand, the state change of the respiratory muscle of the user in the chest and abdomen in the breathing process is detected, and then the air flow in the breathing process of the user is estimated according to the state change of the respiratory muscle. The airflow in this embodiment refers primarily to the instantaneous airflow and total airflow for a single respiratory effort.
In this embodiment, the real-time respiratory training detection amounts include instantaneous airflow and total airflow of each respiratory action; the duration of a single exhalation or inhalation maneuver, the number of exhalation or inhalation maneuvers in a single training task unit, and the beats of the exhalation and inhalation maneuvers.
Specifically, the instantaneous airflow for a single exhalation or inhalation event is obtained by comprehensively measuring the changes in state of respiratory airflow and respiratory muscles. The total airflow of a single exhalation or inhalation motion is also obtained by comprehensively measuring the state changes of respiratory airflow 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: in this term, α has a value of 1 and β has a value of 0; 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.
In this embodiment, the method for calculating the value of a certain real-time breath training measurement value through the variation of the breath flow is as follows: the real-time state change of the airflow of the mouth and the nose of the user is monitored in real time, and the current breathing action is judged to be the exhalation action or the inhalation action according to the direction of the airflow. And 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 user breathing action based on 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.
The measurement conditions to be met by the flow measurement of the actual respiratory airflow of the user are high, for example, the measurement may need to be completed in a closed airflow measurement environment, so as to ensure that the respiratory airflow is not lost. This is not consistent with the requirement that measurements be performed in real time during the respiratory training process in this embodiment, and it is difficult to directly perform the measurements. The present embodiment thus enables the estimation of instantaneous airflow through a trained neural network-based respiratory volume prediction algorithm. Considering that the most relevant factor of the air flow of the breathing action is the air flow pressure of the mouth and nose of the user in the breathing process, the embodiment takes the air flow as the input of a breathing quantity prediction algorithm, takes various basic physiological indexes of the user as the input, further obtains the predicted value of the instantaneous air flow of the breathing process of the user, and can obtain the total air flow of the single breathing action of the user based on the instantaneous air flow. Because the respiratory quantity prediction algorithm is trained by adopting the state data of the breathing action of the real user, the reliability of the predicted value obtained in the embodiment is higher.
On the other hand, the embodiment also calculates the state data of the breathing action of the user through the state change of the breathing muscle, and the method for calculating the value of a certain real-time breathing training detection quantity by using the method is as follows: 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.
In the embodiment, the idea of the method is to obtain the tension value of the user in the circumferential direction of the waist and abdomen by measuring the state change of the respiratory muscle of the user in different respiratory states; the value has relevance to the value of the equilibrium pressure in the abdominal cavity of the user, and an approximate model of the correlation can be obtained through a large amount of data. After the approximate model is established, the breathing volume or the inhalation volume of the user is calculated according to the change of the balance pressure in the abdominal cavity of different users, so that the state data in the breathing process of the user required in the embodiment can be obtained.
Step four: the requirements and the execution conditions of the breathing training scheme are displayed in an interactive mode of graphic display and voice prompt, and the user is guided; as shown in fig. 2, the specific procedure is as follows:
(1) Displaying all training task units contained in the breathing training scheme in a first graph mode, wherein the first graph reflecting the breathing training scheme is ordered by a time axis; at least each target value in the target respiratory training index is displayed in the first graph.
(2) And sending out voice prompts at the starting time and the ending time of the execution of the respiratory training scheme and the time when the expiration action and the inspiration action are switched.
(3) And generating a second graph for reflecting the execution state of the breathing training scheme according to the acquired values of each real-time breathing training detection quantity when the user executes the breathing training scheme. The second graph is ordered by a time axis; the second graph at least reflects actual measurement values of each real-time breath training measurement value in the actual execution state.
(4) And comparing and displaying the first graph and the second graph in a graph mode, wherein the graph comparison at least reflects 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 breathing training scheme, and displaying the execution completion rate in an interactive mode of graphic display and/or voice prompt. The execution completion rate can be displayed by adopting a numerical value of percentage; the display may also be in the form of percent score values.
As shown in fig. 3, the method for calculating the execution completion rate in the fourth step is as follows:
sequentially acquiring target breath training indexes and real-time breath training detection values in the implementation process of the breath training scheme.
(ii) sequencing the respiratory motion in the respiratory training scheme according to the respiratory motion and the inhalation motion respectively to obtain an exhalation motion task queue and an inhalation motion task array.
(iii) calculating the completion rate of each expiratory motion or inspiratory motion of the expiratory motion task queue and the inspiratory motion task queue; obtaining the arithmetic average completion rate of all the expiration actions in the expiration action task queue and the arithmetic average completion rate of all the inspiration actions in the inspiration 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 that the target respiratory training indicator requires to be completed in the current respiratory training regimen.
(iv) carrying out weighted average on the arithmetic average completion rate of the two parts in the step 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 influence weight of the expiration completion rate on the execution completion rate of the current respiratory training scheme, b represents the influence weight of the inspiration completion rate on the execution completion rate of the current respiratory training scheme, and a+b=1.
In the step, the relation between the state data of the real-time breathing of the user and the target breathing training index in the breathing training scheme is detected, and whether the breathing training process of the user meets the index requirement of the breathing training scheme is judged.
One of the cores in the embodiment is to intuitively display the real-time breathing state data and the target breathing training index of the user through an interactive mode of graphic display and voice prompt. The user checks whether the own breathing training process meets the requirement while watching the breathing training scheme which should be executed currently, and adjusts the own breathing training process in time. This training method actually achieves a special feedback; the user observes own scheme execution condition in real time, and when deviation occurs, the breathing process is timely adjusted, so that the aim of accurately executing according to the breathing training scheme is finally achieved.
The interaction mode of the graphic display and the voice prompt has good guiding effect, and ensures that the breathing training scheme of the user can obtain better execution effect. In the following, the present embodiment will illustrate in two ways how the process of breath training is presented by way of graphical interaction.
In one implementation of this example, the first graph of step four is displayed with a peak graph with time as the abscissa and instantaneous respiratory airflow as the ordinate; each waveform in the peak map reflects one expiratory or inspiratory action. The waveform reflecting the exhalation action is located in the first quadrant of the coordinates of the peak map, and the waveform reflecting the inhalation action is located in the fourth quadrant of the coordinates of the peak map. The ordinate of each point on each waveform represents the instantaneous airflow at that moment, the length of the abscissa corresponding to the waveform reflects the duration of the exhalation or inhalation, and the area enclosed by each waveform and the axis reflects the total airflow of the exhalation or inhalation. 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, so that the comparison between the first graph and the second graph is realized.
In particular, this graphical display scheme in the present embodiment is similar to an electrocardiogram. According to the condition reflected by the first graph, a user observes the respiratory training index to be achieved, then executes the inspiration action and the expiration action at proper time according to the display process of the image, and when the user executes the expiration action and the inspiration action, the user can intuitively know whether the current respiratory intensity meets the requirement according to the overlapping condition of the waveform in the second graph and the waveform of the first graph. If the requirements are not met, the breathing intensity can be adjusted when the same action is executed again, and the beat and the switching frequency of the breathing action can be adjusted according to the results displayed in the first graph and the second graph. Accordingly, the user can observe the completion effect of the completed actions, and further adjust the subsequent actions to realize negative feedback adjustment. The index requirements of the subsequent breathing actions to be executed can be known according to the first graph, namely, the first graph has the effect of guiding the user to perform breathing training.
In another implementation of this example, the first graphic may also be displayed above a continuous time axis using a striped bubble pattern. Each bubble in the first graph represents one breathing action or breathing action, and the colors of the bubbles reflecting the breathing action and the breathing action are different; the area filled inside the bubble characterizes the size of the total air flow of the inspiration or expiration action; the duration of the area filling process in the bubble characterizes the duration of a single exhalation or inhalation motion. The center of the bubble displays the current expiratory or inspiratory instantaneous airflow magnitude by means of a continuously changing number. Each training task element is characterized by a plurality of bubbles arranged in succession 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.
In the scheme, in the process of executing a breath training scheme, a user determines whether to exhale or inhale by observing the color of the air bubble, determines the breathing intensity according to the size of the air bubble, and determines the duration of a single breathing action according to the length of the air bubble on a time axis. Meanwhile, the difference between the bubbles generated in the second graph and the bubbles in the first graph is observed, so that the gap between the currently executed breathing process and the target breathing scheme can be determined, and the purpose of guiding the user to breathe is achieved.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes using the descriptions and drawings of the present invention or directly or indirectly applied to other related technical fields are included in the 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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