CN113555082A - 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 function. The method comprises the following steps: acquiring data of basic physiological indexes related to respiratory function 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 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 the respiratory airflow and respiratory muscles into real-time respiratory training detection quantity of the current user; step four: and the requirements and the execution situation of the breathing training scheme are shown in an interactive mode of a graphic display and a voice prompt, and guidance is provided for a 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 function.
Background
Respiratory function training is a routine rehabilitation means for doctors aiming at patients with certain lung 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 function comprises the following functions: 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 may also help patients to increase the effectiveness of coughing; thereby guaranteeing the rehabilitation effect of the postoperative patient and avoiding the postoperative patient from influencing the rehabilitation of the disease because of the ineffective sputum excretion.
The existing respiratory function training is mainly guided by medical care personnel, a patient is required to carry out regular respiratory training, and the training and supervision of the respiratory process are automatically completed by the patient. Differentiation requirements and training cannot be performed for different types of patients. Meanwhile, the patient can only try according to the advice in most cases, and can not intuitively know how to do the breathing training and know whether the execution condition of the breathing training meets the requirements; these all result in less effective breathing training for the patient.
Disclosure of Invention
Based on the above, the respiratory function training device aims to solve the problems that the existing respiratory function training effect is poor, and 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 of respiratory function, comprising the following steps:
the method comprises the following steps: acquiring data of basic physiological indexes related to respiratory function of a current user: the basic physiological indicators include: indication, sex, 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 most matched with the current user; the breathing training scheme database stores a pre-designed 'basic physiological index-breathing training scheme comparison table'.
Defining a plurality of exhaling and inhaling actions needing to be continuously completed in a respiratory training scheme as a training task unit; the respiratory training scheme comprises at least one training task unit. And defining an index reflecting the execution state of the breathing training scheme as a target breathing training index. The target breathing training indexes comprise:
a. instantaneous flow of gas for a single exhalation or inhalation maneuver.
b. Total airflow for a single exhalation or inhalation maneuver.
c. The duration of a single exhalation or inhalation maneuver.
d. The number of exhale or inhale actions in a single training task unit, and the tempo of the exhale and inhale actions.
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 the respiratory airflow and respiratory muscles into real-time respiratory training detection quantity of the current user; the real-time breathing training detection amount is an actual measurement value of a target breathing training index; the real-time breathing training detection amount is obtained by detecting the air flow change of the mouth and the nose and the state change of respiratory muscles, and the calculation method of the real-time breathing training detection amount is as follows:
Xfruit of Chinese wolfberry＝α·XQi (Qi)+β·XMuscle
In the above formula, XFruit of Chinese wolfberryA value representing a measure of a real-time breath training; xQi (Qi)A value representing a real-time breath training detected quantity calculated from changes in respiratory airflow; xMuscleA value representing a certain real-time respiratory training detection amount calculated through the change of respiratory muscles; alpha represents the weight of the influence of respiratory airflow change on the real-time respiratory training detection amount in the current index, and beta represents the weight of the influence of respiratory muscle change on the real-time respiratory training detection amount in the current index; wherein α and β are expert experience values associated with each real-time respiratory training measurement, and α + β is 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 guidance is given to a user; 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 takes a time axis as an order; the first graph at least displays each item value in the target breathing training index.
(2) And sending voice prompts at the starting time and the ending time of the execution of the breathing training scheme and at the time of switching between the exhalation action and the inhalation action.
(3) And generating a second graph for reflecting the execution state of the breathing training scheme according to the acquired values of the real-time breathing training detection quantities when the user executes the breathing training scheme. The second graph takes a time axis as a sequence; the second graph at least reflects the measured value of each real-time breathing training detection amount in the actual execution state.
(4) And comparing and displaying the first graph and the second graph in a graph, wherein the graph comparison at least reflects the deviation between the target values of the breathing training scheme and the actual measured values 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 indicates a disease type of the user who is currently performing the respiratory training. The indication card, the sex and the age are collected by adopting a manual input mode or are obtained by collecting recent medical data of the user. The data validity period of the gender is long, and the data validity period of the age is less than one year; in the 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 actually detected by a user recently, and the validity period of the data is not more than seven days.
Furthermore, the basic physiological index-breathing training scheme comparison table establishes the corresponding relation between different basic physiological indexes and different breathing training schemes; the basic physiological index-breathing training scheme comparison table is used for matching the optimal breathing training scheme aiming at users with different types of basic physiological indexes; types of training task units included in the respiratory training regimen include: a quick inhalation and quick expiration process, a quick inhalation and slow expiration process, a slow inhalation and quick expiration process and a slow inhalation and slow expiration process; any number of combinations of training task units of the same or different types are included in the respiratory training regimen.
Further, in step three, the instantaneous airflow of a single exhalation or inhalation action is obtained by comprehensively measuring the respiratory airflow and the state change of respiratory muscles. The total airflow for a single exhalation or inhalation maneuver is also obtained by integrating the respiratory airflow and the changes in state of the respiratory muscles. The duration of a single exhalation or inhalation maneuver is measured only by the change in state of the respiratory airflow, i.e.: in this term, α takes the value of 1 and β takes the value of 0; the number of exhale or inhale actions and the beats of exhale and inhale actions in a single training task unit are obtained only by any one of the respiratory airflow and respiratory muscle state changes, namely: where α is 0 and β is 1; or α ═ 1 and β ═ 0.
Further, the method for calculating the value of a certain real-time respiratory training detection quantity through the change of respiratory airflow comprises the following steps: and monitoring the real-time state change of the airflow at the mouth and the nose of the user in real time, and judging that the current breathing action is taken as the breathing action or the inspiration action according to the direction of the airflow. And outputting a predicted value of the instantaneous airflow of the current user respiratory action through a respiratory volume prediction algorithm based on a neural network by taking the duration of the respiratory action detected in real time, the airflow pressure at the mouth and nose position detected during the respiratory action and the basic physiological indexes of the user as output, and counting the predicted value of the total airflow of the current user respiratory action based on the instantaneous airflow. The respiratory prediction algorithm adopts the real basic physiological indexes and respiratory characteristic data of different users to complete the training process.
Further, the method for calculating the value of the detection quantity of the real-time respiratory training through the state change of the respiratory muscle comprises the following steps: measuring the state change of the respiratory muscle of the user to obtain the tension value of the current user in the circumferential direction of the waist and the abdomen in the breathing process; further calculating the corresponding value of the balance pressure in the abdominal cavity of the current user in the breathing process; and finally, establishing a corresponding relation between the change of the balance pressure value of the current user and the inspiratory volume in the thoracic cavity according to the basic physiological indexes of the current user, and obtaining the predicted values of the instantaneous airflow and the total airflow of the expiratory action and the inspiratory action of the user by using the detected state change of the respiratory muscle of the user.
In one embodiment of the present invention, the first graph of step four is displayed using a peak plot of time as abscissa and instantaneous respiratory airflow as ordinate; each waveform in the peak map reflects an exhalation maneuver or an inhalation maneuver. The waveform reflecting the exhalation action is positioned in the first quadrant of the coordinate of the peak diagram, and the waveform reflecting the inhalation action is positioned in the fourth quadrant of the coordinate of the peak diagram. The ordinate of each point on each waveform represents the instantaneous airflow at the moment, the abscissa length corresponding to the waveform reflects the duration of the expiration or inspiration action, and the enclosed area of each waveform and the axis reflects the total airflow of the expiration or inspiration action. Each training task unit is characterized by a continuous peak diagram on a time axis.
The second graph and the first graph are displayed in an overlapped mode according to the same time axis, and elements of points, lines and surfaces 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.
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 expiration action or an inspiration action, and the colors of the bubbles reflecting the inspiration action and the expiration action are different; the area filled inside the bubble represents the size of the total airflow of the inspiration action or the expiration action; the duration of the area filling process in the bubbles characterizes the duration of a single exhalation maneuver or inhalation maneuver. The center of the bubble displays the magnitude of the instantaneous flow of air for the current exhalation maneuver or inhalation maneuver by means of a constantly changing number. Each training task unit is characterized by a plurality of bubbles arranged in series 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 contrast between the first graph and the second graph is formed.
Further, the execution completion rate in step four is calculated as follows:
sequentially acquiring a target breathing training index and a real-time breathing training detection amount in the execution process of the breathing training scheme.
And (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 a rate of completion of each expiratory movement or inspiratory movement of the expiratory movement task cohort and the inspiratory movement task cohort; further obtaining the arithmetic mean completion rate of all exhalation actions in the exhalation action task queue and the arithmetic mean completion rate of all inhalation actions in the inhalation action task queue; the calculation formula is as follows:
in the above formula, VCalling deviceRepresenting the arithmetic mean completion rate of all expiratory movements in the expiratory movement task queue; vSuction deviceRepresenting the arithmetic mean completion rate of all inhalation actions in the inhalation action task queue; n represents the number of expiratory movements in the expiratory task queue, or the number of inspiratory movements in the inspiratory task queue; q. q.sFact iA real-time breath training test volume representing instantaneous airflow during the ith expiratory maneuver or inspiratory maneuver; q. q.sMesh iA value of a target breathing training indicator representing an instantaneous airflow in an ith expiratory maneuver or an inspiratory maneuver; qFact iA real-time breath training measurement representing total airflow during the ith expiratory maneuver or inspiratory maneuver; qMesh iA value of a target breathing training indicator representing a total airflow in an ith expiratory maneuver or an inspiratory maneuver; t is tFact iReal-time breath training detection quantity representing duration of the ith expiration action or inspiration action; t is tMesh iA value of a target respiratory training indicator representing the duration of an ith expiratory maneuver or inspiratory maneuver; n is a radical ofFruit of Chinese wolfberryRepresenting the number of actually performed expiratory or inspiratory manoeuvres, N, in the current breathing training programmeEyes of a userRepresenting targets in a current breathing training regimenThe respiratory training index requires the number of exhale actions or inhale actions to be completed.
(iv) performing weighted average on the arithmetic average completion rate of the two parts in the above step to obtain the execution completion rate of the breathing training scheme, wherein the calculation formula is as follows:
Vhandle＝(a·VCalling device+b·VSuction device)·100％
In the above formula, VHandleRepresenting a completion rate of execution of a current respiratory training regimen; a denotes the weight of the influence of the expiration action completion rate on the execution completion rate of the current respiratory training regimen, b denotes the weight of the influence of the inspiration action completion rate on the execution completion rate of the current respiratory training regimen, and a + b is 1.
Further, the execution completion rate is displayed in the form of a percentage value or a fractional value of percentage.
The intelligent guiding training method for the respiratory function provided by the invention has the following beneficial effects: the method collects various current basic physiological indexes of the user, and matches an optimal breathing training scheme for the user according to the difference of physiological states and indications of the user; the breathing training scheme has detailed quantitative index requirements for each stage in the user breathing training process. Therefore, the scheme of the invention is very targeted and has strong operability.
According to the invention, in the process of executing the respiratory training scheme by the user, the respiratory effect of the user is detected in real time by comprehensively considering the respiratory airflow of the mouth and nose part and the expansion and contraction motion of the waist and abdomen part of the user. The invention integrates the change of the airflow and the state change of the respiratory muscle, so that the respiratory state data can be more accurately obtained.
Meanwhile, in the scheme executing process of the user, the method displays the breathing training scheme in a visual mode and compares the breathing training scheme with the real-time breathing state data of the user, so that the aim of guiding the user to carry out breathing training is fulfilled. This method is particularly intuitive when the user needs to perform breathing exercises using different types of breathing patterns. And when the user has poor execution effect, the method can also enable the user to find 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 flowchart of an intelligent training method for guiding respiratory function in embodiment 1 of the present invention;
FIG. 2 is a flowchart of a presentation method of a procedure performed by the breathing training protocol in embodiment 1 of the present invention;
fig. 3 is a flowchart of a method for calculating an execution completion rate of a breathing training regimen according to 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 for the purpose of better understanding and enabling those skilled in the art to practice the present invention, which are not intended to limit the present invention.
Example 1
The embodiment provides an intelligent guiding training method for respiratory function, as shown in fig. 1, the method includes the following steps:
the method comprises the following steps: acquiring data of basic physiological indexes related to respiratory function of a current user: the basic physiological indicators include: indication, sex, age, height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation.
When respiratory function training is performed on patients, the physiological state of each user is inconsistent, and the same respiratory training requirements cannot be adopted for each user due to different aimed indications, and targeted training is performed according to specific conditions of the user. For example, for some preoperative users, it is desirable primarily for the user to be able to perform high intensity respiratory training so that the chest is fully expanded and the respiratory muscles are fully exercised. Therefore, the sputum can be effectively eliminated after the operation. For the users with impaired respiratory function or pulmonary function in the recovery stage, the respiratory training strategies that should be adopted are completely different; such a client should perform a low-intensity assisted breathing exercise to enable the user to achieve spontaneous breathing, and then gradually increase the intensity of the breathing exercise according to the recovery state of the patient, so as to gradually recover the normal breathing function of the patient.
Factors influencing various indexes in the user breathing training process provided in the embodiment specifically include an indication, sex, age, height, weight, body fat rate, blood pressure, average heart rate and average blood oxygen saturation. In the basic physiological indexes collected in this embodiment, the indication refers to the disease type of the user currently performing the respiratory training. This is the most important factor affecting the user's breathing training metrics. Age and gender are another important index, the physiological states of people at different ages have obvious difference, and the gender of a user is effectively distinguished by considering the difference of physiological conditions of males and females. Usually, the indication, sex and age are collected by manual input or by collecting recent medical data of the user. That is, the user can directly input or select the specific indication, age and sex information. Considering that users who perform respiratory function training mostly belong to patients with physical health problems, relevant contents can also be filled through the information of the patients in the hospital. The data validity period of the gender is long, and the data validity period of the age is less than one year.
Among 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 actually detected by a user recently, and the validity period of the data is not more than seven days. Height, weight, body fat rate, blood pressure, average heart rate, and average blood oxygen saturation, which are also highly correlated with the respiratory function of the user, and therefore the present embodiment also takes into account the influence of these indices. Considering that the state of the indexes is easy to change, the data acquisition difficulty is relatively low, 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 using process, real-time measurement is performed before each respiratory function training as much as possible.
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 most matched with the current user; the breathing training scheme database stores a pre-designed 'basic physiological index-breathing training scheme comparison table'.
In this embodiment, the "basic physiological index-respiratory training scheme comparison table" has corresponding relationships between different basic physiological indexes and different respiratory training schemes; the basic physiological index-breathing training scheme comparison table is used for matching the optimal breathing training scheme aiming at users with different types of basic physiological indexes. The respiratory training scheme is designed for different types of patients according to experience by professional medical staff, and different respiratory training schemes are usually corresponding to different basic physiological indexes. In the embodiment, the "basic physiological index-respiratory training scheme comparison table" is a comparison table established according to expert experience; the optimal breathing training scheme corresponding to each patient can be found through table lookup. The number of the breathing training schemes in the basic physiological index-breathing training scheme comparison table is limited, usually from dozens to dozens, the refinement degree of the breathing training scheme is related to the type number of users who actually apply the method, and the wider the applicable range of the method is, the higher the refinement degree of the breathing training scheme which needs to be designed is. Each different breathing training regimen corresponds to a data segment for each different underlying physiological indicator. And a user who meets any basic physiological index can obtain a corresponding breathing training scheme.
In the embodiment, a plurality of exhaling and inhaling actions needing to be continuously completed in the respiratory training scheme are defined as a training task unit; the respiratory training scheme comprises at least one training task unit.
Types of training task units included in the respiratory training regimen include: a quick inhalation and quick expiration process, a quick inhalation and slow expiration process, a slow inhalation and quick expiration process and a slow inhalation and slow expiration process; any number of combinations of training task units of the same or different types are included in the respiratory training regimen.
The breathing training scheme actually reflects what strategy the user should take to breathe. In order to quantify various requirements in the breathing training scheme of the user, the embodiment defines a concept of a target breathing training index. The present embodiment defines an index reflecting the execution state of the breathing training scenario as a target breathing training index. The target breathing training indexes comprise:
a. instantaneous flow of gas for a single exhalation or inhalation maneuver.
b. Total airflow for a single exhalation or inhalation maneuver.
c. The duration of a single exhalation or inhalation maneuver.
d. The number of exhale or inhale actions in a single training task unit, and the tempo of the exhale and inhale actions.
A complete breathing training process can be characterized through the indexes. Different types of breathing strategies are reflected in the difference of the target breathing training indicators. For example: in a respiratory training task of a certain user, the current user should firstly breathe in a way of quick inhalation and quick exhalation for 1min, then inhale slowly and quickly for 5min, and finally inhale slowly and slowly for 10 min. During an acute inhalation and an acute exhalation, the total airflow required to satisfy a single exhalation maneuver reaches a fixed value, and specifies how many times the user should inhale, exhale, etc. within 1 min. And the breathing training process of the user can be quantified by the method.
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 the respiratory airflow and respiratory muscles into real-time respiratory training detection quantity of the current user; the real-time breathing training detection amount is an actual measurement value of a target breathing training index; the real-time breathing training detection amount is obtained by detecting the air flow change of the mouth and the nose and the state change of respiratory muscles, and the calculation method of the real-time breathing training detection amount is as follows:
Xfruit of Chinese wolfberry＝α·XQi (Qi)+β·XMuscle
In the above formula, XFruit of Chinese wolfberryA value representing a measure of a real-time breath training; xQi (Qi)Is shown to pass throughCalculating the value of a certain real-time respiration training detection quantity according to the change of the respiratory airflow; xMuscleA value representing a certain real-time respiratory training detection amount calculated through the change of respiratory muscles; alpha represents the weight of the influence of respiratory airflow change on the real-time respiratory training detection amount in the current index, and beta represents the weight of the influence of respiratory muscle change on the real-time respiratory training detection amount in the current index; wherein α and β are expert experience values associated with each real-time respiratory training measurement, and α + β is 1.
The present embodiment considers that the state data of the actual breathing of the user is difficult to detect, and therefore a comprehensive algorithm with higher reliability is adopted for estimation. The overall measuring and calculating idea is as follows: on one hand, the pressure of the airflow of the exhalation and inhalation of the user in the process of breathing is detected; and further estimating the airflow of the user in the breathing process according to the physiological indexes of the user. On the other hand, the state change of the respiratory muscle of the user positioned on the chest and the abdomen in the breathing process is detected, and the airflow in the breathing process of the user is estimated according to the state change of the respiratory muscle. The airflow in this embodiment mainly refers to the instantaneous airflow and the total airflow of a single respiratory action.
In this embodiment, the real-time respiratory training detection volume includes the instantaneous airflow and the total airflow of each respiratory action; the duration of a single exhalation or inhalation maneuver, the number of exhale or inhalation maneuvers in a single training task unit, and the beats of the exhalation and inhalation maneuvers.
Specifically, the instantaneous airflow of a single expiratory or inspiratory activity is obtained by comprehensively measuring the respiratory airflow and the state change of respiratory muscles. The total airflow for a single exhalation or inhalation maneuver is also obtained by integrating the respiratory airflow and the changes in state of the respiratory muscles. The duration of a single exhalation or inhalation maneuver is measured only by the change in state of the respiratory airflow, i.e.: in this term, α takes the value of 1 and β takes the value of 0; the number of exhale or inhale actions and the beats of exhale and inhale actions in a single training task unit are obtained only by any one of the respiratory airflow and respiratory muscle state changes, namely: where α is 0 and β is 1; or α ═ 1 and β ═ 0.
In this embodiment, the method for calculating the value of the detection amount of the real-time respiratory training according to the change of the respiratory airflow includes: and monitoring the real-time state change of the airflow at the mouth and the nose of the user in real time, and judging that the current breathing action is taken as the breathing action or the inspiration action according to the direction of the airflow. And outputting a predicted value of the instantaneous airflow of the current user respiratory action through a respiratory volume prediction algorithm based on a neural network by taking the duration of the respiratory action detected in real time, the airflow pressure at the mouth and nose position detected during the respiratory action and the basic physiological indexes of the user as output, and counting the predicted value of the total airflow of the current user respiratory action based on the instantaneous airflow. The respiratory prediction algorithm adopts the real basic physiological indexes and respiratory characteristic data of different users to complete the training process.
The measurement condition that the real respiratory airflow of the user needs to meet is high through the airflow measurement, for example, the measurement may need to be completed in a closed airflow measurement environment, so that the respiratory airflow is ensured not to be lost. This is not in accordance with the requirement of the present embodiment that the measurement needs to be completed in real time during the respiratory training process, and it is difficult to directly implement the measurement. The present embodiment therefore achieves an estimation of instantaneous airflow by a trained neural network-based respiratory volume prediction algorithm. Considering that the most relevant factor of the airflow of the respiratory action is the airflow pressure of the user in the mouth and nose during the respiratory process, the embodiment takes the amount as the input of the respiratory volume prediction algorithm, and takes each basic physiological index of the user as the input, so as to obtain the predicted value of the instantaneous airflow of the respiratory process of the user, and the total airflow of the single respiratory action of the user can be obtained based on the instantaneous airflow. The respiratory capacity prediction algorithm is trained by adopting the state data of the respiratory action of the real user, so that the reliability of the predicted value obtained in the embodiment is high.
On the other hand, in this embodiment, the state data of the breathing action of the user is calculated according to the state change of the breathing muscle, and the method for calculating the value of the certain real-time breathing training detection amount by the method is as follows: measuring the state change of the respiratory muscle of the user to obtain the tension value of the current user in the circumferential direction of the waist and the abdomen in the breathing process; further calculating the corresponding value of the balance pressure in the abdominal cavity of the current user in the breathing process; and finally, establishing a corresponding relation between the change of the balance pressure value of the current user and the inspiratory volume in the thoracic cavity according to the basic physiological indexes of the current user, and obtaining the predicted values of the instantaneous airflow and the total airflow of the expiratory action and the inspiratory action of the user by using the detected state change of the respiratory muscle of the user.
In this embodiment, the idea of the method is to obtain a pulling force value of the user in the circumferential direction of the waist and abdomen by measuring the state change of respiratory muscles of the user in different respiratory states; the value is correlated with 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 expiratory volume or inspiratory volume of the user 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 in the breathing process 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 guidance is given to a user; as shown in fig. 2, 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 takes a time axis as an order; the first graph at least displays each item value in the target breathing training index.
(2) And sending voice prompts at the starting time and the ending time of the execution of the breathing training scheme and at the time of switching between the exhalation action and the inhalation action.
(3) And generating a second graph for reflecting the execution state of the breathing training scheme according to the acquired values of the real-time breathing training detection quantities when the user executes the breathing training scheme. The second graph takes a time axis as a sequence; the second graph at least reflects the measured value of each real-time breathing training detection amount in the actual execution state.
(4) And comparing and displaying the first graph and the second graph in a graph, wherein the graph comparison at least reflects the deviation between the target values of the breathing training scheme and the actual measured values 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 percentage value; the display may also take the form of a fractional value in percent.
As shown in fig. 3, the execution completion rate in step four is calculated as follows:
sequentially acquiring a target breathing training index and a real-time breathing training detection amount in the execution process of the breathing training scheme.
And (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 a rate of completion of each expiratory movement or inspiratory movement of the expiratory movement task cohort and the inspiratory movement task cohort; further obtaining the arithmetic mean completion rate of all exhalation actions in the exhalation action task queue and the arithmetic mean completion rate of all inhalation actions in the inhalation action task queue; the calculation formula is as follows:
in the above formula, VCalling deviceRepresenting the arithmetic mean completion rate of all expiratory movements in the expiratory movement task queue; vSuction deviceRepresenting the arithmetic mean completion rate of all inhalation actions in the inhalation action task queue; n represents the number of expiratory movements in the expiratory task queue, or the number of inspiratory movements in the inspiratory task queue; q. q.sFact iA real-time breath training test volume representing instantaneous airflow during the ith expiratory maneuver or inspiratory maneuver; q. q.sMesh iA value of a target breathing training indicator representing an instantaneous airflow in an ith expiratory maneuver or an inspiratory maneuver; qFact iA real-time breath training measurement representing total airflow during the ith expiratory maneuver or inspiratory maneuver; qMesh iTargeted respiratory training representing total airflow in the ith expiratory or inspiratory maneuverA value of the index; t is tFact iReal-time breath training detection quantity representing duration of the ith expiration action or inspiration action; t is tMesh iA value of a target respiratory training indicator representing the duration of an ith expiratory maneuver or inspiratory maneuver; n is a radical ofFruit of Chinese wolfberryRepresenting the number of actually performed expiratory or inspiratory manoeuvres, N, in the current breathing training programmeEyes of a userRepresenting the number of expiratory or inspiratory maneuvers required to be completed by the target breathing training index in the current breathing training regimen.
(iv) performing weighted average on the arithmetic average completion rate of the two parts in the above step to obtain the execution completion rate of the breathing training scheme, wherein the calculation formula is as follows:
Vhandle＝(a·VCalling device+b·VSuction device)·100％
In the above formula, VHandleRepresenting a completion rate of execution of a current respiratory training regimen; a denotes the weight of the influence of the expiration action completion rate on the execution completion rate of the current respiratory training regimen, b denotes the weight of the influence of the inspiration action completion rate on the execution completion rate of the current respiratory training regimen, and a + b is 1.
In the step, the relation between the state data of the real-time breath of the user and the target breath training index in the breath training scheme is mainly detected, and whether the breath training process of the user meets the index requirement of the breath training scheme or not is judged.
One of the cores in this embodiment is to visually display real-time respiratory state data and target respiratory training indexes of a user through an interactive mode of graphical display and voice prompt. The user can watch the breathing training scheme which should be executed currently, detect whether the breathing training process of the user meets the requirements or not, and adjust the breathing training process of the user in time. This training method actually achieves a special feedback; the user observes the scheme execution condition of the user in real time, and the breathing process is adjusted in time when deviation occurs, so that the aim of accurately executing according to the breathing training scheme is finally achieved.
The interactive 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 how the process of breathing training is presented by means of graphical interaction in two ways.
In one embodiment of this example, the first graph of step four is displayed using a peak plot of time as abscissa and instantaneous respiratory airflow as ordinate; each waveform in the peak map reflects an exhalation maneuver or an inhalation maneuver. The waveform reflecting the exhalation action is positioned in the first quadrant of the coordinate of the peak diagram, and the waveform reflecting the inhalation action is positioned in the fourth quadrant of the coordinate of the peak diagram. The ordinate of each point on each waveform represents the instantaneous airflow at the moment, the abscissa length corresponding to the waveform reflects the duration of the expiration or inspiration action, and the enclosed area of each waveform and the axis reflects the total airflow of the expiration or inspiration action. Each training task unit is characterized by a continuous peak diagram on a time axis.
The second graph and the first graph are displayed in an overlapped mode according to the same time axis, and elements of points, lines and surfaces 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.
In particular, this graphical display scheme in this embodiment is similar to an electrocardiogram. According to the condition reflected by the first graph, a user observes the respiratory training index required to be achieved, then performs inspiration action and expiration action at proper time according to the display process of the image, and when the user performs 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 requirement is not met, the breathing intensity can be adjusted when the same action is executed again, and meanwhile, 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 action, and further adjust the subsequent action to realize negative feedback adjustment. The index requirement of the subsequent breathing action to be executed can be known according to the first graph, namely the first graph has the effect of guiding the user to carry out breathing training.
In another embodiment 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 expiration action or an inspiration action, and the colors of the bubbles reflecting the inspiration action and the expiration action are different; the area filled inside the bubble represents the size of the total airflow of the inspiration action or the expiration action; the duration of the area filling process in the bubbles characterizes the duration of a single exhalation maneuver or inhalation maneuver. The center of the bubble displays the magnitude of the instantaneous flow of air for the current exhalation maneuver or inhalation maneuver by means of a constantly changing number. Each training task unit is characterized by a plurality of bubbles arranged in series 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 contrast between the first graph and the second graph is formed.
In the scheme, during the process of executing the breathing training scheme, a user determines whether expiration or inspiration should be carried out by observing the color of the bubbles, determines the breathing intensity according to the size of the bubbles, and determines the duration of a single breathing action according to the length of the bubbles 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 difference between the currently executed breathing process and the target breathing scheme can be determined, and the purpose of guiding the user to breathe is further achieved.
The above description is only for the preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent flow transformations that may be applied to the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An intelligent training method for guiding respiratory function, which is characterized by comprising the following steps:
the method comprises the following steps: acquiring data of basic physiological indexes related to respiratory function of a current user: the basic physiological indexes include: indication, sex, 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 most matched with the current user; the breathing training scheme database stores a pre-designed 'basic physiological index-breathing training scheme comparison table';
defining a plurality of exhaling and inhaling actions needing 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 breathing training scheme as a target breathing training index, the target breathing training index including:
a. instantaneous airflow for a single exhalation or inhalation maneuver;
b. total airflow for a single exhalation or inhalation maneuver;
c. the duration of a single exhalation or inhalation maneuver;
d. the number of exhale or inhale actions in a single training task unit, and the beats of exhale and inhale actions;
step three: when the user executes the respiratory training scheme, acquiring the state change of the respiratory airflow of the mouth and nose of the user and the state change of respiratory muscles; converting the state changes of the respiratory airflow and respiratory muscles into real-time respiratory training detection quantity of the current user; the real-time breathing training detection amount is an actual measurement value of the target breathing training index; the real-time breathing training detection amount is obtained by detecting the air flow change of the mouth and the nose and the state change of respiratory muscles, and the calculation method of the real-time breathing training detection amount is as follows:
Xfruit of Chinese wolfberry＝α·XQi (Qi)+β·XMuscle
In the above formula, XFruit of Chinese wolfberryExpress a certain(ii) a value of the real-time breath training detection quantity; xQi (Qi)A value representing a certain one of the real-time breath training detection quantities calculated from changes in respiratory airflow; xMuscleA value representing a certain real-time respiration training detection amount calculated through the change of respiratory muscle; alpha represents the weight of the influence of respiratory airflow change on the real-time respiratory training detection amount in the current index, and beta represents the weight of the influence of respiratory muscle change on the real-time respiratory training detection amount in the current index; wherein α and β are expert experience values associated with each of the real-time respiratory training measures, and α + β is 1;
step four: displaying the requirements and the execution conditions of the breathing training scheme in an interactive mode of graphic display and voice prompt, and guiding a user; 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 takes a time axis as an order; at least displaying each item value in the target breathing training index in the first graph;
(2) sending voice prompts at the starting time and the ending time of the execution of the breathing training scheme and at the time of switching between the exhalation action and the inhalation action;
(3) generating a second graph for reflecting the execution state of the breathing training scheme according to the acquired values of the real-time breathing training detection quantities when the user executes the breathing training scheme; the second graph takes a time axis as a sequence; the second graph at least reflects measured values of various real-time breathing training detection quantities in an actual execution state;
(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 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 intelligent guidance training method for respiratory function according to claim 1, characterized in that: in the basic physiological indexes, the adaptation symptoms refer to the disease types of users who currently perform respiratory training; the indication, the sex and the age are acquired by adopting a manual input mode or acquiring recent medical data of the user; the data validity period of the gender is long, and the data validity period of the age is less than one year; in the 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 actually detected by a user recently, and the validity period of the data is not more than seven days.
3. The intelligent guidance training method for respiratory function according to claim 2, characterized in that: the basic physiological index-breathing training scheme comparison table is provided with corresponding relations between different basic physiological indexes and different breathing training schemes; the basic physiological index-breathing training scheme comparison table is used for matching the optimal breathing training scheme aiming at users with different types of basic physiological indexes; the types of the training task units included in the respiratory training regimen include: a quick inhalation and quick expiration process, a quick inhalation and slow expiration process, a slow inhalation and quick expiration process and a slow inhalation and slow expiration process; any combination of a plurality of the same or different types of training task units is included in the respiratory training regimen.
4. The intelligent guidance training method for respiratory function according to claim 3, characterized in that: in the third step, the instantaneous airflow of single expiration or inspiration action is obtained by comprehensively measuring and calculating the state changes of the respiratory airflow and respiratory muscles; the total airflow of a single expiration or inspiration action is also obtained by comprehensively measuring and calculating the state changes of the respiratory airflow and respiratory muscles; the duration of a single exhalation or inhalation maneuver is measured only by the change in state of the respiratory airflow, i.e.: in the term, the value of alpha is 1, and the value of beta is 0; the number of exhale or inhale actions and the beats of exhale and inhale actions in a single training task unit are obtained only by any one of the respiratory airflow and respiratory muscle state changes, namely: where α is 0 and β is 1; or α ═ 1 and β ═ 0.
5. The intelligent guidance training method for respiratory function according to claim 4, characterized in that: the method for calculating the value of the real-time respiration training detection quantity through the change of the respiration airflow comprises the following steps: monitoring the real-time state change of the airflow at the mouth and the nose of the user in real time, and judging the current breathing action as the breathing action or the inspiration action according to the direction of the airflow; outputting a predicted value of the instantaneous airflow of the respiratory action of the current user through a respiratory quantity prediction algorithm based on a neural network by taking the duration of the respiratory action detected in real time, the airflow pressure at the position of the mouth and the nose detected during the respiratory action and the basic physiological indexes of the user as output, and statistically calculating the predicted value of the total airflow of the current respiratory action through the value of the instantaneous airflow; the respiratory capacity prediction algorithm adopts the real basic physiological indexes and respiratory characteristic data of different users to complete the training process.
6. The intelligent guidance training method for respiratory function according to claim 5, characterized in that: the method for calculating the value of the real-time respiration training detection quantity through the state change of the respiratory muscle comprises the following steps: measuring the state change of the respiratory muscle of the user to obtain the tension value of the current user in the circumferential direction of the waist and the abdomen in the breathing process; further calculating the corresponding value of the balance pressure in the abdominal cavity of the current user in the breathing process; and finally, establishing a corresponding relation between the change of the balance pressure value of the current user and the inspiratory volume in the thoracic cavity according to the basic physiological indexes of the current user, and obtaining the predicted values of the instantaneous airflow and the total airflow of the expiratory action and the inspiratory action of the user by using the detected state change of the respiratory muscle of the user.
7. The intelligent guidance training method for respiratory function according to claim 6, characterized in that: the first graph is displayed by adopting a peak graph with time as an abscissa and respiratory instantaneous airflow as an ordinate; each waveform in the peak map reflects an exhalation maneuver or an inhalation maneuver; the waveform reflecting the exhalation action is positioned in the first quadrant of the coordinate of the peak diagram, and the waveform reflecting the inhalation action is positioned in the fourth quadrant of the coordinate of the peak diagram; the ordinate of each point on each waveform represents the instantaneous airflow at the current moment, the abscissa length corresponding to the waveform reflects the duration of the expiration or inspiration action, and the enclosed area of each waveform and the shaft reflects the total airflow of the expiration or inspiration action; each training task unit is characterized by a continuous crest map on a time axis;
the second graph and the first graph are displayed in an overlapped mode according to the same time axis, and elements of points, lines and surfaces 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 intelligent guidance training method for respiratory function according to claim 6, characterized in that: the first graph is displayed above a continuous time axis by using a striped bubble graph; each bubble in the first graph represents an expiration action or an inspiration action, and the colors of the bubbles reflecting the expiration action and the inspiration action are different; the area filled inside the bubble represents the size of the total airflow of the inspiration action or the expiration action; the duration of the area filling process in the bubbles characterizes the duration of a single exhalation action or inhalation action; the center of the bubble displays the size of the instantaneous airflow of the current expiration action or inspiration action through a constantly changing number; each training task unit is characterized by a plurality of bubbles which are continuously arranged 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 contrast between the first graph and the second graph is formed.
9. The intelligent guidance training method for respiratory function according to claim 7 or 8, characterized in that: the execution completion rate is calculated as follows:
sequentially acquiring a target breathing training index and a real-time breathing training detection amount in the execution process of a breathing 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 a rate of completion of each expiratory or inspiratory motion of the expiratory motion task cohort and the inspiratory motion task cohort; further obtaining the arithmetic mean completion rate of all expiratory actions in the expiratory action task queue and the arithmetic mean completion rate of all inspiratory actions in the inspiratory action task queue; the calculation formula is as follows:
in the above formula, VCalling deviceRepresenting the arithmetic mean completion rate of all expiratory movements in the expiratory movement task queue; vSuction deviceRepresenting the arithmetic mean completion rate of all inhalation actions in the inhalation action task queue; n represents the number of expiratory movements in the expiratory task queue, or the number of inspiratory movements in the inspiratory task queue; q. q.sFact iA real-time breath training test volume representing instantaneous airflow during the ith expiratory maneuver or inspiratory maneuver; q. q.sMesh iA value of a target breathing training indicator representing an instantaneous airflow in an ith expiratory maneuver or an inspiratory maneuver; qFact iA real-time breath training measurement representing total airflow during the ith expiratory maneuver or inspiratory maneuver; qMesh iA value of a target breathing training indicator representing a total airflow in an ith expiratory maneuver or an inspiratory maneuver; t is tFact iReal-time breath training detection quantity representing duration of the ith expiration action or inspiration action; t is tMesh iA value of a target respiratory training indicator representing the duration of an ith expiratory maneuver or inspiratory maneuver; n is a radical ofFruit of Chinese wolfberryIndicating the current breathing exerciseNumber of actually performed exhaling or inhaling actions in the exercise program, NEyes of a userRepresenting the number of expiration actions or inspiration actions required to be completed by a target respiration training index in the current respiration training scheme;
(iv) performing weighted average on the arithmetic mean completion rate of the two parts in the previous step to obtain the execution completion rate of the breathing training scheme, wherein the calculation formula is as follows:
Vhandle＝(a·VCalling device+b·VSuction device)·100％
In the above formula, VHandleRepresenting a completion rate of execution of a current respiratory training regimen; a denotes the weight of the influence of the expiration action completion rate on the execution completion rate of the current respiratory training regimen, b denotes the weight of the influence of the inspiration action completion rate on the execution completion rate of the current respiratory training regimen, and a + b is 1.
10. The intelligent guidance training method for respiratory function according to claim 1, characterized in that: the execution completion rate is displayed in a percentage numerical value or a percentage fractional value mode.
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