A method for detecting breathing training data

Abstract

This invention relates to the field of respiratory training technology and provides a method for detecting respiratory training data. The method includes collecting respiratory data detected by a respiratory training device and preprocessing the data to obtain preprocessed data; calculating flow rate based on the respiratory data; calculating volume based on the respiratory data and flow rate; calculating power based on the respiratory data, muscle strength, and flow rate; calculating calories based on the respiratory data and power; and combining volume, flow rate, muscle strength, power, and calories to analyze and optimize the training effect. This invention provides a quantitative analysis of respiratory training using volume, flow rate, muscle strength, power, and calories, providing specific data representation of the respiratory training effect, and enabling corresponding strengthening and optimization of respiratory training to improve its effectiveness.

Description

Technical Field

[0001] This invention belongs to the field of breathing training technology, and in particular relates to a method for detecting breathing training data. Background Technology

[0002] The purpose of breathing training is to improve gas exchange; improve the elasticity of the lungs and chest; maintain and increase the mobility of the rib cage; strengthen effective coughing; strengthen respiratory muscles and improve respiratory coordination; relieve chest tension; and improve the patient's physical condition. Common methods include: 1. Abdominal breathing training: lie quietly with a moderately weighted sandbag placed on the abdomen, and control the rib cage to breathe evenly by raising and lowering the abdomen; 2. Inspiratory resistance breathing training: control the inhalation volume and duration; 3. Passive training: requires manual therapy by a professional therapist, such as compression of the rib cage, back, and scapula.

[0003] In the process of lung breathing training using equipment-assisted valve-type or step-type breathing trainers, data testing typically measures gas pressure values, valve opening values, and other data. However, this data cannot help medical staff effectively quantify and analyze the effects of breathing training, thus preventing them from further optimizing the breathing training process. Summary of the Invention

[0004] This invention provides a method for detecting breathing training data, aiming to solve the problem that current breathing training data from breathing trainers cannot be quantitatively analyzed, which affects the optimization of breathing training.

[0005] This invention is implemented as follows: a method for detecting breathing training data, comprising:

[0006] Collect lung training breathing data detected by the breathing training device and preprocess the lung training breathing data to obtain preprocessed data; the lung training breathing data includes gas pressure value, atmospheric pressure value, valve opening value and device detection time;

[0007] The preprocessing of the lung training breathing data includes: calculating the sensor coefficient Ki of the gas sensor and filtering the valve opening value to obtain the final valve opening value Do. Se ;

[0008] The formula for calculating the sensor coefficient Ki is as follows:

[0009] ;

[0010] ;

[0011] Where Sdi is the sensor's air pressure value under standard atmospheric pressure, Sin is the sensor's last sensing value under artificially defined standard airflow conditions, Sn is the air pressure sensor value measured at different times under standard airflow, Se is the number of times the sensor was read under standard airflow, and Sse is the set of absolute values ​​of all newly detected sensor values ​​from the 0th to the Seth items minus the standard atmospheric pressure value;

[0012] The final value of the valve opening value Do Se The calculation formula is as follows:

[0013] Do Se =Do' Se +(Sin / Fu*Fd);

[0014] F(u) = Ki * Preset upper limit of resistance value;

[0015] F(d) = Ki * Preset lower limit of resistance value;

[0016] Do' Se ={Do0, Do1, ..., Do Se};

[0017] Among them, Do Se The current number of times the air valve is opened, from item 0 to item Se;

[0018] Flow rate is calculated based on lung training breathing data. Based on lung training breathing data and flow rate Calculate the volume Tl;

[0019] The flow rate The calculation is as follows:

[0020] ;

[0021] Where Dt is the total detection time, Tis is the flow rate deviation value, Tis = Ki * flow rate unit conversion value, and the flow rate unit conversion value is 10. Set the valve opening value. This represents the average value of the sensor after subtracting atmospheric pressure during the breathing process; Do represents the final value of the valve opening after removing the filtering effect during the breathing process. Se The average value of the ratio of the valve opening value Ds to the set valve opening value;

[0022] Muscle strength is calculated based on lung training breathing data. And according to muscle strength Calculate power Tw based on flow velocity;

[0023] The muscle strength The calculation is as follows:

[0024] ;

[0025] Where Tic is the muscle strength deviation value, Tic = Ki * muscle strength unit conversion value, and the muscle strength unit conversion value is 100;

[0026] The power Tw is calculated as follows:

[0027] ;

[0028] Where Tiw is the power deviation value; Tiw=Ki*S*power unit conversion value, S is the cross-sectional area of ​​the exhalation channel in the breathing trainer, and the power unit conversion value is 100;

[0029] The calorie Tj is calculated based on lung training breathing data and power Tw, as follows:

[0030] ;

[0031] Where Tij is the heat deviation value, Tij=Ki*heat unit conversion value, and the heat unit conversion value is 1000;

[0032] Combining volume Tl and flow rate Muscle strength The effects of breathing training were optimized by analyzing power (Tw) and heat (Tj).

[0033] Preferably, the artificially defined standard airflow conditions are an airflow velocity of 1 L / s and a volume of 1 L.

[0034] Preferably, the capacity Tl is calculated as follows:

[0035] .

[0036] Compared with the prior art, the embodiments of this application have the following main advantages:

[0037] 1. The breathing training data detection method provided by this invention captures lung training breathing data during exhalation, including gas pressure value, atmospheric pressure value, valve opening value, and equipment detection time. It defines and calculates volume, flow rate, muscle strength, power, and heat, and uses volume, flow rate, muscle strength, power, and heat to perform quantitative analysis of breathing training, thereby improving the ability to quantitatively analyze breathing training and providing specific data representation of the breathing training effect.

[0038] 2. The breathing training data detection method provided by this invention uses volume, flow rate, muscle strength, power and heat to quantitatively analyze breathing training, effectively obtain the training effect during the breathing training process, and further improve breathing training through relevant data effects, strengthen and optimize breathing training accordingly, and improve the training effect of breathing training. Detailed Implementation

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification and claims of this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification and claims of this application are used to distinguish different objects, not to describe a particular order.

[0040] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] This invention provides a method for detecting breathing training data, the method comprising:

[0042] Collect lung training breathing data detected by the breathing training device and preprocess the lung training breathing data to obtain the preprocessed data;

[0043] The lung training breathing data includes gas pressure value, atmospheric pressure value, valve opening value, and equipment detection time;

[0044] Calculate flow rate based on lung training breathing data; and calculate volume based on lung training breathing data and flow rate.

[0045] Power is calculated based on lung training breathing data and muscle strength and flow rate.

[0046] Calories are calculated based on lung training breathing data and power output;

[0047] The effectiveness of breathing training can be optimized by combining volume, flow rate, muscle strength, power, and calorie analysis.

[0048] In this embodiment, the lung training breathing data includes gas pressure value, atmospheric pressure value, valve opening value, and device detection time. This data is acquired by detecting the trainee's exhalation through the built-in pressure sensor and key valve components in the breathing training device. By capturing the gas pressure value, atmospheric pressure value, valve opening value, and device detection time during exhalation, volume, flow rate, muscle strength, power, and heat are defined and calculated. These metrics are then used to quantify and analyze the breathing training, improving the ability to quantitatively analyze breathing training and providing concrete data representation of the training effect.

[0049] In this embodiment, the breathing training data detection method relies on a barometric pressure sensor. To ensure data accuracy, the method will control the differences between sensors as much as possible. As the airflow speeds up and the gas pressure increases, in order to achieve the corresponding level of gas resistance value, the valve needs to open wider after exceeding the preset pressure value at the corresponding level to achieve a suitable resistance value. During this adjustment process, the change in the valve opening value will also have a certain impact on the data analysis process. Therefore, it is necessary to preprocess the relevant data, calculate the correlation coefficient, or perform filtering to improve the rigor of the basic data.

[0050] In this embodiment, volume Tl: Lung volume refers to the amount of gas contained in the lungs, specifically the amount of gas contained in the lungs after a deep inhalation. Common units are liters (L) and milliliters (ml); flow rate Ts: The flow rate of gas in the lungs is expressed as the volume of gas passing through the detector per unit time. Common units are liters per second (L / s) or liters per minute (L / min); muscle strength Tc: Lung muscle strength refers to the force of lung contraction during voluntary breathing. The unit is Newtons (N); power Tw: Lung power refers to the amount of work done by the lungs per unit time. Power is a physical quantity describing the rate of work done. The unit is watts (W); heat Tj: Lung heat refers to the energy consumed by the lungs during respiratory movements. The unit is joules (J).

[0051] By using volume, flow rate, muscle strength, power, and heat to quantify breathing training, the training effect during the breathing training process can be effectively obtained. The relevant data can be used to further improve breathing training, and corresponding strengthening and optimization can be carried out to improve the training effect of breathing training.

[0052] In a further preferred embodiment of the present invention, the preprocessing of the lung training breathing data includes:

[0053] Calculation of the sensor coefficient Ki for the gas sensor;

[0054] The final value of the valve opening is obtained by filtering the valve opening value. Se ;

[0055] The formula for calculating the sensor coefficient Ki is as follows:

[0056] ;

[0057] ;

[0058] Where Sdi is the sensor's pressure value under standard atmospheric pressure, Sin is the sensor's last reading under artificially defined standard airflow conditions, Sn is the sensor's pressure value measured at different times under standard airflow conditions, and Se is the number of times the sensor was read under standard airflow conditions; S se The set of absolute values ​​of all sensor readings from the newly detected 0th to the Seth term minus the standard atmospheric pressure value.

[0059] In this embodiment, the breathing training data detection method also relies on a barometric pressure sensor. To control the differences between sensors as much as possible in terms of data accuracy, errors within the allowable range will occur in actual sensors. These errors will affect the data results of the algorithm of this invention. Therefore, a sensor calibration algorithm is also added to the algorithm to avoid the problem of large errors. Setting a sensor coefficient Ki for each sensor and adding the sensor coefficient of the sensor to the detection data for calculation can greatly improve the accuracy of the data.

[0060] The final valve opening value, Do, needs to be generated by filtering the last sensor value along with the lower and upper limits of the air pressure resistance. Se The final value of the valve opening, Do. Se The calculation formula is as follows:

[0061] Do Se =Do' Se +(Sin / Fu*Fd);

[0062] F(u) = Ki * Preset upper limit of resistance value;

[0063] F(d) = Ki * Preset lower limit of resistance value;

[0064] Do' Se ={Do0, Do1, ..., Do Se};

[0065] Among them, Do' Se For terms 0 to Se, the threshold is set for the current number of times.

[0066] In this embodiment, during valve-type breathing training, as the airflow accelerates and the gas pressure increases, to achieve the corresponding level of gas resistance value, the valve needs to increase its opening degree after exceeding the preset gas pressure value at the corresponding level, thus achieving a suitable resistance value. Here, Do is the valve opening value, Dm is the maximum valve opening value, Fu is the upper limit of the gas pressure resistance value, and Fd is the lower limit of the gas pressure resistance value. When the device detects that the gas resistance value is greater than the upper limit of the gas pressure resistance value, it will gradually increase the valve opening value Do according to the set valve opening value Ds, cyclically, until the gas resistance value reaches the preset range.

[0067] As a preferred embodiment in this example, the flow rate The flow rate is the volume of air exhaled from the lungs per unit time during the exhalation process; the flow rate is... The calculation is as follows:

[0068] ;

[0069] Where Dt is the total detection time and Tis is the flow rate deviation value. Set the valve opening value as follows: Tis = Ki * flow rate unit conversion value, where the flow rate unit conversion value is 10.

[0070] In this embodiment, the main function of the flow rate unit conversion value is to balance the units between the numerical values ​​and ensure unit balance;

[0071] In this embodiment, This represents the average value of the sensor after subtracting atmospheric pressure during the breathing process; Do represents the final value of the valve opening after removing the filtering effect during the breathing process. Se The average value of the ratio of the sensor reading to the set valve opening value Ds, after removing the influence of atmospheric pressure, is compared with the final valve opening value Do. Se The product of the average ratio of the set valve opening value Ds and the total lung gas volume during this breath can be obtained by dividing by the time. Multiply by Tis to get the flow rate deviation value, and obtain the final value. ;

[0072] As a preferred embodiment of this example, the muscle strength This refers to the force of lung contraction during voluntary breathing, expressed as the opening value of the air valve; the muscle force... The calculation is as follows:

[0073] ;

[0074] Where Tic is the muscle strength deviation value, Tic = Ki * muscle strength unit conversion value, and the muscle strength unit conversion value is 100.

[0075] In this embodiment, the unit of muscle strength is N, the muscle strength deviation value Tic is to remove interference from the sensor, and the unit conversion value mainly serves to balance the unit balance between values ​​and ensure unit balance.

[0076] In this embodiment, This represents the average value of the sensor after subtracting atmospheric pressure during the breathing process; Do represents the final value of the valve opening after removing the filtering effect during the breathing process. Se The average value of the ratio of the set valve opening value Ds reflects the unrevised value of the average lung gas volume per breath. The unrevised value of the average lung gas volume per breath is multiplied by the muscle strength deviation value Tic, and the average lung gas volume per breath reflects the force of lung contraction during voluntary breathing.

[0077] As a preferred embodiment of this work, the capacity Tl is calculated as follows:

[0078] .

[0079] In this embodiment, the volume = flow rate per unit time * time, and the value is obtained through the above formula;

[0080] In a preferred embodiment of this invention, the power Tw is calculated as follows:

[0081] ;

[0082] Where Tiw is the power deviation value, Tiw=Ki*S*power unit conversion value, S is the cross-sectional area of ​​the exhalation channel in the breathing trainer, and the power unit conversion value is 100.

[0083] In this embodiment, the power unit is W, the power deviation value Tiw is to remove interference from the sensor, and the power unit conversion value is mainly used to balance the unit balance between values ​​and ensure unit balance.

[0084] As a preferred embodiment of this work, the heat Tj is calculated as follows:

[0085] ;

[0086] Where Tij is the heat deviation value, Tij=Ki*heat unit conversion value, and the heat unit conversion value is 1000.

[0087] In this embodiment, the heat unit is J, the heat deviation value Tij is to remove interference from the sensor, and the heat unit conversion value is mainly used to balance the units between the values ​​and ensure unit balance.

[0088] It should be noted that, for the sake of simplicity, the foregoing embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to the present invention. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.

Claims

1. A method for detecting breathing training data, characterized in that, include: Collect lung training breathing data detected by the breathing training device and preprocess the lung training breathing data to obtain preprocessed data; the lung training breathing data includes gas pressure value, atmospheric pressure value, valve opening value and device detection time; The pre-processing of the lung training breathing data includes calculation of sensor coefficients Ki of the gas sensor pair and filtering of the gas valve opening value to obtain a final value Do of the gas valve opening value Se ; The formula for calculating the sensor coefficient Ki is as follows: ； ； Where Sdi is the sensor's air pressure value under standard atmospheric pressure, Sin is the sensor's last sensing value under artificially defined standard airflow conditions, Sn is the air pressure sensor value measured at different times under standard airflow, Se is the number of times the sensor was read under standard airflow, and Sse is the set of absolute values ​​of all newly detected sensor values ​​from the 0th to the Seth items minus the standard atmospheric pressure value; The final value of the valve opening value Do Se The calculation formula is as follows: Do Se =Do' Se +(Sin / Fu*Fd); F(u) = Ki * Preset upper limit of resistance value; F(d) = Ki * Preset lower limit of resistance value; Do' Se ={Do0,Do1…,Do Se }; Among them, Do Se The current number of times the air valve is opened, from item 0 to item Se; Flow rate is calculated based on lung training breathing data. Based on lung training breathing data and flow rate Calculate the volume Tl; Wherein, the flow rate The calculation is as follows: ； Dt represents the total detection time, and Tis represents the flow rate deviation value. Tis = Ki * the flow rate unit conversion value, where the flow rate unit conversion value is 10. Set the valve opening value. This represents the average value of the sensor after subtracting atmospheric pressure during the breathing process; Do represents the final value of the valve opening after removing the filtering effect during the breathing process. Se The average value of the ratio of the valve opening value Ds to the set valve opening value; The volume Tl is calculated as follows: Where Dt is the total detection time; Muscle strength is calculated based on lung training breathing data. And according to muscle strength Calculate power Tw based on flow velocity; The muscle strength The calculation is as follows: ； Where Tic is the muscle strength deviation value, Tic = Ki * muscle strength unit conversion value, and the muscle strength unit conversion value is 100; The power Tw is calculated as follows: ； Where Tiw is the power deviation value; Tiw=Ki*S*power unit conversion value, S is the cross-sectional area of ​​the exhalation channel in the breathing trainer, and the power unit conversion value is 100; The calorie Tj is calculated based on lung training breathing data and power Tw, as follows: ； Where Tij is the heat deviation value, Tij=Ki*heat unit conversion value, and the heat unit conversion value is 1000; Combining volume Tl and flow rate Muscle strength The effects of breathing training were optimized by analyzing power (Tw) and heat (Tj).

2. The breathing training data detection method as described in claim 1, characterized in that, The artificially defined standard airflow conditions are an airflow velocity of 1 L / s and a volume of 1 L passing through the airflow.

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