KR20160093457A

KR20160093457A - APPARATUS OF GENERATING predictive model BASED Supervised Learning AND APPARATUS AND METHOD OF TRAINNING CARDIOPULMONARY RESUSCITATION USING THE predictive model

Info

Publication number: KR20160093457A
Application number: KR1020150014494A
Authority: KR
Inventors: 권예람; 이성원; 박신후
Original assignee: 주식회사 아이엠랩
Priority date: 2015-01-29
Filing date: 2015-01-29
Publication date: 2016-08-08
Also published as: WO2016122052A1

Abstract

A data processing apparatus is provided. An apparatus according to an embodiment includes a first sensor for measuring a first parameter associated with CPR that is repeatedly performed on an object, a second sensor associated with the CPR and measuring a second parameter, And a processor for constructing a correlation between the second parameters as a prediction model.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a device for generating a predictive model based on map learning and a CPR training apparatus using the predictive model,

The present invention relates to a CPR training device and its method of operation, and more particularly to an apparatus that can provide training feedback using measured values in a low cost CPR training device.

The need for education related to cardiopulmonary resuscitation is emerging around the world, especially in North America and Europe. Cardiopulmonary resuscitation (CPR) refers to the simultaneous treatment of artificial respiration and cardiac arrest. Compression and respiration are very important steps in CPR. Cardiopulmonary resuscitation (CPR) is not only performed in a short period of time, but it also contributes to increasing the survival rate of patients with cardiac arrest by generating adequate depth of compression and volume. To this end, schools and companies are actively promoting CPR training.

The curriculum of CPR is mainly made up of manikins (or dummy) capable of CPR. Providing abundant feedback by precise and rapid measurement of pressure during trainees' breathing and breathing exercises can contribute to raise coping ability in real emergency situations by increasing learning effect. However, low-cost manikin models, which are mainly used in CPR training, have slightly different characteristics for each manufacturer, and even the same model can vary depending on time and management level.

In CPR education, discrete values are measured by using switches or instruments, or sensed each time, and the result is provided through a complicated calculation process. Therefore, the result is provided after a certain time delay after sensing. That is, the sensing and the provision of the result value are not performed in real time, and the feedback can not be provided to the trainee at a proper time. Also, one educator can not closely monitor all of the trainees' training activities.

A data processing apparatus according to an embodiment includes a first sensor for measuring a first parameter associated with a CPR repeatedly performed on an object, a second sensor associated with the CPR and measuring a second parameter, And constructing a correlation between the first parameter and the second parameter as a predictive model.

The first parameter according to one embodiment corresponds to the pressure applied to the object in association with the CPR, and the second parameter corresponds to the acceleration with which one side of the object is squeezed due to the pressure.

The processing unit according to an embodiment calculates the force value by calculating the displacement of the force value in the pressing cycle in consideration of the directionality of the pressure.

The processing unit according to an embodiment calculates the depth by integrating the acceleration, calculating the speed, integrating the calculated speed to calculate the depth, and calculating the depth value based on the distance displacement according to the calculated depth.

The first parameter according to one embodiment corresponds to the air pressure applied to the object in association with the CPR, and the second parameter corresponds to the amount of air that one side of the object swells due to the air pressure.

A CPR training apparatus according to an embodiment includes a sensor for measuring a first parameter associated with CPR performed on an object, and a second parameter associated with the first parameter and a CPR associated with CPR, And a processor for estimating a second parameter associated with the CPR using a predictive model.

The first parameter according to one embodiment corresponds to the pressure applied to the object in association with the CPR and the second parameter corresponds to the depth value at which one side of the object is squeezed due to the pressure.

The CPR training apparatus according to one embodiment includes a detachable patch-type device at one side of an object.

A method of operating a CPR training device in accordance with an embodiment includes the steps of measuring at a sensor a first parameter associated with a CPR performed on an object, and determining, at the processing portion, between the first parameter and a second parameter associated with CPR And estimating a second parameter associated with the CPR using the predictive model constructed by learning the correlation of the CPR.

Measuring the first parameter according to an embodiment includes measuring pressure applied to the object in association with the CPR, and estimating the second parameter comprises: And estimating a depth value at which one side of the object is squeezed.

Measuring the first parameter in accordance with an embodiment includes measuring an air pressure applied to the object in connection with the CPR, and estimating the second parameter comprises: And estimating the amount of breathing that one side of the object swells up.

The sensor according to one embodiment includes a detachable patch-type device at one side of the object.

According to some embodiments, real-time feedback is possible during CPR training by reducing the process of calculating the result according to the CPR training. In addition, continuous and consistent data measurement is possible regardless of the manikin model during CPR training. Furthermore, by providing rich feedback by precise and rapid value measurement, it is possible to improve learning effect by CPR training.

1 is a view for explaining a data processing apparatus for constructing a prediction model according to an embodiment.
2 is a diagram illustrating a CPR training apparatus using a prediction model according to an embodiment.
3 is a flowchart illustrating an operation of a data processing apparatus and an operation of a CPR training apparatus according to an embodiment.
FIG. 4 is a flowchart illustrating a method of calculating a force-depth linear prediction model among prediction models according to an embodiment.
FIG. 5 is a diagram illustrating a force-depth linear prediction model calculated in FIG.
FIG. 6 is a flowchart illustrating a method of calculating an atmospheric pressure-respiration rate linear prediction model among prediction models according to an embodiment.
FIG. 7 is a view for explaining the barometric pressure-volume linear prediction model calculated in FIG.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the rights is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

The terms used in the following description are chosen to be generic and universal in the art to which they are related, but other terms may exist depending on the development and / or change in technology, customs, preferences of the technician, and the like. Accordingly, the terminology used in the following description should not be construed as limiting the technical thought, but should be understood in the exemplary language used to describe the embodiments.

Also, in certain cases, there may be a term chosen arbitrarily by the applicant, in which case the meaning of the detailed description in the corresponding description section. Therefore, the term used in the following description should be understood based on the meaning of the term, not the name of a simple term, and the contents throughout the specification.

1 is a view for explaining a data processing apparatus 100 for constructing a prediction model according to an embodiment.

The data processing apparatus 100 according to an embodiment can construct a prediction model by applying map learning, which is a method of machine learning, prior to CPR training. For example, the data processing apparatus 100 may generate a prediction model associated with a compression depth and a respiratory volume from training data for a particular object, respectively.

The constructed prediction model is recorded as a correction function of the corresponding CPR so that it can immediately provide a continuous and consistent depth and volume of training for the practitioner in the actual implementation phase of the training situation. In addition, since the prediction model is constructed on an object-by-object basis, the own specification of each object can be reflected.

To this end, the data processing apparatus 100 according to one embodiment may include a first sensor 110, a second sensor 120, and a processing unit 130.

First, the first sensor 110 measures a first parameter associated with CPR that is repeatedly performed on the object, and the second sensor 120 measures a second parameter associated with CPR.

The processing unit 130 can construct a correlation between the first parameter and the second parameter as a prediction model.

In one example, the first parameter corresponds to the pressure applied to the object in connection with CPR, and the second parameter corresponds to the acceleration at which one side of the object is squeezed due to pressure. That is, the first parameter may include pressure by CPR repeated a predetermined number of times or more, and the second parameter may include an acceleration at which one side of the object, e.g., the chest of the dummy, is squeezed by force . Since the depth value can be calculated by the acceleration, the second parameter may be interpreted as the depth value at which the chest of the dummy is pressed.

More specifically, the processing unit 130 may calculate the force value by calculating the displacement of the force value within the compression period in consideration of the directionality of the pressure in the first parameter for constructing the prediction model. Further, the processing unit 130 may calculate the depth by integrating the acceleration, calculating the speed, integrating the calculated speed to calculate the depth, and calculating the depth value based on the distance displacement according to the calculated depth.

The processing unit 130 can construct a predictive model using the calculated force value and depth value. The generated prediction model may include a force-depth linear prediction model.

On the other hand, the first parameter corresponds to the air pressure applied to the object in connection with the CPR, and the second parameter can correspond to the amount of the breathing of one side of the object, e.g., the chest of the dummy, due to the air pressure.

In this case, the processing unit 130 may use the atmospheric pressure instead of the pressure, and generate a predictive model including the atmospheric pressure-volume linear predictive model using the depth value instead of the depth value. For example, the prediction model can be classified according to the age, gender, etc. of the subject of CPR. Specifically, a prediction model of 30 adult males and a prediction model of 10 females can be generated separately. For example, the processing unit 130 may generate a prediction model for 30 adult males by applying a weight to a prediction model of a teenage woman.

2 is a diagram illustrating a CPR training apparatus using a prediction model according to an embodiment.

The CPR training apparatus 200 according to one embodiment can reduce the process of calculating the result of the CPR training using the constructed prediction model, thereby enabling real-time feedback during the CPR training. In addition, the CPR training apparatus 200 according to an exemplary embodiment of the present invention can continuously and consistently measure data regardless of the manikin model during CPR training, and provides a rich feedback by precise and rapid measurement of values. The learning effect can be enhanced.

For this, the CPR training apparatus 200 according to an exemplary embodiment may include a sensor 210 and a processing unit 220.

The sensor 210 measures a first parameter associated with CPR performed on the object. Accordingly, the processing unit 220 can estimate the second parameter related to the CPR using the pre-built prediction model. The prediction model is constructed by learning a correlation between a first parameter and a second parameter associated with CPR, and provides a second parameter corresponding to the measured first parameter.

The prediction model may be stored in the storage unit 230 and stored in the storage unit 230, and the storage unit 230 may be implemented in the form of a local memory or an external database.

In other words, since the second parameter corresponding to the first parameter is estimated using the prediction model, the second parameter calculation process can be omitted.

In one example, the first parameter may correspond to a pressure applied to the object in association with CPR, and the second parameter may correspond to a depth value at which one side of the object is squeezed due to pressure. That is, it is possible to measure the force value of the trainee during CPR using only the pressure sensor, and to estimate the depth value as the second parameter in the predictive model using the measured force value as the first parameter.

In another example, the first parameter corresponds to the atmospheric pressure applied to the object in association with CPR, and the second parameter may correspond to the amount of breathing at one side of the object due to the atmospheric pressure.

The CPR training apparatus 200 according to an exemplary embodiment can repeat or terminate the training process of the trainee by confirming that the estimated depth value is data within an appropriate range. In addition, it is possible to improve the interest of the trainee in the education by feeding back to the trainee whether or not the data is within the proper range.

The CPR training device 200 according to one embodiment may be implemented in the form of a detachable patch-type device at one side of an object.

3 is a flowchart illustrating an operation of a data processing apparatus and an operation of a CPR training apparatus according to an embodiment.

In a data processing apparatus according to an embodiment, a first parameter associated with CPR may be measured (Step 301), and a second parameter may be measured corresponding to the measured first parameter (Step 302). Further, in the data processing apparatus, the correlation between the first parameter and the second parameter can be constructed as a predictive model (step 303).

For example, the data processing apparatus may measure a pressure displacement associated with CPR using a first parameter generated on an object, and calculate a distance displacement by measuring a second parameter generated on the object. Also, a depth value corresponding to the calculated pressure displacement and a depth value corresponding to the calculated distance displacement can be calculated, and this can be repeated more than a predetermined number of times to construct a prediction model based on the force value and the depth value.

If the data processing apparatus measures the atmospheric pressure as the first parameter and the tidal volume as the second parameter, the data processing apparatus can construct a predictive model including the atmospheric pressure-volume information.

A prediction model can be constructed in the data processing apparatus through steps 301 to 303. [

In a CPR training device, the established predictive model can be used for CPR training.

Specifically, when the sensing information is collected, the CPR training apparatus can load the generated prediction model (step 304). For example, a CPR training device may load a pre-built prediction model when measuring a first parameter associated with CPR performed on an object via a sensor.

In one example, the prediction model may be recorded in a local memory and recorded in an external database via a wired or wireless network.

Next, the CPR training device can extract at least one of the depth value and the breath volume using the predictive model (step 305).

If the measured first parameter is a pressure (force value) by the pressure sensor, the CPR training device can extract the depth value corresponding to the pressure using the predictive model. When the measured first parameter is the atmospheric pressure by the atmospheric pressure sensor, the CPR training apparatus can extract the breath volume corresponding to the atmospheric pressure using the predictive model.

FIG. 4 is a flowchart illustrating a method of calculating a force-depth linear prediction model among prediction models according to an exemplary embodiment.

The prediction model may include a force-depth linear prediction model where the first parameter corresponds to the pressure applied to the object in connection with CPR and the second parameter is the acceleration at which one side of the object is squeezed due to pressure .

Specifically, the method of operation of the data processing apparatus can calculate the force value by the pressure displacement as a first parameter (step 402) in response to the pressure 401 performed by the trainee. Further, the operation method of the data processing apparatus calculates the velocity through integration of the acceleration value (Step 403), calculates the depth by the integral of the calculated velocity (Step 404), and calculates The depth value at which the chest of the dummy is compressed can be calculated (step 405).

When the compression is performed, the operation method of the data processing apparatus can calculate the force value measured from the pressure sensor and the acceleration for each axis measured from the three-axis acceleration sensor. In addition, it is possible to divide the compression period in consideration of the directionality and to calculate the displacement of the force value in the cycle, for example, the difference between the maximum value and the minimum value, and to calculate the depth value.

The operation method of the data processing apparatus can repeat the processes of steps 402 to 405 by a predetermined number of times or more. For example, the process from step 402 to step 405 may be repeated 30 times or more as indicated by reference numeral 406. [

Upon completion of the iteration of step 406, the method of operation of the data processing apparatus may acquire force-depth data (step 408) and calculate a force-depth linear prediction model based thereon (step 408).

FIG. 5 is a view for explaining the force-depth linear prediction model 500 calculated in FIG.

As shown in step 402 to step 405 of FIG. 4, two-dimensional training data is generated one by one every time the learning corresponding to the pressing progresses, and repeated until collecting a statistically sufficient number of data. As a result, the force-depth linear prediction model 500 ) Can be calculated. The force-depth linear prediction model 500 can be predicted from sufficient two-dimensional data by a linear regression analysis method, for example, least-squares method can be used.

In the subsequent stages of the training, the force-depth linear prediction model 500 corresponding to the corresponding manikin can be loaded and utilized at any time. Accordingly, the corresponding depth value can be obtained in real time only by the force value by the pressure sensor during the actual teaching using the force-depth linear prediction model 500. [

Also, by judging whether the sensed force value corresponds to 510 of the force-depth linear prediction model 500, it is easy to evaluate according to the compression standard according to the currently used CPR.

FIG. 6 is a flowchart illustrating a method of calculating an atmospheric pressure-respiration rate linear prediction model among prediction models according to an embodiment.

The first parameter corresponds to the air pressure applied to the object in association with the CPR, and the second parameter corresponds to the amount of air that one side of the object swells due to the air pressure. In this case, the prediction model may include a barometric pressure-volume linear prediction model.

In particular, the method of operation of the data processing apparatus may inject air within the target volume into the object in response to respiration 601 performed by the trainee (step 602).

Respiration for sample data acquisition may be performed in the form of actual breathing of an air injector or CPR specialist.

Accordingly, the operation method of the data processing apparatus measures the atmospheric pressure to the air injected into the object through the sensor (step 603). To measure atmospheric pressure, the data processing device uses a single air pressure sensor, which can be attached to the respiratory pack inside the manikin to measure the air pressure inside the respiratory pack.

The operation method of the data processing apparatus repeats the processes of steps 601 to 603 (step 605) to obtain the sample data, and obtains the atmospheric-pressure-volume data as a result of repeating 30 times or more (step 604).

Thereafter, the operation method of the data processing apparatus may calculate the barometric pressure-volume linear prediction model using the obtained barometric pressure-breathing volume data (step 606).

FIG. 7 is a view for explaining the barometric pressure-volume linear prediction model 700 calculated in FIG.

As in steps 601 to 603 of FIG. 6, two-dimensional training data is generated one by one every time learning corresponding to respiration proceeds, and it is possible to repeat until collecting a statistically sufficient number of data. As a result, the operation method of the data processing apparatus can calculate the barometric pressure-volume linear prediction model 700. The barometric pressure-volume linear prediction model 700 can be predicted from sufficient two-dimensional data by a linear regression analysis method, for example, a least square method and the like can be used. In addition, the reason for using the linear model in the breathing sensing is that it can reduce the amount of calculations and provide consistent breathing feedback when breathing by the user in real education.

In the execution stage of the training, the respiratory amount corresponding to only the air pressure collected by the air pressure sensor during actual education can be obtained in real time using the air pressure-volume linear prediction model 700.

According to the present invention, it is possible to reduce the process for calculating the result of CPR training, and to provide feedback in real time during CPR training. In addition, continuous and consistent data measurement is possible regardless of the manikin model in CPR training, and the rich feedback provided by precise and rapid value measurement can enhance the learning effect of CPR training.

The method according to an embodiment of the present invention can be implemented in the form of a program command which can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

While the invention has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. This is possible.

Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the equivalents of the claims, as well as the claims.

Claims

A first sensor for measuring a first parameter associated with CPR which is repeatedly performed on an object;
A second sensor associated with the CPR and measuring a second parameter; And
A processor for constructing a correlation between the first parameter and the second parameter as a prediction model,
To the data processing apparatus.

The method according to claim 1,
Wherein the first parameter corresponds to a pressure applied to the object in association with the CPR and the second parameter corresponds to an acceleration to which one side of the object is squeezed due to the pressure.

3. The method of claim 2,
Wherein,
And calculates a force value by calculating a displacement of the force value in the pressing cycle in consideration of the directionality of the pressure.

3. The method of claim 2,
Wherein,
Calculates a velocity by integrating the acceleration, calculates a depth by integrating the calculated velocity, and calculates a depth value based on a distance displacement according to the calculated depth.

The method according to claim 1,
Wherein the first parameter corresponds to the atmospheric pressure applied to the object in association with the CPR and the second parameter corresponds to the amount of breathing at one side of the object due to the atmospheric pressure.

A sensor for measuring a first parameter associated with CPR performed on the object; And
A processor for estimating a second parameter associated with the CPR using a predictive model constructed by learning a correlation between the first parameter and a second parameter related to CPR,
And a CPR training device.

The method according to claim 6,
Wherein the first parameter corresponds to a pressure applied to the object in association with the CPR and the second parameter corresponds to a depth value at which one side of the object is squeezed due to the pressure.

The method according to claim 6,
Wherein the first parameter corresponds to the atmospheric pressure applied to the object in association with the CPR and the second parameter corresponds to the amount of volume at which one side of the object swells due to the atmospheric pressure.

The method according to claim 6,
Wherein the CPR training device includes a patch type device detachable from one side of an object.

Measuring, at the sensor, a first parameter associated with CPR performed on the object; And
Estimating a second parameter associated with the CPR using a predictive model constructed by learning a correlation between the first parameter and a second parameter associated with CPR in a processing unit,
Gt; a < / RTI > method of operating a CPR training device.

11. The method of claim 10,
Wherein measuring the first parameter comprises:
Measuring pressure applied to the object in association with the CPR
Lt; / RTI >
Wherein estimating the second parameter comprises:
Estimating a depth value at which one side of the object is squeezed due to the measured pressure
Gt; a < / RTI > method of operating a CPR training device.

11. The method of claim 10,
Wherein measuring the first parameter comprises:
Measuring atmospheric pressure applied to the object in association with the CPR
Lt; / RTI >
Wherein estimating the second parameter comprises:
Estimating a volume of inflation of one side of the object due to the atmospheric pressure
Gt; a < / RTI > method of operating a CPR training device.

11. The method of claim 10,
Wherein the sensor comprises a patch-type device detachable at one side of the object.