KR102068585B1 - Device for measuring bone density - Google Patents

Abstract

According to an embodiment of the present disclosure, to realize the purpose of the present invention, disclosed is a bone density measuring device. The bone density measuring device comprises: a memory for storing user identification information including height, weight, age and gender of a user, and at least one pre-measured first impedance value; a measurement unit including two or more electrodes which can be in contact with at least a part of a body of the user, applying a voltage to the electrodes, and measuring a current corresponding to the applied voltage; and a control unit for determining a second impedance value for at least a part of the body of the user based on the voltage applied in the measurement unit and the current measured in the measurement unit, and generating a first bone density value based on the user identification information and the second impedance value. The control unit is capable of generating at least one second bone density value by using the user identification information and the at least one first impedance value, and generating first health information indicating a health problem based on the first bone density value and the at least one second bone density value.

Description

Bone Density Measurement Device {DEVICE FOR MEASURING BONE DENSITY}

The present disclosure relates to an apparatus for measuring bone density, and more particularly, to an apparatus for measuring bone density for generating health information by measuring a change in bone density value.

As the number of elderly people around the world increases and their income levels rise, so does their concern for health. In older people, diseases such as osteoporosis are increasing, and the demand for bone condition analysis is increased. In order to analyze various bone conditions, various methods such as X-ray absorption measurement, ultrasound, and clove computed tomography exist. Currently, X-ray absorption measurement is the most used to analyze the condition of bone.

However, the X-ray absorption measurement method requires a lot of expensive equipment, so there is a point that the public is not easy to access. Therefore, there is a demand for a bone density measuring apparatus that can easily measure the condition of bone such as osteoporosis and deliver useful information to the user.

Republic of Korea Patent No. 10-1567502 discloses a device for measuring bone density.

The present disclosure is devised in response to the above-described background, and an object of the present invention is to provide a device for measuring a bone density for generating health information by measuring a change in a bone density value.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

Disclosed is a bone density measuring apparatus according to an embodiment of the present disclosure for realizing the above problems. The apparatus for measuring bone density may include: a memory configured to store user identification information including height, weight, age, and gender of the user and at least one first impedance value measured; A measuring unit including two or more electrodes in contact with at least a part of a user's body, applying a voltage to the electrodes, and measuring a current corresponding to the applied voltages; Determine a second impedance value for at least a portion of the body of the user based on the voltage applied by the measurement unit and the current measured by the measurement unit, and based on the user identification information and the second impedance value A control unit for generating a first bone density value; The control unit may include: generating at least one second bone density value using the user identification information and the at least one first impedance value, and generating the at least one second bone density value to the first bone density value and the at least one second bone density value. Based on the health information, the first health information indicating the abnormality can be generated.

Alternatively, the control unit may be configured to: a preset threshold wherein a first variation value between the first bone density value and the at least one second bone density value is determined based on a plurality of second variation values between the at least one second bone density value When the value is exceeded, the first health information may be generated.

Alternatively, the controller may determine the maximum value among the plurality of second variation values as the preset threshold.

Alternatively, the controller may determine the preset threshold by assigning a preset weight to a maximum value among the plurality of second variation values.

Alternatively, the controller may determine the average value of the plurality of second variation values as the preset threshold.

Alternatively, the controller may determine the preset threshold by assigning a predetermined weight to an average value of the plurality of second variation values.

Alternatively, the controller may generate the first health information when the first bone density value is smaller than a minimum value among the at least one second bone density value.

Alternatively, the controller may generate the first health information when the first bone density value is smaller than a minimum value among the at least one second bone density value to which a predetermined weight is assigned.

Alternatively, when the first health information is generated, the controller may be configured to perform a re-measurement through the measurement unit to determine a third impedance value, and based on the user identification information and the third impedance value. The third bone density value may be generated, and the first health information may be verified based on the third bone density value.

Alternatively, the controller may include: at least one of recommended exercise information for the user, recommended dietary information for the user, and medical institution information based on the user's location information when the first health information is generated. The second health information can be generated.

Alternatively, the control unit may: transmit the second health information to a mobile terminal or a server of the user through a network unit.

Alternatively, the display unit may output the first health information.

Technical solutions obtained in the present disclosure are not limited to the above-mentioned solutions, and other solutions not mentioned above may be apparent to those skilled in the art from the following description. It can be understood.

The present disclosure can provide a device for measuring bone density.

Effects obtained in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned above may be clearly understood by those skilled in the art from the following description. .

Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to like components throughout. In the following examples, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect (s) may be practiced without these specific details.
1 illustrates an apparatus for measuring bone density according to an embodiment of the present disclosure.
2 illustrates an apparatus for measuring bone density according to another embodiment of the present disclosure.
3 illustrates an apparatus for measuring bone density according to another embodiment of the present disclosure.
4 is a block diagram of a bone density measuring apparatus according to an embodiment of the present disclosure.
5 is a diagram illustrating a first bone density value, a second bone density value, and a first change amount and a second change amount according to an embodiment of the present disclosure.
6 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.
7 is an exemplary view showing a bone density measurement model according to an embodiment of the present disclosure.
8 is an exemplary graph to which a bone density measurement model according to another embodiment of the present disclosure is applied.
9 is an exemplary graph to which a bone density measurement model according to another embodiment of the present disclosure is applied.
10 is a diagram illustrating an example of another bone density measurement model.
FIG. 11 is a block diagram illustrating logic for implementing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure.
12 is a block diagram illustrating a module for implementing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure.
FIG. 13 is a block diagram illustrating a circuit for implementing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure.
14 is a block diagram illustrating a means for implementing a method for generating health information by measuring a change in bone density value according to one embodiment of the present disclosure.
15 shows a brief general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it will be apparent that such embodiments may be practiced without these specific details.

As used herein, the terms “component”, “module”, “system” and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or the execution of software. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a processor and / or thread of execution. One component can be localized within one computer. One component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may be connected via a network such as the Internet and other systems via, for example, signals with one or more data packets (e.g., data and / or signals from one component interacting with other components in a local system, distributed system). Data may be communicated via local and / or remote processes.

In addition, the term “or” is intended to mean an implicit “or” rather than an exclusive “or”. In other words, unless specified otherwise or unambiguously in context, "X uses A or B" is intended to mean one of the natural implicit substitutions. That is, X uses A; X uses B; Or where X uses both A and B, "X uses A or B" may apply in either of these cases. Also, it is to be understood that the term "and / or" as used herein refers to and includes all possible combinations of one or more of the listed related items.

In addition, the terms "comprises" and / or "comprising" should be understood to mean that the corresponding features and / or components are present. It is to be understood, however, that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Also, unless otherwise specified or in the context of indicating a singular form, the singular in the specification and claims should generally be interpreted as meaning "one or more."

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be electronic hardware, computer software, or a combination of both. It should be appreciated that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in a variety of ways for each particular application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

In one embodiment of the present disclosure, the server may include other configurations for performing the server environment of the server. The server may include all types of devices. The server is a digital device, and may be a digital device having a computing power with a processor and a memory such as a laptop computer, a notebook computer, a desktop computer, a web pad, a mobile phone, and the like. The server may be a web server that handles services. The type of server described above is merely an example and the present disclosure is not limited thereto.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those of ordinary skill in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The invention should be construed in the broadest scope consistent with the principles and novel features set forth herein.

1 illustrates an apparatus for measuring bone density according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the bone density measuring apparatus 1000 may include a measuring unit 100. Here, the measuring unit 100 may include an electrode, and the electrode may be configured with two or more to measure impedance by applying a current to at least a part of the user's body. In addition, the electrode may provide the measured impedance to the controller 410 of the computing device 400.

The controller 410 of the computing device 400 may generate at least one of current and frequency signals. The controller 410 may generate a predetermined frequency signal. The frequency signal may be a multi-frequency signal having a frequency in a range of 1 kHz to 10 MHz. The controller 410 may generate a current to be applied to the current electrode based on the frequency signal. For example, the controller 410 may generate a signal having a frequency of 1kHz or more and 10MHz or less, and the signal generated by the controller 410 may be synthesized as a multi-frequency signal. For example, the controller 410 may generate a current to be applied to the current electrode based on the multi-frequency signal. The current generated by the controller 410 of the computing device 400 may have frequency characteristics of a predetermined frequency signal. For example, the current may have a multi-frequency characteristic like a predetermined frequency signal.

More specifically, the electrode according to an embodiment of the present disclosure may be arranged to be in contact with at least a portion of the user's body. In addition, the electrode may apply a current having a constant frequency to at least a part of the user's body. Accordingly, the electrode may measure an impedance of at least a part of the user's body.

For example, the electrode may be a left voltage electrode disposed on the bone density measuring apparatus 1000 so as to be in contact with the user's left wrist, a right voltage electrode to which the two fingers of the left current electrode and the right hand may contact, and a right current electrode. It may be configured as. In addition, the electrode may apply a current having a frequency of 50kHz to the left current electrode of the left wrist. The current may return to the right current electrode of the right hand via the left wrist, left arm, torso, right arm and right wrist. In addition, the electrode can measure the impedance of both the arm and the body portion of the user body through which the current is energized by measuring the voltage difference between the left voltage electrode of the left wrist and the right voltage electrode of the right hand when a 50 kHz current flows. The arrangement and operation of the electrode are only illustrative examples, and the present disclosure is not limited thereto.

According to one embodiment of the present disclosure, the electrode is composed of four or more, it is possible to apply a voltage having a plurality of frequencies to the user. More specifically, the electrode may be composed of a plurality of current electrodes and voltage electrodes, it is possible to energize a current having a plurality of frequencies. In addition, the impedance of at least a part of the user's body corresponding to various frequencies may be measured.

In addition, according to one embodiment of the present disclosure, the electrode is located on the base contact surface in contact with at least one of the palm and the sole of the user and protruding on the base contact surface, one or more close contact configured to contact between the user's fingers or toes It may include a contact surface.

More specifically, the basic contact surface may be arranged to be in contact with at least a part of the surface located in the palm of the user's palm region. Here, the hand region is a portion where five fingers are gathered and may mean a portion where the hand bones of each finger are located. In this case, the finger portion may mean the first node to the end node in contact with the palmar portion of each finger of the back of the hand. In addition, the close contact surface may include one or more units protruding from the basic contact surface, and may be disposed to contact at least a portion of the side of the hand and finger portion. Here, the side may include a portion between the fingers and the blade surface positioned between the upper surface of the back of the hand and the lower surface of the palm of the hand. Therefore, the close contact surface may be in contact with a relatively soft skin located between the user's fingers in addition to the general palm portion where the basic contact surface is in contact.

An electrode according to another embodiment of the present disclosure may include a basic contact surface and a close contact surface. In addition, the basic contact surface may be arranged to contact at least a portion of the toe portion and the foot portion located in the direction of the sole of the user. Here, the foot part may mean a forefoot part including a foot ball where five toes are gathered and the foot bones of each of the toes are located. In this case, the toe portion may mean the first to the end portion of the back of the foot in contact with the foot portion of each toe. In addition, the close contact surface may include at least one foot interlimb unit projecting on the basic contact surface, it may be arranged to contact at least a portion of the side of the foot portion and the toe portion. Here, the side may include a portion between each of the toes, the blade surface and the arch portion of the sole located between the upper surface of the instep direction and the lower surface of the sole direction. Therefore, the close contact surface may be in contact with a relatively soft skin located between the toes of the user in addition to the general sole portion where the basic contact surface is in contact.

In addition, the electrode may be made of a material that can be deformed based on the external pressure according to an embodiment of the present disclosure. More specifically, the basic contact surface and the close contact surface may be deformed based on the pressure provided by the user, and may be made of a material through which current can flow. In addition, the basic contact surface and the close contact surface may be composed of the same material or different materials.

For example, when the user provides pressure through an operation of holding the electrode with the palm of the hand, the electrode may be provided with the total azimuth pressure according to the grip force of the user. In addition, the basic contact surface and the close contact surface may be made of an aluminum alloy that can be transformed into a shape that allows the user to clench the fist most conveniently based on the total azimuth pressure. The description of the shape conversion of the above-described electrode is only an example, and the present disclosure is not limited thereto.

In addition, according to an embodiment of the present disclosure, a portion in which an external pressure exists may form a basic contact surface, and a portion in which the external pressure does not exist may be configured to form a close contact surface. More specifically, the basic contact surface may be formed to be contracted based on the form in which the user provides pressure. In addition, the close contact surface may be formed to expand or extend in response to the contraction of the basic contact surface.

For example, when a user stands on an electrode, the electrode may be provided with pressure according to the weight of the user. In addition, the basic contact surface may be customized to the shape of the sole of the user based on the shape of the sole to provide pressure. In addition, the close contact surface is not directly provided with pressure from the sole of the foot can be expanded by the volume of the contracted contact surface. Therefore, the interlimb unit existing between the toes of the user and included in the close contact surface may be extended. The description of the shape conversion of the above-described electrode is only an example, and the present disclosure is not limited thereto.

An electrode according to an embodiment of the present disclosure may be maintained at a body temperature of a user, or a preset temperature higher than the body temperature of the user. More specifically, the measurement unit may include a heat generating module (not shown) or a heat insulating material capable of providing heat to the electrode. Therefore, the electrode temperature of the user, or 0 ℃ ~ 10 ℃ than body temperature? It may be configured to maintain at least one of the higher temperatures. In addition, the contact surface may provide heat between the user's fingers, or between the toes.

Thus, the bone density measuring apparatus according to an embodiment of the present disclosure may measure impedance by contacting the skin between the toes of the palms of the dry, callused user, or the wet, callus-free fingers or the toes instead of the soles of the feet. In addition, the bone density measuring apparatus 1000 may reduce an error of measurement occurring at a place where dry and callus is high.

In addition, the electrode can be deformed to reflect the shape of the user's hand and foot by the pressure provided by the user, it can be fixed better. In addition, the electrode can be maintained at a temperature higher than the body temperature of the user may also have the effect of increasing the humidity by causing the sweat on the measurement site.

The bone density measuring apparatus 1000 may include a controller 410 of the computing device 400 generating bone density information based on the impedance measured by the measuring unit 100.

The impedance measurement value may be an impedance measurement value corresponding to at least a portion of a user's body measured through the electrode.

Basic contact surface according to an embodiment of the present disclosure may include an outer peripheral surface configured to be wrapped in the palm of the user. The close contact surface may include three interlocking units 231, 232, and 233 disposed at a predetermined angle on the outer circumferential surface.

More specifically, the electrode may be configured in a form that the user can wrap around the palm. In addition, the basic contact surface may be disposed on at least a portion of the outer circumferential surface of the electrode to at least partially contact the palm. In addition, the close contact surface may include three interdigital units 231, 232, and 233 configured to be disposed between four fingers from the user's index finger to the ring finger. Each of the three inter-units 231, 232, and 233 may be formed at a predetermined angle with the basic contact surface so as to be easily gripped by the user. For example, the three inter-units 231, 232, and 233 of the close contact surface may be formed at 80 °, 85 °, and 90 ° with the basic contact surface, respectively. The numerical description of the angle of the above-described interdental unit is merely an example, and the present disclosure is not limited thereto.

In addition, the three finger units 231, 232, and 233 may be configured to have a length equal to or greater than the average finger thickness of a person, and may be configured such that the middle portion is thinner than both ends. More specifically, the three spanning units may be configured to be at least as long as the average finger first segment thickness of people. In this case, the first node may be a node that first comes into contact with the palmar part of the user's palm. In addition, the three inter-gear units may be configured such that the middle portion is thinner than both ends so as to be in close contact with the user's finger, so that the fingers can be more easily fixed.

2 illustrates an apparatus for measuring bone density according to another embodiment of the present disclosure.

The close contact surface according to the exemplary embodiment of the present disclosure may include one thumb unit 234 configured to cover at least a portion of a portion between the user's thumb and the index finger.

More specifically, the electrode may be configured in a form that the user can wrap around the palm. In addition, the basic contact surface may be disposed on at least a portion of the outer circumferential surface of the electrode to at least partially contact the palm. In addition, the close contact surface may include one thumb unit 234 configured to be disposed between the user's thumb and index finger. In addition, one thumb unit 234 may be formed to cover at least a portion of the side between the user's thumb and index finger.

For example, as shown in FIG. 2, one thumb unit of the close contact surface may be formed of a circular metal disposed to protrude between the user's right thumb and the index finger. Therefore, when one user wraps the measurement unit 100 with his or her hand, the one thumb unit may be located on the side between the thumb and the index finger and may be in close contact with the user's finger, and may be more easily fixed. The description of one thumb unit described above is merely an example, and the present disclosure is not limited thereto. For example, one thumb unit may be configured to cover from the side of the thumb and index finger to the back of the hand.

Thus, the bone density measuring apparatus according to an embodiment of the present disclosure may measure impedance by contacting the skin between the toes of the palms of the dry, callused user, or the wet, callus-free fingers or the toes instead of the soles of the feet. In addition, the bone density measuring apparatus 1000 may reduce an error of measurement occurring at a place where dry and callus is high.

In addition, the electrode can be deformed to reflect the shape of the user's hand and foot by the pressure provided by the user, it can be fixed better. In addition, the electrode can be maintained at a temperature higher than the body temperature of the user may also have the effect of increasing the humidity by causing the sweat on the measurement site.

3 illustrates an apparatus for measuring bone density according to another embodiment of the present disclosure.

As shown in the figure, the bone density measuring apparatus 1000 may be a standing type bone density measuring apparatus including a measuring unit 100 and a biometric information generating unit. It may also be a bone density measuring device for measuring partial impedance of the upper body or the lower body.

More specifically, the standing bone density measuring apparatus, as shown in the figure, based on the impedance value for the measuring unit 100 in contact with a part of the user's body and the part of the user's body measured by the measuring unit 100 The controller 410 of the computing device 400 may generate the bone density information.

Measuring unit 100 according to an embodiment of the present disclosure may include two or more electrodes for measuring the impedance by applying a current to at least a portion of the user's body. In addition, the electrode may include a basic contact surface and a close contact surface. Here, the basic contact surface may be configured to contact at least one of the palm and the sole of the user, and the close contact surface may be configured to contact at least one of the finger and the sole of the user.

According to another embodiment of the present disclosure, the bone density measuring apparatus 1000 may include a camera unit (not shown) for photographing at least a part of a user's body. The camera unit may photograph at least a part of the user's body. The controller 410 may receive an image image of at least a part of the user's body from the camera unit, and recognize the body joint of the user based on the image image. The controller 410 may recognize a body joint of the user through an image processing algorithm of an image of the body of the user. The image processing algorithm may include canny edge detection, Harris corner detection, and the like, but the present disclosure is not limited thereto. Through Canny Edge Detection, the computing device blurs the image to remove noise, detects edges using mask edges, removes Non-Maximum Values, and connects the edges by dividing the size with double thresholds. By this, the edge can be extracted. The controller 410 may recognize a body joint of the user based on the angle of the edge of the image of the body of the user. The controller 410 may determine a portion where the inclination of the edge of the image photographing the user's body changes by more than a predetermined value as the portion where the joint of the user is located. The controller 410 may calculate the length of a part of the user's body based on the portion of the user's body where the joint is located. The length of the body part of the user calculated by the controller 410 may be one item value of the biometric data of the bone density measurement model described later.

According to another embodiment of the present disclosure, the bone density measuring apparatus 1000 may further include a height measuring frame (not shown), a sliding member (not shown), a movable bar (not shown), and an acceleration sensor (not shown). . The elongation measuring frame may be formed as, for example, a U-shaped member in the bone density measuring apparatus 1000 so that the sliding member is lifted and / or lowered up and down. The opening of the elongation measuring frame faces the front side, and a part of the sliding member is inserted into the opening to move through the lifting and / or lowering operation. The sliding member may be installed to move up and down along the elongation measuring frame. The movable bar may protrude to the front side of the stretch measurement frame. The movable bar may be in contact with a location corresponding to an upper one area of the user's body. For example, the movable bar can be in contact with the user's head. An acceleration sensor may be provided at a portion of the lower side of the movable bar in order to recognize a point of time when the movable bar contacts a position corresponding to an upper one area of the user's body. The acceleration sensor may recognize that the movable bar contacts the user's head when the movable bar contacts the user's head so that the change in acceleration is greater than or equal to a predetermined value. The controller 410 may derive the measured value as the height value of the user when the mobile bar recognizes contact with a position corresponding to the upper one area of the user's body. The controller 410 may derive the user's height value based on the distance the movable bar moves from the time when the movable bar starts to drive to the time when the movable bar recognizes contact with a position corresponding to the upper one area of the user's body. . The controller 410 subtracts the distance that the movable bar moves from the time when the movable bar starts to drive in the length of the height measurement frame to the time when the movable bar recognizes contact with a position corresponding to the upper one area of the user's body. The value can be derived. The height of the user calculated by the controller 410 may be one item value of the biometric data of the bone density measurement model described later.

4 is a block diagram of a bone density measuring apparatus according to an embodiment of the present disclosure. 5 is a view for explaining a first bone density value, a second bone density value, a first change amount and a second change amount according to an embodiment of the present disclosure.

The configuration of the computing device 400 shown in FIG. 4 is merely an example for simplicity. In one embodiment of the present disclosure, the computing device 400 may include other components for performing the computing environment of the computing device 400. The computing device 400 may be a terminal or a server. Computing device 400 may include any type of device. The computing device 400 may be a digital device, and may be a digital device having a computing power including a processor and having a memory such as a laptop computer, a notebook computer, a desktop computer, a web pad, a mobile phone, and the like. The computing device 400 may be a web server that processes bone density measurements.

In the present specification, the network function may be used interchangeably with an artificial neural network and a neural network. In this specification, the network function may include one or more neural networks, in which case the output of the network function may be an ensemble of the outputs of the one or more neural networks.

In this specification, the model may include a network function. The model may include one or more network functions, in which case the output of the model may be an ensemble of outputs of one or more network functions.

The computing device 400 may include a control unit 410, a network unit 420, a memory 430, a display unit 440, a user input unit 450, and a measurement unit 100.

The controller 410 may include one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. and a processor for data analysis and deep learning. The controller 410 may read a computer program stored in the memory 430 to perform a method for measuring bone density according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the controller 410 may perform calculation for learning a neural network. The controller 410 may be configured to process neural networks such as processing input data for learning in deep learning (DN), feature extraction from input data, error calculation, and weight update of neural networks using backpropagation. You can perform calculations for learning.

At least one of the CPU, the GPGPU, and the TPU of the controller 410 may process training of the model. For example, the CPU and GPGPU can work together to train the model and compute bone density data using the model. In addition, in an embodiment of the present disclosure, a processor of a plurality of computing devices may be used together to process training of a model and calculation of bone density data through a bone density measurement model. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, a GPGPU, or a TPU executable program.

In an embodiment of the present disclosure, the computing device 400 may distribute and process the model by using at least one of the CPU, the GPGPU, and the TPU. In addition, in an embodiment of the present disclosure, the computing device 400 may distribute and process the model along with other computing devices.

A method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure will be described.

The apparatus for measuring bone density according to the present disclosure may generate health information indicating whether there is a health abnormality by comparing a bone density value measured in the past of the user with a bone density value currently measured. For example, the apparatus for measuring bone density according to the present disclosure may determine whether there is an abnormality in health when the currently measured bone density value is lower than the bone density value measured in the past, or when the large change amount is measured compared to the change amount between the bone density values measured in the past. The alert may generate health information. Hereinafter, the operation of the controller 410 for measuring the change in the bone density value to generate health information will be described in detail.

The controller 410 determines a second impedance value of at least a part of the user's body based on the voltage applied by the measurement unit and the current measured by the measurement unit, and based on the user identification information and the second impedance value, Bone density values can be generated. As described above, the measuring unit 100 may apply a voltage to at least a portion of the user's body using two or more electrodes to measure a current corresponding to the applied voltage. The controller 410 may determine the second impedance value by using the voltage value applied by the measurement unit and a current value corresponding to the applied voltage. Here, the second impedance value may be an impedance value for generating a bone density value of the current user.

The controller 410 may generate the first bone density value using the user identification information and the second impedance value. The first bone density value may mean a bone density value of a user to be measured at present, and as described above, the first bone density value may be determined using a second impedance value measured at the current measurement unit. In detail, the controller 410 may calculate the first bone density value using the user identification information including the height, weight, age, and gender of the user and the second impedance value.

Here, the controller 410 may receive user identification information through the user input unit 450. In addition, the controller 410 may use user identification information stored in the memory 430. In addition, the controller 410 may receive user identification information from the outside through the network unit 420. However, the present invention is not limited thereto.

The controller 410 may generate a past bone density value for comparison with the currently measured bone density value (ie, the first bone density value). In detail, the controller 410 may generate at least one second bone density value using the user identification information and the at least one first impedance value. Here, the second bone density value may mean a user's bone density value calculated based on the impedance value measured in the past. For example, the controller 410 may store the measured impedance value in the memory 430, and the controller 410 may use the impedance values stored in the memory 430 to generate the second bone density value. . The user identification information may be obtained by various methods as described above. For example, the controller 410 may generate the second bone density value using the user identification information stored in the memory. However, the present invention is not limited thereto.

The controller 410 may generate first health information indicating a health abnormality based on the first bone density value and the at least one second bone density value. Here, the first health information may be information indicating that there may be an abnormality in the health of the user due to a change in the bone density value. Hereinafter, a method of generating, by the controller 410, first health information indicating health abnormality based on the first bone density value and the at least one second bone density value will be described with reference to FIG. 5.

Referring to FIG. 5A, seven second bone density values 710 to 780 and a first bone density value 790 are shown. (B) of FIG. 5 shows the amount of change of the values shown in (a) of FIG. Specifically, FIG. 5B illustrates a first variation 880 between the first bone density value 790 and the at least one second bone density value 710 to 780 and the at least one second bone density value 710 to 780. A plurality of second variation values 810-870 are shown.

The first variation value may mean a deviation between any one of the first bone density value and the second bone density value. For example, referring to FIG. 5B, the first variation 880 is the second bone density value 780 measured at the closest time to the first bone density value 790 and the first bone density value 790. ) Can mean a deviation between. However, the present invention is not limited thereto, and the first variation value may be variously determined.

The second variation value may mean a deviation between any two values of the second bone density values 710 to 780. For example, the second variation value 810 may be a deviation between any one second bone density value 720 and the second bone density value 710 measured before. In addition, the second variation 840 may be a deviation between the second bone density value 750 and the second bone density value 740 measured before. However, the present invention is not limited thereto, and the second variation value may be variously determined.

According to an embodiment of the present disclosure, the controller 410 may generate the first health information when the first variation value between the first bone density value and the at least one second bone density value exceeds a preset threshold. have. Here, the predetermined threshold value may be determined based on the plurality of second variation values calculated by at least three second bone density values. Specifically, the plurality of second variation values, when the second bone density value is three, the first deviation value and the second second bone density value and the third bone density value, which are deviations between the first second bone density value and the second second bone density value, respectively. It may include a second deviation value that is a deviation. For example, the controller 410 may generate the first health information when the first variation 880 exceeds a preset threshold, where the controller 410 sets a plurality of threshold values. 2 It can be determined using the maximum value and average value of the variation value.

The controller 410 may determine a maximum value among the plurality of second variation values as a preset threshold value. For example, referring to FIG. 5B, among the plurality of second variation values 810 to 870, the controller 410 may determine the second variation value 860, which is the maximum value, as a preset threshold. have. In this case, the controller 410 may generate the second health information when the first variation value is larger than the second variation value 860 which is the maximum value. As another example, the controller 410 may determine a preset threshold by assigning a preset weight to a maximum value among the plurality of second variation values. For example, when the preset weight is 1.2, the controller 410 may determine a value obtained by multiplying the second variation value 860, which is the maximum value, by 1.2, as the preset threshold value. The disclosed preset weights are merely examples, and do not limit the embodiments of the present invention.

The controller 410 may determine an average value among the plurality of second variation values as a preset threshold value. For example, referring to FIG. 5B, the controller 410 may determine an average value of the plurality of second variation values 810 to 870 as a preset threshold value. Accordingly, the controller 410 may generate the second health information when the first variation value is greater than the average value of the plurality of second variation values. As another example, the controller 410 may determine a preset threshold by assigning a preset weight to an average value of the plurality of second variation values. For example, when the preset weight is 2, the controller 410 may determine a value obtained by multiplying an average value of the plurality of second variation values by 2 as a preset threshold value. The disclosed preset weights are merely examples, and do not limit the embodiments of the present invention.

According to an embodiment of the present disclosure, the controller 410 may generate the first health information when the first bone density value is smaller than the minimum value among the at least one second bone density value. For example, referring to FIG. 5A, the controller 410 may determine the first health information when the first bone density value 790 is smaller than the minimum value 770 among the second bone density values 710 to 780. Can be generated. As another example, the controller 410 may generate the first health information when the first bone density value is smaller than the minimum value of the second weighted bone density value. For example, referring to FIG. 5A, when the preset weight is 0.9, the controller 410 determines that the first bone density value 790 is multiplied by 0.9 to the minimum value 770 of the second bone density value. In the smaller case, the first health information can be generated. The disclosed preset weights are merely examples, and do not limit the embodiments of the present invention.

According to an embodiment of the present invention, when the first health information is generated, the controller 410 determines the third impedance value by performing re-measurement through the measurement unit, and based on the user identification information and the third impedance value. The third bone density value may be generated, and the first health information may be verified based on the third bone density value. For example, when the first health information is generated, the controller 410 may perform re-measurement to verify the first health information. In this case, the controller 410 may determine the third impedance value by performing re-measurement through the measurement unit. In addition, the controller 410 may determine the third bone density value using the third impedance value and the user identification information which are the re-measured values. The controller 410 may verify the first health information by comparing the third bone density value with the second bone density value. For example, the controller 410 determines that the first health information is reliable information when the deviation between the third bone density value and the second bone density value is equal to or less than a preset ratio (for example, 5% or less of the second bone density value). You can decide. However, the present disclosure is not limited thereto, and the controller 410 may verify the first health information based on the third bone density value in various ways. Therefore, the bone density measuring apparatus according to the present invention can prevent the user from providing wrong health information.

According to an embodiment of the present invention, when the first health information is generated, the controller 410 may include at least one of recommended exercise information for the user, recommended diet information for the user, and medical institution information based on the user's location information. It may generate a second health information comprising a. For example, the controller 410 may generate second health information when the first health information is generated, and the second health information may include information for improving health of the user through bone density management. For example, the second health information may include information about nearby medical institutions determined by the recommended exercise information, the recommended diet, and the location information of the user, but is not limited thereto. In addition, the controller 410 may transmit the generated second health information to the mobile terminal or the server of the user through the network unit 420. Thus, the user can easily check the transmitted second health information through the mobile terminal or the server.

Hereinafter, a method for measuring bone density according to an embodiment of the present disclosure will be described.

A method of generating a bone density measurement model including one or more network functions for calculating bone density data according to an embodiment of the present disclosure will be described.

일 수 있다. 전술한 관계 정보에 대한 기재는 예시일 뿐이며, 본 개시는 이에 제한되지 않는다. 학습 골밀도 데이터는 사용자의 뼈의 강도와 관련한 데이터일 수 있다. 예를 들어, 학습 골밀도 데이터는 사용자의 골밀도 값, 사전결정된 사용자 그룹의 골밀도와 비교한 지수, 상기 골밀도 데이터의 사용자와 동일한 연령대의 사용자 그룹의 골밀도와 비교한 지수, 상기 골밀도 데이터의 사용자와 동일한 성별의 사용자 그룹의 골밀도와 비교한 지수, 상기 골밀도 데이터의 사용자와 동일한 인종 그룹의 골밀도와 비교한 지수, T-값 및 Z-값 중 적어도 하나를 포함할 수 있다. T-값은 동일한 성별에서 젊은 성인 집단의 평균 골밀도와 비교하여 표준편차로 나타낸 값을 포함할 수 있다. T-값은 (환자의 측정 골밀도 값 - 젊은 집단의 평균 골밀도 값)에 기초하여 표준편차로 나타낸 값을 포함할 수 있다. T-값은 골절에 관한 절대적인 위험도를 나타내는 값일 수 있다. Z-값은 동일한 연령대의 골밀도와 평균 골밀도를 비교하여 표준편차로 나타낸 값을 포함할 수 있다. Z-값은 (환자의 측정 골밀도 값 - 동일 연령 집단의 평균 골밀도 값)에 기초하여 표준 편차로 나타낸 값을 포함할 수 있다. 예를 들어, 학습 골밀도 데이터는 폐경 이후의 여성 또는 50세 이상의 남성에 대하여 측정한 경우 T-값일 수 있고, 소아, 청소년, 폐경 전 여성 또는 50세 이전 남성에 대하여 측정한 경우 Z-값일 수 있다. 전술한 학습 생체 데이터 및 학습 골밀도 데이터에 대한 기재는 예시일 뿐이며, 본 개시는 이에 제한되지 않는다.The controller 410 may acquire the training data set by using the network unit 420. The training data set may include training biometric data and training bone density data of the entire customer. The learning biometric data may be data relating to the health of the user. The training biometric data may include one or more items having respective item values. For example, the learning biometric data may include at least one of height, weight, age, gender, multi-impedance, and relationship information. Height, weight, age and gender may be referred to herein as user identification information. The multi-impedance may be an impedance measurement value corresponding to at least a part of the user's body measured by the measuring unit. The multi-impedance may include a first impedance value or a second impedance value. The multi-impedance is at least a portion of the user's body for measuring the multi-path impedance and the impedance for at least a portion of the user's body is an impedance associated with the path of current flow from one of the two or more electrodes to the other electrode It may include a multi-frequency impedance which is one or more frequency-specific impedance of the voltage applied. The multi-impedance may be a value including a phase angle and reactance. The multi-frequency impedance may be an impedance measured by flowing a plurality of currents having a plurality of frequencies through the user's body. For example, multipath impedance is the impedance of the user's body associated with the path of current flow from the electrode in contact with the left hand to the electrode in contact with the right hand, and the path of current flowing between the electrode in contact with the right hand and the electrode in contact with the left foot. And an impedance related to a user's body associated with the same. That is, the multipath impedance may include impedances for each of the paths through which a current flows along the user's body between the electrodes of the BMD 1000. For example, the multi-frequency impedance may include an impedance of a user's body when a current of 50 kHz flows by a voltage applied to at least a portion of the user's body and an impedance of a current of 30 Hz. The above description of the multi-impedance is only an example and the present disclosure is not limited thereto. The relationship information may be information about a relationship between at least two factors among factors other than the relationship information in the learning biometric data. For example, the relationship information may be information about a relationship between weight and multi-impedance among factors of the learning biometric data. For example, relationship information

Can be. The above description of the relationship information is only an example, and the present disclosure is not limited thereto. The learning bone density data may be data relating to the strength of the user's bones. For example, the learning bone density data may include a bone density value of a user, an index comparing with a bone density of a predetermined user group, an index comparing with a bone density of a user group of the same age group as a user of the bone density data, and a gender equal to a user of the bone density data. It may include at least one of the index compared with the bone density of the user group of, the index compared with the bone density of the same ethnic group as the user of the bone density data, T-value and Z-value. T-values can include values expressed as standard deviations as compared to the average BMD of a young adult population at the same gender. T-values can include values expressed as standard deviations based on (measured bone density values in patients-average bone density values in younger populations). The T-value may be a value that represents an absolute risk for fracture. The Z-value may include a value expressed as a standard deviation by comparing bone density and mean bone density of the same age group. Z-values can include values expressed as standard deviations based on (measured bone density values of patients-average bone density values of the same age group). For example, the learning bone density data may be a T-value when measured for postmenopausal women or men 50 years or older, and may be a Z-value when measured for children, adolescents, premenopausal women, or men 50 years or older. . The description of the above-described learning biometric data and learning bone density data is only an example, and the present disclosure is not limited thereto.

)를 학습 데이터의 입력으로 하고, 상기 사용자 A의 학습 골밀도 데이터(본 예시에서, T-값 -0.1)를 학습 데이터의 라벨로 하여 학습 데이터를 생성할 수 있다. 전술한 학습 데이터의 생성에 대한 기재는 예시일 뿐이며 본 개시는 이에 제한되지 않는다.The controller 410 may generate the learning data by labeling the learning bone density data of the user on the learning biometric data of the user included in the learning data set. The controller 410 may generate learning data using the learning biometric data of the user as input of the learning data and the learning bone density data of the user as the label of the learning data. For example, user A's learning biometric data (in this example, 60 years old, 164 cm, female, 53 kg, 700 Ω,

) Can be used as input of the training data, and the training data can be generated using the training bone density data of the user A (T-value -0.1 in this example) as the label of the training data. The description of the generation of the above-described learning data is only an example and the present disclosure is not limited thereto.

When there is a missing item among one or more items of the user's learning biometric data, the controller 410 may set an item value of the missing item as an intermediate value or an average value of the learning data set. The controller 410 may delete the column for the corresponding item when the data for one item of the biometric data of the users of the learning biometric data set is insufficient. For example, if there is no data of an item for a key among the items of user B's learning biometric data, for a missing item for a user B's key, for a user's height of all users of the same gender and age, The item value for the missing item can be supplemented with an average value of 173 cm. For example, when the data on the weight of the items of the learning biometric data is present in the learning biometric data of the user more than a predetermined ratio among all the users, the column for the corresponding item (weight in this example) may be deleted. The description of the item value supplement of the above-described missing item is only an example, and the present disclosure is not limited thereto.

The controller 410 may generate a bone density measurement model including one or more network functions. The controller 410 may generate a bone density measurement model including one or more network functions including at least one of an input layer, one or more hidden layers, and an output layer. The controller 410 may generate a network function composed of an input layer including one or more input nodes. The controller 410 may generate a network function such that the hidden layer included in the network function includes one or more hidden nodes. The controller 410 may generate a network function such that an output layer included in the network function includes one or more output nodes. The controller 410 may generate each node included in the layer of the network function to be connected to one or more nodes of another layer through a link. Each weight may be set to each link.

The controller 410 may input learning biometric data of the training data as an input of the bone density measurement model. The controller 410 may input the learning biometric data to one or more input nodes included in the input layer of the bone density measurement model. The training biometric data may include one or more items. The item may mean each piece of data related to a user's health. The controller 410 may input the items of the learning biometric data to each of one or more input nodes included in the input layer of the bone density measurement model. For example, if five nodes are included in the input layer of the bone density measurement model, the controller 410 sets five items of the five items of the learning biometric data, such as height, weight, age, gender, and multi-impedance, respectively. You can type in For example, the controller 410 may include a first item (in this example, item height 156 cm) in the first node included in the input layer of the bone density measurement model, and a second item (in this example, item weight 43 kg in the second node). ), The third node has a third item (in this example, item age 17), the fourth node has a fourth item (in this example, item gender female) and the fifth node has a fifth item (in this example, item Multi impedance 800 Ω) can be input. In another example, the input layer of the bone density measurement model may further receive a plurality of bio multi-impedances. For example, the description of the input of the above-described bone density measurement model is only an example, and the present disclosure is not limited thereto.

The controller 410 may generate a bone density measurement model including an input layer, one or more hidden layers, and an output layer. The layer can include one or more nodes. Nodes can be connected via links with other nodes. The controller 410 may calculate an item input to an input node of an input layer of the bone density measurement model through a link connected to the input node and propagate it to the hidden layer. The operation can include any mathematical operation. For example, the operation may be a product or a compound product, but the foregoing description is merely an example and the present disclosure is not limited thereto. The controller 410 may calculate an item input to an input node of the bone density measurement model through a link connected to the input node and propagate it to the output layer through one or more hidden layers. The controller 410 may generate bone density data based on a value propagated to the output layer of the bone density measurement model.

The control unit 410 may determine the first node value of the first node of the bone density measurement model, the second node value of the second node included in the previous layer connected to the first node, the second node included in the previous layer, and the It can be derived by calculating the first link weight of the link set in the link connecting the first node. The controller 410 calculates the first node value of the first node of the bone density measurement model by using the second link weight set on the link connecting the third node included in the next layer connected to the first node and propagates to the third node. can do.

The controller 410 inputs each item value of an item included in the training biometric data of the training data into one or more input nodes included in the input layer of the bone density measurement model, and generates an output layer of the bone density measurement model in order to generate a bone density measurement model. The error can be calculated by comparing the bone density data (that is, output) calculated in step 2 with the learning bone density data (ie, correct answer). The controller 410 may adjust the weight of the bone density measurement model based on the error. The controller 410 may update the weights set for the respective links by propagating from the output layer included in the one or more network functions of the bone density measurement model through the one or more hidden layers to the input layer based on the error.

In generating the bone density measurement model, the controller 410 may set the dropout so that a part of the output of the hidden node is not transmitted to the next hidden node in order to prevent overfitting.

The training epoch inputs training biometric data to each of one or more input nodes included in the input layer of one or more network functions of the bone mineral density model, and labels the training biometric data with respect to all the training data included in the training data set. The derived learning bone density data (i.e., the correct answer) and the bone density data (i.e., the output) of the bone density measurement model to derive an error, and the derived error from one or more hidden layers from the output layer of one or more network functions of the bone density measurement model. By propagating through the input layer, the weights set in the respective links may be updated. That is, when the operation using the bone density measurement model and the weight update process for the bone density measurement model are performed on all the training data included in the training data set, the training data may be 1 epoch.

In generating the bone density measurement model, the controller 410 may set the learning rate of the bone density measurement model to a predetermined value or more when the learning epoch for learning the bone density measurement model is less than or equal to a predetermined epoch. In generating the bone density measurement model, the controller 410 may set the learning rate of the bone density measurement model to be equal to or less than a predetermined value when the learning epoch for learning the bone density measurement model is greater than or equal to a predetermined epoch. The learning rate may mean an update degree of weights. For example, in the early stage of learning, the learning rate can be set high (ie, the weighting degree of the update is large), so that the output of the learning data can quickly approach the label of the learning data. For example, later in the training, you can set the learning rate low (that is, by making the weight update smaller) to reduce the error between the output of the training data and the label of the training data (that is, to improve accuracy). can do. The above disclosure about the learning rate is only an example, and the disclosure is not limited thereto.

The controller 410 may perform learning of the bone density measurement model after a predetermined epoch or more, and determine whether to stop learning using the verification data set. The predetermined epoch may be part of the overall learning goal epoch. The controller 410 may set a part of the training data set as the verification data set. The verification data is data corresponding to the learning data, and may be data for determining whether to stop learning. After the training of the bone density measurement model is repeated over a predetermined epoch, the controller 410 may determine whether the learning effect of the bone density measurement model is greater than or equal to a predetermined level using the verification data. For example, the controller 410 uses 1000 pieces of validation data after performing 10000 times of repetitive learning, which is a predetermined epoch, when 100 million times of learning data is used to perform a target repetition learning. 10 repetitive learning (ie, 10 epochs), and when the change of neural network output is less than or equal to a predetermined level during the 10 repetitive learning, it may be determined that further learning is meaningless and the learning may be terminated. That is, the verification data may be used to determine the completion of the learning based on whether the effect of the epoch-specific learning in the repetitive learning of the neural network is above or below a certain level. The above-described learning data, the number of verification data, and the number of repetitions are only examples, and the present disclosure is not limited thereto.

The controller 410 may set at least one of the training data sets as a test data set. The test data is data corresponding to the training data, and may be data used to verify performance after the training of the model is completed. The controller 410 may set one or more biometric data and bone density data labeled in the biometric data as a test data set. The controller 410 inputs the biometric data included in the test data set into the bone density measurement model, compares the output from the bone density measurement model with the labeled bone density data, and calculates a correct answer rate of the bone density measurement model for the test data set. You can judge. The controller 410 inputs the learning biometric data of the user included in the test data into the bone density measurement model, and outputs the bone density data (ie, output) output from the bone density measurement model and the learning bone density of the user included in the test data. By comparing the data (ie, the correct answer), the activation of the bone density measurement model can be determined if the error is less than or equal to a predetermined value. The controller 410 compares the bone density data (ie, output) output from the bone density measurement model with the learning bone density data of the user included in the test data (ie, the correct answer), and when the error is greater than or equal to a predetermined value, the bone density Training of the measurement model can be performed further above a predetermined epoch or deactivated the bone density measurement model. When the controller 410 deactivates the bone density measurement model, the controller 410 may discard the bone density measurement model. The controller 410 may determine the performance of the generated bone density measurement model based on factors such as accuracy, precision, recall, and the like. The foregoing performance evaluation criteria are only examples and the present disclosure is not limited thereto. According to an exemplary embodiment of the present disclosure, the controller 410 may independently generate one or more bone density measurement models by independently learning one or more network functions included in each bone density measurement model, and evaluate the performance of only the neural network having a predetermined performance or more. It can be used for measuring bone density.

According to an embodiment of the present disclosure, a bone density measurement model may be generated based on learning biometric data and learning bone density data of a user.

A method of outputting bone density data using a trained bone density measurement model according to an embodiment of the present disclosure will be described.

=

)를 생성할 수 있다. 예를 들어, 제어부(410)는 메모리(430)에 저장된 사용자A의 키, 체중, 나이 및 성별에 대한 정보와 측정부(100)를 이용하여 상기 사용자A의 멀티 임피던스 항목 값을 입력 받을 수 있다. 전술한 생체 데이터에 대한 기재는 예시일 뿐이며, 본 개시는 이에 제한되지 않는다.The controller 410 may receive biometric data including one or more items by using the network unit 420. The controller 410 may input biometric data including one or more items stored in the memory 430 into a bone density measurement model. The biometric data may correspond to the training biometric data. The biometric data may be data relating to the health of the user. For example, the biometric data may include at least one of height, weight, age, gender, multi-impedance, and relationship information. The height, weight, and gender may be referred to as user identification information, and the multi-impedance may include a first impedance value and a second impedance value. The controller 410 may receive data obtained by measuring the multi-impedance items included in the biometric data of the user using the measurement unit 100. The controller 410 may generate the relationship information by using two or more item values among other item values except the relationship information of the biometric data. For example, the network unit 420 may receive a value input of a key, weight, age, and gender transmitted through the user terminal and transmit the received value to the controller 410. The controller 410 may receive an input of an item value for an item included in the biometric data for the user through the input unit. For example, the controller 410 receives items of biometric data by receiving a height, weight, age, and gender of a user through an input unit (not shown) such as a touch panel or a button included in the bone density measuring apparatus 1000. can do. For example, the controller 410 may receive data of a user 178cm tall, 84kg in weight, age 31, and male by using the network unit 420, and the controller 410 may use the measurement unit 100. The user A may receive a value of the multi-impedance item (650Ω in this example). For example, the control unit 410 may be an item value for the age input through the touch panel (31 years old in this example) and an item value for the multi-impedance input using the measurement unit 100 (in this example, 5 kΩ) using the relationship information (in this example,

=

) Can be created. For example, the controller 410 may receive the multi-impedance item value of the user A by using information about the height, weight, age, and gender of the user A stored in the memory 430 and the measurement unit 100. . The above description of the biometric data is only an example, and the present disclosure is not limited thereto.

The controller 410 may input the biometric data of the user into the learned bone density measurement model. The controller 410 may input each item included in the biometric data of the user to each node included in the input layer of the bone density measurement model. The controller 410 calculates each item by using the weight of the link connected to the input layer and delivers the items to each of the one or more hidden nodes included in the hidden layer. The operation may include any mathematical algorithm. For example, the calculation may include a product, a compound product, and the like, but the above description is only an example and the present disclosure is not limited thereto. The controller 410 may calculate and propagate biometric data including one or more items input to the input layer to the output layer via one or more hidden nodes. The controller 410 may calculate the value of each hidden node included in the hidden layer using the weights of the links connected to the hidden node and propagate it to the hidden node included in the other hidden layer or the output node included in the output layer. . The learned bone density measurement model may be a neural network learned by a method for generating a bone density measurement model according to an embodiment of the present disclosure described above.

The controller 410 may generate bone density data based on the output of the output layer included in the learned bone density measurement model. The bone density data may be data relating to the strength of the user's bones. For example, the bone density data may include a bone density value of a user, an index comparing with a bone density of a predetermined user group, an index comparing with a bone density of a user group of the same age group as the user of the bone density data, and a gender equal to the user of the bone density data. It may include at least one of the index compared with the bone density of the user group, the index compared with the bone density of the same ethnic group as the user of the bone density data, T-value and Z-value. T-values can include values expressed as standard deviations as compared to the average BMD of a young adult population at the same gender. Z-value means the difference from the mean BMD of BMD in the same age group. For example, bone density data may be a T-value when measured for postmenopausal women or men 50 years of age or older, and may be a Z-value when measured for children, adolescents, premenopausal women, or men 50 years of age or older. For example, the controller 410 may generate bone density data having a T-value of −1.5 based on the biometric data input to the learned bone density measurement model. The description of the above-described bone density data is only an example, and the present disclosure is not limited thereto.

The controller 410 may generate health risk information based on the bone density data. Health risk information may mean the probability of occurrence for health risks that may occur in the body. For example, the health risk information may be a probability of developing osteoporosis. For example, the controller 410 may generate health risk information as data or a graph. For example, the controller 410 may diagnose osteoporosis when the T-value is greater than or equal to -0.1 and normal, when the T-value is between -0.1 to -2.5, and when the T-value is lower than or equal to -2.5. For example, the controller 410 may diagnose osteoporosis when the T value of the female patient in her 70s is -3.0. For example, if the T value of the female patient in their 70s is -3.0, the control unit 410 generates health risk information by displaying a dot on a graph so as to know the position of the patient in the BMD distribution of the adult female group in their 30s. can do. The above description of the generation of the health risk information is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, bone density data may be generated by using biometric data of a user as an input of a bone density measurement model.

Hereinafter, a method for measuring bone density according to another embodiment of the present disclosure will be described.

A method of generating a bone density measurement model including a regression equation for calculating bone density data according to another embodiment of the present disclosure will be described.

The controller 410 may acquire the training data set by using the network unit 420. The training data set may include training biometric data and training bone density data of the entire customer. The training data set may include learning biometric data of all customers as an independent variable for regression analysis, and include learning bone density data of all customers as a dependent variable for regression analysis. The learning data may include the learning biometric data of the customer as an independent variable for regression analysis, and may include the learning bone density data of the customer matching the learning biometric data of the customer as a dependent variable for the independent variable. . The training biometric data may include one or more items having respective item values as described above. As described above, the learning bone density data may include a bone density value of a user, an index comparing with a bone density of a predetermined user group, an index comparing with a bone density of a user group of the same age as the user of the bone density data, and a gender equal to the user of the bone density data. It may include at least one of the index compared to the bone density of the user group of, the index compared to the bone density of the same ethnic group as the user of the bone density data, T-value and Z-value. To improve the predictability of the regression equation, you can include only customers who meet one or more constraints on the customers of your training data set. For example, a constraint might be a condition such that the proportion of males and females in the training data set is equal to or greater than 20, or that a customer with a T-score value of zero or less in bone density is more than a customer with a T-score value of 0 or more. It may include. The description of the foregoing constraints is merely an example, and the present disclosure is not limited thereto.

일 수 있다. 전술한 회귀식의 독립 변수는 다른 독립 변수 항목이 더 포함될 수 있다. 전술한 회귀식은 상수를 더 포함할 수 있다. 예를 들어, 회귀식의 독립 변수는

일 수 있다. 회귀식의 a 내지 e는 각 독립 변수에 대한 계수일 수 있다. 회귀식의 a 내지 e는 각 독립 변수와 골밀도 데이터의 상관성을 나타내는 계수일 수 있다. 종속 변수(즉, 골밀도 데이터)와 상관 관계가 높은 독립 변수들의 계수 값은 상관 관계가 낮은 독립 변수들의 계수 값보다 큰 값을 가질 수 있다. 종속 변수(즉, 골밀도 데이터)와 상관 관계가 사전 결정된 값 이하인 경우(즉, 상관 관계가 낮은 경우) 회귀식의 독립 변수에서 제외될 수 있다. 전술한 회귀식의 멀티 임피던스 항목 값은, 다중 경로 임피던스에 관한 값 또는 다중 주파수 임피던스에 관한 값일 수 있다. 전술한 회귀식의 관계 정보는 다른 둘 이상의 독립 변수에 기초한 관계 정보를 포함할 수 있다. 회귀식은 전체 사용자에 대해 적용되는 식일 수도 있고, 연령대 별, 성별 별, 또는 골밀도 관련 질병 내력 유무에 기초하여 적용되는 식일 수도 있다. 전술한 회귀식에 대한 기재는 예시일 뿐이며, 본 개시는 이에 제한되지 않는다.When two variables are given, the controller 410 may predict one variable as one input of a regression equation, or may analyze the relationship between the two variables by calculating through the regression equation. According to another embodiment of the present disclosure, the controller 410 may perform at least one of biometric data of a user as an independent variable for regression analysis, and multiple linear regression analysis of the user's bone density data as a dependent variable for regression analysis. You can create expressions (ie regressions). The regression equation may be an expression for correlation analysis between the independent variable and the dependent variable. For example, regression

Can be. The independent variable of the aforementioned regression expression may further include other independent variable items. The above regression equation may further comprise a constant. For example, a regression independent variable

Can be. A to e of the regression equation may be coefficients for each independent variable. A to e of the regression equation may be coefficients representing correlation of each independent variable with bone density data. The coefficient value of the independent variables having a high correlation with the dependent variable (ie, bone density data) may have a value larger than that of the independent variables having a low correlation. If the correlation with the dependent variable (ie, bone density data) is below a predetermined value (ie, the correlation is low), it may be excluded from the independent variable of the regression equation. The above-described regression multi-impedance item value may be a value regarding multipath impedance or a value regarding multi-frequency impedance. The relationship information of the aforementioned regression equation may include relationship information based on two or more independent variables. The regression equation may be an expression applied to the entire user, or may be an expression applied based on age group, gender, or the presence of disease history related to bone density. The description of the above regression equation is merely an example, and the present disclosure is not limited thereto.

인 경우, 제어부(410)는 실제 골밀도 값인

와 근사적으로 구한 골밀도 값인

의 오차(즉,

)의 제곱의 합(즉,

)이 최소가 되는 모델을 생성할 수 있다. 제어부(410)는 복수의 학습 데이터(

)에 대하여 최소 제곱법을 만족하는 골밀도 측정 모델을 생성할 수 있다. 전술한 골밀도 측정 모델의 회귀식에 관한 기재는 예시일 뿐이며, 본 개시는 이에 제한되지 않는다.The control unit 410 uses the learning biometric data of the customer included in the training data as the independent variable of the regression equation and the learning bone density data of the customer included in the training data as the dependent variable of the regression equation, and the dependent variable of the regression equation and the dependent variable. Coefficients representing the correlation of variables can be extracted. The controller 410 may generate a bone density measurement model based on the least square method. The least square method may be a method of obtaining a model in which the sum of squares of the error of the value of the dependent variable and the value corresponding to the actual dependent variable is minimized. Regression Diet of Bone Mineral Density Model

In this case, the controller 410 is an actual bone density value.

The approximate bone density value

Of error (i.e.

Sum of squares of squares (i.e.

You can create a model with)). The controller 410 stores a plurality of learning data (

We can create a model for measuring bone density that satisfies the least square method. The description regarding the regression equation of the above-described bone density measurement model is only an example, and the present disclosure is not limited thereto.

(

))를 연산하여 골밀도 측정 모델의 회귀식에 대한 성능을 검증할 수 있다. 전술한 결정 계수 식에서, n은 테스트 데이터의 수일 수 있고,

는 테스트 데이터의 학습 생체 데이터를 회귀식의 독립 변수로 하여 도출된 종속 변수의 값일 수 있고,

는 테스트 데이터의 학습 골밀도 데이터 값일 수 있고,

는 테스트 데이터 세트에 포함된 하나 이상의 테스트 데이터의 학습 생체 데이터를 회귀식의 독립 변수로 하여 도출된 종속 변수의 값의 평균 값일 수 있다. 제어부(410)는 학습된 골밀도 측정 모델의 결정 계수가 사전 결정된 값 이상인 경우(즉, 1에 가까운 값인 경우) 골밀도 측정 모델의 활성화를 결정할 수 있다. 전술한 성능 검증에 대한 기재는 예시일 뿐이며, 본 개시는 이에 제한되지 않는다.The controller 410 may use at least one learning data of the learning data set as test data. The test data is data corresponding to the training data, and may be data used to verify performance after the training of the model is completed. According to one embodiment of the performance verification of the bone density measurement model of the present disclosure, the controller 410 may verify the performance of the regression equation of the bone density measurement model using residual analysis. The residual is the difference between the value derived by the regression equation (that is, the value of the dependent variable derived from the test biometric data of the test data as an independent variable of the regression equation) and the actual value (ie, the learning bone density data of the test data). It can mean a car. The controller 410 may determine the activation of the bone density measurement model when the residual of the learned bone density measurement model is equal to or less than a predetermined value (that is, a value that converges to zero). The controller 410 may determine deactivation of the bone density measurement model when the residual of the learned bone density measurement model is equal to or greater than a predetermined value (that is, a value far from zero). According to another embodiment of the performance verification of the bone density measurement model of the present disclosure, the controller 410 may determine the coefficient of determination (

(

) Can be used to verify the performance of the regression equation of the BMD model. In the above determination coefficient formula, n may be the number of test data,

May be the value of the dependent variable derived from the learning biometric data of the test data as an independent variable of the regression equation,

May be a learning bone density data value of the test data,

May be an average value of values of the dependent variable derived by using the learning biometric data of one or more test data included in the test data set as an independent variable of the regression equation. The controller 410 may determine the activation of the bone density measurement model when the determined coefficient of the learned bone density measurement model is greater than or equal to a predetermined value (ie, a value close to 1). The above description of the performance verification is only an example, and the present disclosure is not limited thereto.

According to another embodiment of the present disclosure, a bone density measurement model may be generated based on learning biometric data and learning bone density data of a user.

A method of outputting bone density data using a trained bone density measurement model according to another embodiment of the present disclosure will be described.

The controller 410 may receive biometric data including one or more items by using the network unit 420. The controller 410 may input biometric data including one or more items stored in the memory 430 into a bone density measurement model. The controller 410 may receive data obtained by measuring the multi-impedance items included in the biometric data of the user using the measurement unit 100. The controller 410 may generate the relationship information by using two or more item values among other item values except the relationship information of the biometric data. The controller 410 may input the items of the biometric data into the regression equation as independent variables of the regression equation included in the learned bone density measurement models. The controller 410 may set the value of the dependent variable of the regression equation as the output of the bone density measurement model. The controller 410 may generate bone density data based on the output of the bone density measurement model. The controller 410 may generate health risk information based on the bone density data.

According to another embodiment of the present disclosure, bone density data may be generated by using biometric data of a user as an input of a bone density measurement model.

Bone density data measurement method using the above-described bone density measurement model can measure bone density within a short time. The method for measuring bone density data using the above-described bone density measurement model has a small error rate due to a change in posture due to carelessness of the measurer. In addition, the bone density data measurement method using the above-described bone density measurement model is economical because it can be used without the management of periodic equipment.

By measuring the bone density data using the bone density measurement model according to the embodiment of the present disclosure, the bone density measuring apparatus may prevent an error that may occur when the bone density data is processed. The bone density measuring apparatus can improve the processing performance and speed of the computer by measuring the bone density data using the bone density measurement model of the embodiments of the present disclosure. As described in FIGS. 8 to 10 to be described later, by using the bone density measurement model of the present embodiment, the bone density measuring apparatus can improve the accuracy of the processing of the computing device. By using the bone density measurement model of the present embodiment, the bone density measuring apparatus can reduce the resource consumption of the bone density measuring apparatus.

The network unit 420 may include a transmitter and a receiver. The network unit 420 may include a wired / wireless internet module for network connection. Wireless Internet technologies may include Wireless LAN (Wi-Fi), Wireless Broadband (Wibro), World Interoperability for Microwave Access (Wimax), High Speed Downlink Packet Access (HSDPA), and the like. As a wired Internet technology, a digital subscriber line (XDSL), fibers to the home (FTTH), power line communication (PLC), and the like may be used.

The network unit 420 may include a short range communication module, and may transmit / receive data with an electronic device including a short range communication module, which is located at a relatively short distance with the service processing device. As a short range communication technology, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and the like may be used. In one embodiment of the present disclosure, the network unit 420 may detect the connection state of the network and the transmission and reception speed of the network. Data received through the network unit 420 may be stored through the memory 430 or may be transmitted to other electronic devices in a short range through a short range communication module.

The network unit 420 may transmit / receive data for performing a method for measuring bone density according to an embodiment of the present disclosure to another computing device, a server, and the like. In addition, the network unit 420 may transmit and receive data for performing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure. For example, the network unit 420 may include user identification information, first impedance value, second impedance value, third impedance value, first bone density value, second bone density value, third bone density value, first health information, and Second health information can be transmitted and received. The network unit 420 may transmit / receive data necessary for an embodiment of the present disclosure, such as biometric data and bone density data, to another computing device, a server, or the like. In addition, the network unit 420 may enable communication between a plurality of computing devices so that learning of the bone density measurement model may be distributed in each of the plurality of computing devices. The network unit 420 may enable communication between a plurality of computing devices to perform distributed processing of bone density data calculation using a bone density measurement model.

The memory 430 may store a computer program for performing a method for measuring bone density according to an embodiment of the present disclosure, and the stored computer program may be read and driven by the controller 410. For example, the memory 430 may store user identification information including a height, a weight, an age, and a gender of the user, and at least one measured first impedance value. In addition, the memory 430 may store the generated second impedance value and the third impedance value as the first impedance value. The second impedance value and the third impedance value stored as the first impedance value may then be used to generate the second bone density value for comparison with the newly generated first bone density value.

The memory 430 according to the embodiments of the present disclosure may store a program for the operation of the controller 410, and temporarily or permanently store input / output data (eg, bone density data, biometric data, etc.). It may be. The memory 430 may store data regarding a display and a sound. The memory 430 may include a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory), RAM (Random Access Memory, RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM), Magnetic Memory, Magnetic At least one type of storage medium may be included.

The display unit 440 may display various information regarding the bone density device. For example, the display unit 440 may display user identification information, a first bone density value, and a second bone density value. In addition, the display unit 440 may display the first health information and the second health information. However, the present invention is not limited thereto, and the display unit 440 may display various information.

The display unit 440 may include a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), and a flexible display (flexible). display and a 3D display, but are not limited thereto.

According to an embodiment of the present invention, the display unit 440 may include a touch sensor. The touch sensor may be configured to convert a change in pressure applied to a specific portion of the display 120 or a capacitance generated at a specific portion of the display 120 into an electrical input signal. The touch sensor may be configured to detect not only the position and area of the touch but also the pressure at the touch.

If there is a touch input to the touch sensor, the corresponding signal (s) is sent to the touch controller. The touch controller processes the signal (s) and then transmits the corresponding data to the controller 410. As a result, the controller 410 may determine which area of the display 120 is touched. When the display unit 440 includes a touch sensor, the display unit 440 may be used as the user input unit 450.

The user input unit 450 may receive a user input for controlling the bone density measuring apparatus 1000. For example, the user input unit 450 may receive user identification information from a user. The user input unit 450 may be implemented by at least one of a keypad, a dome switch, a touch pad (static pressure / capacitance), a jog wheel, and a jog switch, but is not limited thereto.

The measurement unit 100 may apply a voltage to two or more electrodes that are in contact with at least a part of the user's body. The measurement unit 100 may generate a current in at least two electrodes that are in contact with at least a part of the user's body. The measuring unit 100 may include a heat generating module or a heat insulating material capable of providing heat to the electrode. The measuring unit 100 may include at least one of a basic contact surface and a close contact surface. The multi-impedance of the user measured by the measuring unit 100 may be an item value for one item of the biometric data.

6 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.

Throughout this specification, computational models, neural networks, network functions, neural networks may be used in the same sense. A neural network may consist of a set of interconnected computing units, which may generally be referred to as "nodes." Such "nodes" may be referred to as "neurons". The neural network comprises at least one node. The nodes (or neurons) that make up the neural networks may be interconnected by one or more "links".

Within a neural network, one or more nodes connected via a link may form a relationship of input node and output node relatively. The concept of an input node and an output node is relative; any node in an output node relationship for one node may be in an input node relationship in relation to another node, and vice versa. As mentioned above, the input node to output node relationship can be created around the link. More than one output node can be connected to a single input node via a link, and vice versa.

In an input node and output node relationship connected via one link, the output node may be determined based on data input to the input node. Here, the node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are interconnected by each link to one output node, the output node is set to values input to input nodes connected to the output node and to a link corresponding to each input node. The output node value may be determined based on the weight.

As described above, a neural network is formed by interconnecting one or more nodes through one or more links to form an input node and an output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the relationship between the nodes and the links, and the value of the weight assigned to each of the links. For example, if there are the same number of nodes and links, and there are two neural networks with different weight values between the links, the two neural networks may be recognized as different from each other.

The neural network may comprise one or more nodes. Some of the nodes that make up the neural network may construct one layer based on distances from the original input node, for example, a set of nodes with a distance n from the original input node, You can configure n layers. The distance from the original input node may be defined by the minimum number of links that must pass to reach the node from the original input node. However, the definition of such a layer is arbitrary for explanation, and the order of the layer in the neural network may be defined in a manner different from that described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among the nodes in the neural network. Alternatively, in a neural network network, in a relationship between nodes based on a link, it may mean nodes having no other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes of the nodes in the neural network. Also, the hidden node may refer to nodes constituting a neural network other than the first input node and the last output node. The neural network according to an embodiment of the present disclosure may have the same number of nodes in the input layer as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes goes from the input layer to the hidden layer. Can be. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer is smaller than the number of nodes in the output layer, and the number of nodes decreases as the node progresses from the input layer to the hidden layer. have. In addition, the neural network according to another embodiment of the present disclosure may have a larger number of nodes in the input layer than the number of nodes in the output layer, and the number of nodes increases as the number of nodes increases from the input layer to the hidden layer. Can be. The neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the neural networks described above.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify latent structures of data. In other words, you can identify the potential structures of photos, texts, videos, voices, and music (e.g., what objects are in the photos, what the content and emotions of the text are, and what the content and emotions of the voice are). . Deep neural networks include convolutional neural networks (CNNs), recurrentneural networks (RNNs), auto encoders, Generic Adversarial Networks (GAN), restricted boltzmann (RBM) machine), deep belief network (DBN), Q network, U network, Siamese network and the like. The description of the deep neural network described above is merely an example and the present disclosure is not limited thereto.

The neural network may be learned in at least one of supervised learning, unsupervised learning, and semi supervised learning. Training of neural networks is intended to minimize errors in the output. In the neural network learning, the neural network errors are inputted from the output layer of the neural network in order to repeatedly input the training data into the neural network, calculate the neural network output and target errors for the training data, and reduce the errors. The process of updating the weight of each node of the neural network by backpropagation in the direction. In the case of teacher learning, the learning data using the correct answer is labeled in each learning data (ie, the labeled learning data), and in the case of the comparative learning, the correct answer may not be labeled in each learning data. That is, for example, the learning data in the case of teacher learning regarding data classification may be data in which a category is labeled in each of the learning data. The labeled training data is input to the neural network, and an error can be calculated by comparing the label of the training data with the output (category) of the neural network. As another example, in the case of non-comparative learning on data classification, an error may be calculated by comparing learning data as an input with a neural network output. The calculated error is propagated backward in the neural network (ie, the direction from the output layer to the input layer), and the connection weight of each node of each layer of the neural network may be updated according to the reverse propagation. The connection weight of each node to be updated may be changed according to a learning rate. The computation of the neural network for the input data and the backpropagation of the errors can constitute a learning cycle. The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network learning, high learning rates can be used to allow the neural network to quickly achieve a certain level of performance, increasing efficiency, and lower learning rates for later learning.

In the learning of neural networks, in general, the training data may be a subset of the actual data (i.e., the data to be processed using the trained neural network), thus reducing the error for the training data but not the error for the actual data. There may be an increasing learning cycle. Overfitting is a phenomenon in which an error about the actual data is increased by excessively learning the training data. For example, a neural network that learns a cat by showing a yellow cat may not recognize that the cat is a cat other than a yellow cat. Overfitting can act as a cause of increased errors in machine learning algorithms. Various optimization methods can be used to prevent such overfitting. In order to prevent overfitting, a method of increasing learning data, regulating, or dropping out of a node of a network in the course of learning may be applied.

7 is an exemplary view showing a bone density measurement model according to an embodiment of the present disclosure.

A learning method in a bone density measurement model according to an embodiment of the present disclosure will be described. In this example, one or more hidden layers may be included between the hidden layer 1 640 and the hidden layer 2 650, and the one or more hidden layers may be omitted.

The controller 410 may generate a bone density measurement model including one or more network functions 600. The network function 600 may include one input layer 630, one or more hidden layers, and one output layer 660.

The controller 410 may input training biometric data to the network function 600 of the bone density measurement model. Each item of training biometric data may be input to each input node included in the input layer 630 of the network function 600 of the bone density measurement model. For example, an item value for height 179 cm is a first input node 601, an item value for weight 78 kg is a second input node 602, an item value for age 32 is a third input node 603, The item value for the gender male may be input to the fourth input node 604 and the item value for the multi impedance 600 Ω to the fifth input node 605. The description of the above-described item value is only an example, and the present disclosure is not limited thereto.

The controller 410 may calculate each of the item values input to the input node included in the input layer 630 of the network function 600 by using the weights set in the link connected to the input node and propagate them to the hidden layer. For example, the first hidden node 620 included in the hidden layer 1 640 is a value obtained by calculating a value passed to the first input node 601 and a first weight 611, and a second input node 602. ), The value passed to the third input node 603 and the value passed to the third input node 603, the value passed to the fourth input node 604, and the fourth weight The calculated value, the value transferred to the fifth input node 605, and the value calculated by the fifth weight may be received. For example, the first hidden node 620 included in the hidden layer 1 640 is a value obtained by multiplying the value passed to the first input node 601 by the first weight 611, and the second input node 602. A value multiplied by a second weighted value, a value multiplied by a third weighted value and a value passed to a third input node 603, a value multiplied by a fourth weighted value, and a value passed to a fourth input node 604, The sum of the value multiplied by the fifth weighted value and the value transmitted to the fifth input node 605 may be received. The description of the above operation is merely an example, and the present disclosure is not limited thereto.

The training biometric data of the network function 600 may be propagated from the input layer 630 to the output layer 660 through the hidden layer 1 640 and the hidden layer 2 650. The weight of the network function 600 may be adjusted based on an error between bone density data (ie, output) and learning bone density data (ie, correct answer), which are output values at the output node 662 included in the output layer 660. . The controller propagates the error from the output layer 660 of the network function 500 to the input layer 630 via one or more hidden layers (e.g., hidden layer 2 650, then hidden layer 1 640). In addition, the weights set for each link may be updated (for example, after adjusting the weights of W2 (1,1) 631), the weights of W1 (1,1) 611 may be adjusted.

According to an embodiment of the present disclosure, a bone density measurement model may be generated based on learning biometric data and learning bone density data of a user.

A method for generating bone density data using a trained bone density measurement model according to an embodiment of the present disclosure will be described.

The controller 410 may input each item value of items included in the biometric data of the user to each of the input nodes included in the input layer 630 of the network function 600. For example, the controller 410 may input a value corresponding to 176 cm, which is an item value of a key, of biometric data to the first input node 601 of the input layer 630. The controller 410 weights the value input to the input layer 630 of the network function 600 (in this example, the first weight W1 (1,1) 611) and the second weight W2 (1,1). Output node 662 included in output layer 660 via one or more hidden nodes (in this example, including hidden layer 1 640 and hidden layer 2 650) Can propagate. The controller 410 may send the bone density data, which is an output value of the output node 662, through the network unit 420, store in the memory 430, or transmit the bone density data to the display means (eg, the display). The above description of the method for generating bone density data is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, bone density data may be generated by using biometric data of a user as an input of a trained bone density measurement model.

8 is an exemplary graph to which a bone density measurement model according to another embodiment of the present disclosure is applied.

FIG. 8 is an exemplary graph showing graphs comparing bone density data using a bone density measurement model including a regression equation and a bone density measurement method DEXA (Dual Energy X-ray Absorptiometry) according to another embodiment of the present disclosure. . The x-axis of the graph is a user's bone density measured using DEXA, a bone density measurement method, and the y-axis of the graph is a user's bone density measured using a bone density measurement model including the above regression equation. The user's bone density values measured using DEXA and the user's bone density values calculated using a bone density measurement model including a regression equation are plotted on a graph. A graph in the form of a scatter plot can visually show the relationship between the two values. In a graph showing the correlation in the form of a scatter plot, the graph may be drawn in the upward direction. In the graph showing the correlation in the form of scatter plot, the graph may be drawn in the downward direction when the correlation is negative. The correlation obtained through the graph is 0.523, and the output of bone density data using a DEXA and the output of bone density data using a bone density measurement model including a regression equation are highly correlated. Accordingly, it can be seen that the bone density measurement model including the regression equation according to another embodiment of the present disclosure has similar performance to the bone density data output when compared to DEXA which is commonly used in hospitals and the like. Bone density measurement model including a regression equation according to another embodiment of the present disclosure, it is possible to measure the bone density of the whole body within a shorter time than DEXA, less error due to the user's skill or posture changes, periodic equipment management It is more economical because it is not needed.

9 is an exemplary graph to which a bone density measurement model according to another embodiment of the present disclosure is applied.

FIG. 9 is an exemplary graph illustrating a Receiver Operating Characteristic (ROC) curve using a bone density measurement model including a regression equation according to another embodiment of the present disclosure. In this graph showing the ROC curve, the x-axis is 1-specificity, and the bone density measurement model is determined to be free of disease (in this embodiment, the T-score is determined to be a disease from a user with osteopenia having -1.0 or less. The y-axis is the sensitivity, and the bone density measurement model indicates the percentage of users who actually have a disease. In this example, a high sensitivity of 80% and specificity of 70% were obtained. The area under the graph in the ROC curve is called AUC (Area Under the Curve). The larger the AUC, the more accurate the model. In this embodiment, the AUC value for the ROC curve is 0.847, indicating that the bone density measurement model is a very accurate model.

10 is a diagram illustrating an example of another bone density measurement model.

FIG. 10 is a table illustrating values comparing bone density data using DEXA, which is a method for measuring bone density, with bone density measuring apparatuses of an ultrasound method. When compared with DEXA, which is a method for measuring bone density of ultrasound-type bone density measuring apparatus, the highest correlation is 0.526, when compared with bone density measurement model including a regression equation and bone density measurement method DEXA according to another embodiment of the present invention. It has a value similar to that of 0.523. That is, it can be seen that the bone density measurement model including the regression equation according to another embodiment of the present disclosure has higher or similar performance than the bone density measurement apparatus using ultrasound. The maximum value of AUC of the ROC curve of the ultrasonic bone density measuring apparatus is 0.75. Therefore, it can be seen that the bone density measurement model of the present invention is a model capable of measuring more accurate bone density data when compared with the AUC value of 0.847 of the bone density measurement model including the regression equation according to another embodiment of the present invention.

FIG. 11 is a block diagram illustrating logic for implementing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in bone density value may be implemented by the following logic.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in a bone density value includes logic 1210 for storing user identification information and at least one measured first impedance value in a memory; Logic 1220 for applying a voltage to at least two electrodes by a measuring unit and measuring a current corresponding to the applied voltage; Logic 1230 for determining a second impedance value for at least a portion of a user's body based on the voltage applied by the measurement unit and the current measured by the measurement unit; Logic 1240 for generating a first bone density value based on the user identification information and the second impedance value; Logic 1250 for generating at least one second bone density value using the user identification information stored in the memory and the at least one first impedance value; It may be implemented by logic 1260 that generates first health information indicative of health abnormalities based on the first bone density value and the at least one second bone density value.

12 is a block diagram illustrating a module for implementing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in bone density value may be implemented by the following module.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in a bone density value includes: a module 1310 for storing user identification information and at least one measured first impedance value in a memory; A module 1320 for applying a voltage to at least two electrodes by a measuring unit and measuring a current corresponding to the applied voltage; A module 1330 for determining a second impedance value for at least a part of a user's body based on the voltage applied by the measurement unit and the current measured by the measurement unit; A module 1340 for generating a first bone density value based on the user identification information and the second impedance value; A module 1350 for generating at least one second bone density value using the user identification information stored in the memory and the at least one first impedance value; It may be implemented by the module 1360 for generating first health information indicating health abnormality based on the first bone density value and the at least one second bone density value.

13 is a block diagram illustrating a circuit for implementing a method for generating health information by measuring a change in bone density value according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in bone density value may be implemented by the following circuit.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in a bone density value includes a circuit 1410 for storing user identification information and at least one first measured impedance value measured in a memory; A circuit 1420 for applying a voltage to at least two electrodes by a measuring unit and measuring a current corresponding to the applied voltage; A circuit 1430 for determining a second impedance value for at least a part of a user's body based on the voltage applied by the measurement unit and the current measured by the measurement unit; A circuit 1440 for generating a first bone density value based on the user identification information and the second impedance value; A circuit 1450 for generating at least one second bone density value using the user identification information stored in the memory and the at least one first impedance value; It may be implemented by a circuit 1460 for generating first health information indicative of health abnormalities based on the first bone density value and the at least one second bone density value.

14 is a block diagram illustrating a means for implementing a method for generating health information by measuring a change in bone density value according to one embodiment of the present disclosure.

According to one embodiment of the present disclosure, a method for generating health information by measuring a change in bone density value may be implemented by the following means.

According to an embodiment of the present disclosure, a method for generating health information by measuring a change in a bone density value includes: means for storing user identification information and at least one measured first impedance value in a memory; Means for applying a voltage to at least two electrodes by a measuring unit and measuring a current corresponding to the applied voltage; Means (1530) for determining a second impedance value for at least a portion of a user's body based on the voltage applied at the measurement unit and the current measured at the measurement unit; Means (1540) for generating a first bone density value based on the user identification information and the second impedance value; Means (1550) for generating at least one second bone density value using user identification information and at least one first impedance value stored in a memory; It may be implemented by means 1560 for generating first health information indicative of health abnormalities based on the first bone density value and the at least one second bone density value.

15 shows a brief general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above generally with respect to computer executable instructions that may be executed on one or more computers, those skilled in the art will appreciate that the present disclosure may be implemented in combination with other program modules and / or as a combination of hardware and software. will be.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure may include uniprocessor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like (each of which And other computer system configurations, including one or more associated devices.

The described embodiments of the present disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Any medium that can be accessed by a computer can be a computer readable medium, which can be volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Removable media. By way of example, and not limitation, computer readable media may comprise computer readable storage media and computer readable transmission media. Computer-readable storage media are volatile and nonvolatile media, temporary and non-transitory media, removable and non-removable implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. Media. Computer storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROMs, digital video disks or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, Or any other medium that can be accessed by a computer and used to store desired information.

Computer-readable transmission media typically embody computer readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and the like. Includes all information delivery media. The term modulated data signal refers to a signal that has one or more of its characteristics set or changed to encode information in the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above should also be included within the scope of computer readable transmission media.

An exemplary environment 1100 is illustrated that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 connects system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

The system bus 1108 may be any of several types of bus structures that may be further interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input / output system (BIOS) is stored in nonvolatile memory 1110, such as ROM, EPROM, EEPROM, etc., and the BIOS provides a basic aid for transferring information between components in the computer 1102, such as during startup. Contains routines. RAM 1112 may also include fast RAM, such as static RAM, for caching data.

Computer 1102 also includes an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA). The internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). ), A magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (eg, a CD-ROM disc 1122 or for reading from or writing to other high capacity optical media such as DVD). The hard disk drive 1114, the magnetic disk drive 1116, and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124, the magnetic disk drive interface 1126, and the optical drive interface 1128, respectively. ) Can be connected. Interface 1124 for external drive implementation includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide nonvolatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate zip drives, magnetic cassettes, flash memory cards, cartridges, and the like. Other types of media readable by the computer, etc. may also be used in the exemplary operating environment and it will be appreciated that any such media may include computer executable instructions for performing the methods of the present disclosure.

Multiple program modules may be stored in the drive and RAM 1112, including operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or a portion of the operating system, applications, modules and / or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired / wireless input devices, such as a keyboard 1138 and a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. These and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, but the parallel port, IEEE 1394 serial port, game port, USB port, IR interface, Etc. can be connected by other interfaces.

A monitor 1144 or other type of display device is also connected to the system bus 1108 via an interface such as a video adapter 1146. In addition to the monitor 1144, the computer generally includes other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer (s) 1148, via wired and / or wireless communications. Remote computer (s) 1148 can be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and typically is associated with computer 1102. Although many or all of the components described above are included, for simplicity, only memory storage 1150 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 1152 and / or a larger network, such as a telecommunications network (WAN) 1154. Such LAN and WAN networking environments are commonplace in offices and businesses, facilitating enterprise-wide computer networks such as intranets, all of which may be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 via a wired and / or wireless communication network interface or adapter 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed therein for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158, connect to a communications computing device on the WAN 1154, or establish communications over the WAN 1154, such as over the Internet. Other means. The modem 1158, which may be an internal or external and wired or wireless device, is connected to the system bus 1108 via the serial port interface 1142. In a networked environment, program modules or portions thereof described with respect to computer 1102 may be stored in remote memory / storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

Computer 1102 is associated with any wireless device or entity disposed and operating in wireless communication, such as a printer, scanner, desktop and / or portable computer, portable data assistant, communications satellite, wireless detectable tag. Communicate with any equipment or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technology. Thus, the communication can be a predefined structure as in a conventional network or simply an ad hoc communication between at least two devices.

Wireless Fidelity (Wi-Fi) allows you to connect to the Internet without wires. Wi-Fi is a wireless technology such as a cell phone that allows a device, for example, a computer, to transmit and receive data indoors and outdoors, ie anywhere within the coverage area of a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, high-speed wireless connections. Wi-Fi may be used to connect computers to each other, to the Internet, and to a wired network (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). have.

Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols, and chips that may be referenced in the above description may include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. Or particles, or any combination thereof.

One of ordinary skill in the art of the disclosure will appreciate that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, It will be appreciated that for purposes of the present invention, various forms of program or design code, or combinations thereof, may be implemented. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. One skilled in the art of the present disclosure may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The various embodiments presented herein may be embodied in a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media may include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical discs (eg, CDs, DVDs, etc.), smart cards, and flash memory. Devices, such as, but not limited to, EEPROM, cards, sticks, key drives, and the like. In addition, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure should not be limited to the embodiments set forth herein but should be construed in the broadest scope consistent with the principles and novel features set forth herein.

Claims (12)

As a bone density measuring device,
A memory for storing user identification information including height, weight, age and gender of the user and at least three measured first impedance values;
A measuring unit including two or more electrodes in contact with at least a part of a user's body, applying a voltage to the electrodes, and measuring a current corresponding to the applied voltages;
Determine a second impedance value for at least a portion of the body of the user based on the voltage applied by the measurement unit and the current measured by the measurement unit, and based on the user identification information and the second impedance value A control unit for generating a first bone density value;
Including;
The control unit:
Generate at least three second bone density values using the user identification information and the at least three first impedance values,
Generating first health information indicative of health abnormalities when the first bone density value is less than a minimum value among the at least three second bone density values,
Bone density measuring device.
The method of claim 1,
The control unit:
A first variation value representing a deviation between the first bone density value and one of the at least three second bone density values is determined based on a plurality of second variation values calculated by the at least three second bone density values Generate the first health information when the threshold value is exceeded;
The second variation value is,
A deviation between two of said at least three second bone density values,
Bone density measuring device.
The method of claim 2,
The control unit:
Determining a maximum value among the plurality of second variation values as the preset threshold value,
Bone density measuring device.
The method of claim 2,
The control unit:
Determining the preset threshold by giving a preset weight to a maximum value among the plurality of second variation values,
Bone density measuring device.
The method of claim 2,
The control unit:
Determining an average value of the plurality of second variation values as the preset threshold value,
Bone density measuring device.
The method of claim 2,
The control unit:
Determining the preset threshold by applying a predetermined weight to an average value of the plurality of second variation values,
Bone density measuring device.
delete The method of claim 1,
The control unit:
Generating the first health information when the first bone density value is smaller than a minimum value among the at least three second bone density values to which a predetermined weight is assigned;
Bone density measuring device.
The method of claim 1,
The control unit:
When the first health information is generated, re-measurement is performed through the measuring unit to determine a third impedance value, and generate a third bone density value based on the user identification information and the third impedance value,
Verifying the first health information based on the third bone density value,
Bone density measuring device
The method of claim 1,
The control unit:
When the first health information is generated, generating second health information including at least one of recommended exercise information for the user, recommended dietary information for the user, and medical institution information based on the user's location information. ,
Bone density measuring device.
The method of claim 10,
The control unit:
Transmitting the second health information to a mobile terminal or a server of the user through a network unit;
Bone density measuring device.
The method of claim 1,
A display unit configured to output the first health information;
Further comprising,
Bone density measuring device.

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