KR102555157B1 - Integrated care system and method using heatlcare device - Google Patents

Abstract

The present invention relates to an integrated care system and a method thereof. According to an aspect of the present invention, the integrated care system can collect health condition data comprehensively describing a health condition of a user remotely positioned in various aspects. According to embodiments of the present invention, the integrated care system includes: a healthcare device generating the health condition data of a care subject by obtaining health condition information by measuring the health condition of the care subject; and a service server interacting with the healthcare device and an electronic device of a caregiver to execute a predetermined application installed in advance in the healthcare device and the electronic device of the caregiver. The service server also receives a daily care record of the caregiver and the health condition data and stores the health condition information included in the health condition data in at least one database connected. Moreover, the service server provides a diagnostic service based on the health condition data.

Description

Integrated care system and method using heatlcare device {Integrated care system and method using heatlcare device}

Embodiments of the present application relate to an integrated care system and method using a healthcare device that comprehensively collects health conditions of remotely located users.

In the case of medically vulnerable groups such as the elderly/disabled, demand for primary medical services is very high, and health care such as chronic diseases is essential. However, in the case of the medically vulnerable group, access to medical care is low, and the 'unmet medical utilization rate' is high. In order to solve this problem, the government is striving to realize ‘integrated care’ centered on local governments. ‘Integrated care’ refers to welfare that can simultaneously satisfy the living support service and medical service of the elderly.

Due to the increase in life expectancy, the population aged 65 and over and the number of long-term care recipients are rapidly increasing, and accordingly, the need for ‘integrated care’ is growing. Long-term care is largely divided into home care and institutional care, and among them, visiting care services provided by caregivers account for 60% of the total. The Ministry of Health and Welfare is trying to realize 'integrated care' through this long-term care service, but 'integrated care' including medical service is not realized due to reasons such as medical disparities between regions and lack of medical service supply, and living support services is focused only on

In addition, in the existing long-term care service, the caregiver keeps a service record by writing a care diary. However, there is no standard for this, so not only is the content insufficient, but even this has a limitation that it is difficult for the guardian of the person to be cared for to confirm.

Recently, attempts have been made to implement an integrated care service using a user terminal, but there is a limit in relying mainly on information according to a user's input.

Patent Registration Publication No. 10-2306826 (registration date: 2018.04.03.)

In order to solve the above problems, embodiments of the present application are intended to provide an integrated care system and method using a healthcare device that comprehensively collects the health status of a user located remotely in order to track and manage the user's health data more conveniently. do.

An integrated care system according to an aspect of the present application is a healthcare device that measures the health status of a care target, acquires health status information, and generates health status data of the care target - the healthcare device is configured to provide each type of sensing data. configured to generate health state data of the care target using a plurality of types of sensor modules, each measuring a; a caregiver's electronic device generating care information including a care diary for the care subject and requesting a non-face-to-face medical treatment service; An electronic device of a medical staff providing a non-face-to-face medical treatment service based on the health condition information according to the request; and interacting with the caregiver's electronic device and the healthcare device to execute a specific application pre-installed on the caregiver's electronic device and the health care device, receiving the health state data and the caregiver's care diary, and receiving the health status data. It may also include a service server configured to store health state information included in the data in at least one connected database and to provide a diagnosis service based on the health state data.

In one embodiment, the healthcare device may include a communication unit configured to transmit the health state data through electrical communication with a service server; And receiving a photographing result from a camera module for photographing the body composition of the care subject or receiving a result of measuring the health condition of the care subject from at least one sensor module among a plurality of types of sensor modules to describe the health condition of the care subject It may also include a processor that generates health state data. The plurality of types of sensor modules include a sound sensor module, a body temperature sensor module, a blood pressure sensor module, and a blood sugar sensor module. The health state data may include measurement data of one or more of lung sounds, gastrointestinal sounds, heartbeat data, body temperature data, blood pressure data, and blood sugar data.

In one embodiment, one surface of the camera module and one surface of each of the plurality of types of sensor modules may include a coupling member configured to be mechanically and electrically attached to and detached from one surface of the healthcare device. The healthcare device acquires an image of the body composition in a state in which a camera module is attached, acquires other measurement values through a newly attached sensor module after the attached camera module is detached, and sequentially acquires Health condition data including images and measured values may be generated.

In one embodiment, the healthcare device may further include a display displaying a screen of an operation guide that supports obtaining health state data describing a more accurate health state. The operation guide is one of a heart rate test guide, a body temperature test guide, a lung test guide, an ear test guide, a nose test guide, a mouth test guide, a heart test guide, a skin test guide, an electrocardiogram test guide, a blood pressure test guide, and a blood sugar test guide. The above inspection guide may be included. The healthcare device displays a 'lung examination' button on the screen of the output device, displays a lung examination guide when a button selection command is received, and the lung examination guide screen displays an image or video of placing an acoustic sensor module on the back. And configured to display a figure screen that can induce deep breathing for more accurate measurement during lung measurement, and the figure screen may be a screen in which a circular figure repeatedly increases and decreases.

In one embodiment, the healthcare device may display a 'mouth examination' button on the screen of the output device and display a mouth examination guide when a button selection command is received. The mouth examination guide screen is configured to display an image or video in which the camera module of the healthcare device is placed in the mouth. The caregiver's electronic device linked to the healthcare device may display the photographed view of the camera module as a real-time preview.

In one embodiment, the service server may include a device management unit processing the received health state data to obtain health state information included in the health state data; an application service management unit that performs an operation of interacting with the caregiver's electronic device and the healthcare device to execute a specific application pre-installed on the caregiver's electronic device and the healthcare device; a social channel service unit configured to exchange a message related to a non-face-to-face medical treatment service between a caregiver's electronic device and a medical staff's electronic device through a channel on social media; and a machine learning service unit that inputs the acquired health state data of the subject of care into at least one pre-learned judgment model to determine the accuracy of the user's health state data or to provide a prediction service for determining the user's health state. You may.

In one embodiment, the service server may be connected to a distributed DB and a relational DB. The device management unit may first store the health state information in a relational DB and then store the health state information in a distributed DB.

In an embodiment, the machine learning service unit may include a first judgment model pre-trained using a first training data set based on health state data. The first judgment model includes an artificial neural network structure pre-learned to determine whether the measured health state data is accurately measured. The artificial neural network structure is configured to extract a feature from input data and calculate a probability value for classifying whether the input data is a correct measurement class or an inaccurate measurement class based on the extracted feature. The first training data set consists of a plurality of first training samples, each first training sample is based on sample health state data obtained before learning from a healthcare device, and the first training sample is a sample health state data, and label data indicating the accuracy of the corresponding health condition data.

In one embodiment, the device management unit starts to store the corresponding health state information when the health state information is accurately measured according to the determination result of the first determination model, and according to the determination result of the first determination model It may also be configured to transmit a re-measurement request to the healthcare device when the health state information is incorrectly measured.

In an embodiment, the machine learning service unit may include a second judgment model pre-learned using a second training data set based on health state data obtained from a healthcare device.

The second judgment model may include an artificial neural network structure pre-learned to determine the possibility of a disease of a care target based on the measured health state data. The artificial neural network structure is configured to extract features from input data and calculate a probability that the input data has a disease as a probability value based on the extracted features. The second training data set consists of a plurality of second training samples, each second training sample is based on sample health state data obtained before learning from the healthcare device, and the second training sample is a sample health state It includes label data including the data and the disease value indicated by the corresponding health condition.

In one embodiment, the social channel service unit transmits a channel message including health status information of the care target and a diagnosis result using the second judgment model to a medical staff's electronic device in order to provide a non-face-to-face medical treatment service. may be

In one embodiment, the integrated care system may further include an in-memory DB configured to store information inquired from a search engine of a distributed DB and return the stored information to a service server when requested. In the integrated care system, the device management unit checks whether a previous search result is stored in the in-memory DB, and if the search result is confirmed, the in-memory DB transfers data of the search result to the device management unit. And, if the search result is not confirmed, the target data is searched from the distributed DB, the searched data is stored in the in-memory DB so that it can be immediately searched in the in-memory DB in the next search, and the in-memory DB After being stored in the data is configured to be transferred to the device management unit again.

In one embodiment, the service server is connected to the blockchain network as a node of the blockchain network, and generates health status data and personal information of the subject of care as blockchain data using a private key, and converts the blockchain data into blockchain data. It may further include a ledger providing unit for storing on the blockchain network.

The integrated care system according to one aspect of the present application may collect health state data that comprehensively describes the health state of a user located remotely from various aspects.

In addition, the integrated care system may provide non-face-to-face medical support services by utilizing the collected health condition data or provide customized health management services based on big data analysis.

In addition, the integrated care system may provide a care task support system capable of computerizing various administrative tasks of home welfare institutions and managing care recipients and care managers.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a schematic diagram of an integrated care system according to one aspect of the present application.
2 is a block diagram of a healthcare device 100 according to an embodiment of the present application.
3 illustrates a healthcare device in which a sensor module is detachable/attached according to an embodiment of the present application.
4A to 4C illustrate an EGUI screen displaying an operation guide according to an embodiment of the present application.
5 illustrates a real-time preview screen according to an embodiment of the present application.
6 is a schematic diagram of a system architecture of a service server 130, according to an embodiment of the present application.
7 is a schematic diagram of a self-diagnosis service using a second judgment model according to an embodiment of the present application.
8 is a schematic diagram of a process of storing blockchain data according to an embodiment of the present application.
9 is a schematic diagram illustrating an operation of an integrated care system utilizing a distributed DB and an in-memory DB according to an embodiment of the present application.
10 is a flowchart of an integrated care method according to another aspect of the present application.

Hereinafter, with reference to the drawings, look at the embodiments of the present application in detail.

However, it should be understood that this disclosure is not intended to limit the disclosure to specific embodiments, and includes various modifications, equivalents, and/or alternatives of the embodiments of the disclosure. . In connection with the description of the drawings, like reference numerals may be used for like elements.

In this specification, expressions such as “has,” “may have,” “includes,” or “may include” refer to corresponding characteristics (eg, numerical values, functions, operations, steps, parts, elements, and/or components). elements), and does not preclude the presence or addition of additional features.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Expressions such as "first", "second", "first" or "second" used in various embodiments may modify various elements regardless of order and/or importance, and define the elements. I never do that. The above expressions may be used to distinguish one component from another. For example, the first element and the second element may represent different elements regardless of order or importance.

As used herein, the expression “configured (or configured) to” means, depending on the circumstances, for example, “suitable for,” “having the capacity to” ," "designed to," "adapted to," "made to," or "capable of." The term "configured (or set) to" may not necessarily mean only "specifically designed to" hardware. Instead, in some contexts, the phrase "device configured to" may mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase "a processor configured (or set) to perform A, B, and C" may include a dedicated processor (eg, an embedded processor) to perform those operations, or one or more software programs stored in a memory device that executes By doing so, it may mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

1 is a schematic diagram of an integrated care system according to one aspect of the present application.

Referring to FIG. 1 , the integrated care system 1 includes a health care device 100, a caregiver's electronic device 110, and a service server 130. In some embodiments, the integrated care system 1 includes the medical staff's electronic device 140 . In addition, the integrated care system 1 may further include a guardian's electronic device 150 and/or a care work support system 160 .

The care target includes the elderly, the disabled, patients, caregivers, and/or other medically vulnerable groups.

The guardian is a legal and social guardian of the object of care, and may be a family member or relative.

The caregiver includes a caregiver and a caregiver. In some embodiments, the caregiver may be a guardian of the caregiver. In this case, the operation of the electronic device 150 of the guardian may be performed by the electronic device 110 of the caregiver.

The integrated care system 1 according to embodiments may have an aspect of being entirely hardware, entirely software, or partly hardware and partly software. For example, the system may collectively refer to hardware equipped with data processing capability and operating software for driving the hardware. In this specification, terms such as "unit", "system" and "apparatus" are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device including a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), or another processor. Also, software may refer to a running process, an object, an executable file, a thread of execution, a program, and the like.

The telecommunication network 120 provides a wired/wireless electrical communication path through which the healthcare device 100, the electronic devices 110, 140, and 150, and the service server 130 can transmit and receive data with each other. The telecommunication network 120 is not limited to a communication method according to a specific communication protocol, and an appropriate communication method may be used according to implementation. For example, when configured as an Internet Protocol (IP)-based system, the telecommunication network 120 may be implemented as a wired and/or wireless Internet network, and the electronic devices 110, 140, and 150 and the service server 130 ) is implemented as a mobile communication terminal, the telecommunication network 120 may be implemented as a wireless network such as a cellular network or a wireless local area network (WLAN) network.

The healthcare device 100 is a device that can conveniently measure the comprehensive health condition of a caregiver by interworking with the caregiver's electronic device 110 or the service server 130 in real time. The integrated care system 1 obtains health status information describing the health status of a care target through the healthcare device 100 .

2 is a block diagram of a healthcare device 100 according to an embodiment of the present application.

Referring to FIG. 2, the healthcare device 100 obtains health state information that comprehensively describes the health state of the care target in external/internal dimensions through a camera module 106 and a sensor module 107, and and generate health state data including the obtained health state information.

In specific embodiments, the healthcare device 100 includes a memory 101, a communication unit 102, a processor 103, an input device 104, an output device 105, a camera module 106, a plurality of types of A sensor module 107 may also be included.

The memory 101 is connected to the processor 103 and may store data such as basic programs for operation of the processor 103, application programs, setting information, and information generated by operation of the processor 103. The memory 101 may be composed of volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory. Also, the memory 101 may provide stored data according to a request of the processor 103 . In addition, the memory 101 permanently stores data received from the communication unit 102, data photographed by the camera module 106, sensing data generated by the sensor module 107, and data generated or processed by the processor 103. or temporarily stored.

The memory 101 includes a hard disk drive (HDD), a solid state drive (SSD), a compact disc (CD), a random access memory (RAM), a read only memory (ROM), and various types of permanent, semi-permanent, or temporary storage of data. Other storage devices may also be included.

In certain embodiments, the memory 101 may store a care application for executing the integrated care system and method.

The communication unit 102 is connected to the processor 103 to transmit and receive data, and to transmit and receive data to and from other electronic devices 110 or external devices such as the service server 130 . All or part of the communication unit 103 may be referred to as a transmitter, a receiver, a transceiver, a communication unit, a communication model, or a communication circuit. The transceiver 102 is a wired access system and a wireless access system, such as an institute of electrical and electronics engineers (IEEE) 802.xx system, an IEEE Wi-Fi system, a 3rd generation partnership project (3GPP) system, and a 3GPP long term evolution (LTE) system. , 3GPP 5GNR (new radio) system, 3GPP2 system, at least one of various wireless communication standards such as Bluetooth may be supported.

The processor 103 may be configured to implement the procedures and/or methods proposed in the present invention. The processor 103 may determine at least one executable operation of the healthcare device 100 based on information determined or generated using a data analysis algorithm. In addition, the processor 103 may control components of the healthcare device 100 to perform the determined operation. To this end, the processor 103 may request, retrieve, receive, or utilize data of the memory 101, and execute a predicted operation or an operation determined to be desirable among the at least one executable operation. 100) can be controlled.

When connection with an external device such as the service server 130 is required to perform the determined operation, the processor 103 generates a control signal for controlling the external device, and communicates with the external device through the communication unit 102. The generated control signal may be transmitted.

In certain embodiments, the processor 103 provides health status data of a subject to be cared for to be used for analysis in the service server 130 to provide an integrated care service and executes the care service provided from the service server 130. Controls the overall operation of the healthcare device 100.

Such a processor 103 may be implemented as at least one processor. The processor 103 may include a central processing unit (CPU) or a neuromorphic processor designed to be advantageous in the operation of an artificial neural network by simulating nerve cells and synapses in the human brain.

In addition, the at least one processor 103 may generate image data and sensing data by receiving a photographing result and a sensing result of the camera module 106 and the sensor module 107 .

The input device 104 is a component configured to receive commands associated with user (eg, caregiver) input. The input device 104 may include a touch unit or other input unit.

The touch unit is a component that inputs a buyer command by using a user's body part or another object as a pointing object. The touch unit may include a pressure-sensitive or electrostatic sensor, but is not limited thereto. The other input units include, for example, buttons, keyboards, dials, switches, sticks, and the like.

The output device 105 is a component that displays information stored and/or processed in the healthcare device 100, and may include, for example, an LCD, OLED, flexible screen, etc., but is not limited thereto.

2, the input device 104 and the output device 105 are separated, but in many embodiments, the input device 104 and the output device 105 are implemented as one component to perform input reception and information output It can be. For example, the input device 104 and the output device 105 may be a touch panel implemented as a touch screen forming a layer structure with the screen. A touch input is input by a pointing object (eg, a user's body or a tool).

The camera module 106 acquires an image frame such as a still image or a moving image by photographing a subject. The obtained image frame may be displayed on the display of the output device 106, transmitted to an external device through the communication unit 102, processed by the processor 103, or stored in the memory 101.

The camera module 106 includes a photographing unit that generates an image of an object in response to a wavelength. For example, the camera module 106 may acquire an RGB image by including an RGB sensor. In addition, the camera module 106 may be further configured to additionally acquire an IR image or a depth image by further including an IR sensor or a depth sensor.

In certain embodiments, the healthcare device 100 may generate health state image data obtained by photographing the body composition of the subject to be cared for by the camera module 106 . The body configuration is a configuration capable of providing basic visual information for determining a health state, and may include, for example, the nose, ear, larynx, pharynx, skin, and/or other body configurations.

In some embodiments, the healthcare device 100 may include a first camera module 106a and a second camera module 106b. The first camera module 106a is a camera module that captures a relatively large subject including a distortion lens. A relatively large subject may be, for example, skin or a structure in the mouth. The second camera module 106b is a camera module that captures a relatively small subject that is difficult to photograph with the first camera module 106a. A relatively small object may be, for example, an ear or a coil. To this end, the second camera module 106b may be configured in a size and shape capable of entering a relatively narrow structure in order to capture small body parts. The second camera module 106b is configured to reduce noise and improve light efficiency with improved resolution compared to the first camera module 106a.

The sensor module 107 includes a sensor unit that measures a physical signal and generates a measurement result. The healthcare device 100 may include a plurality of types of sensor modules 107 that measure each type of health condition information in order to generate a plurality of types of sensing data. Each sensor module 107 supplies measurement results to the processor 103 to generate sensing data for each type.

In specific embodiments, the plurality of types of sensor modules 107 may include an acoustic sensor module 107a, a body temperature sensor module 107b, a blood pressure sensor module 107c, and a blood sugar sensor module 107d. In this case, the health state data may include measurement data of one or more of lung sound, gastrointestinal sound, heartbeat sound data, body temperature data, blood pressure data, and blood sugar data.

The healthcare device 100 may obtain lung sound, gastrointestinal sound, or heartbeat data by measuring lung sound, gastrointestinal sound, or heartbeat sound of a care target by the acoustic sensor module 107a. The acoustic sensor module 107a may include, for example, a vibration sensor or other acoustic sensors.

The healthcare device 100 may obtain body temperature data by measuring the body temperature of the care target by the body temperature sensor module 107b. The body temperature sensor module 107b may include an infrared sensor or other temperature sensor. For example, the temperature sensor module 107b is a non-contact infrared temperature sensor module, and may be configured to measure the temperature of a target surface within several tens of ms without making direct contact with the measurement target.

The healthcare device 100 may obtain blood pressure data by measuring the blood pressure of the subject to be cared for by the blood pressure sensor module 107c. The blood pressure sensor module 107c may include a pressure sensor.

The healthcare device 100 may obtain blood glucose data by measuring the blood sugar level of the subject to be cared for by the blood glucose sensor module 107d.

3 illustrates a healthcare device in which a sensor module is detachable/attached according to an embodiment of the present application.

Referring to FIG. 3 , the healthcare device 100 may be configured to detach/attach at least one module 106 or 107 among a camera module 106 and a plurality of types of sensor modules 107 . One surface of the camera module and one surface of each of the plurality of types of sensor modules include a coupling member configured to be mechanically and electrically attached to and detached from one surface of the healthcare device. Similarly, one surface of the healthcare device may also include other coupling members for electrically and mechanically coupling with the coupling members of the modules 106 and 107 . The body of the healthcare device 100, including the memory 101, the communication unit 102, and the processor 103, the camera module 106, and the sensor module 107 are mechanically and electrically connected to each other by the coupling member. do. When image data and various sensing data are generated in the attached modules 106 and 107, they are supplied to the memory 101, the communication unit 102, or the processor 103 through a coupling member.

In some embodiments, the at least one detachable/detachable module 106, 107 includes a first camera module 106a, a second camera module 106b, a sound sensor module 107a, a body temperature sensor module 107b, A blood pressure sensor module 107c and/or a blood sugar sensor module 107d may be included. In this case, the healthcare device 100 may generate health state data by sequentially obtaining health state information including images and measurement values. For example, with the camera module 106 attached, the ear, nose, or skin is photographed to obtain ear, nose, or skin image data, and then the attached camera module 106 is detached and then the acoustic sensor module 107a ) is attached, lung sound, gastrointestinal sound, or heartbeat data are measured to obtain lung sound, gastrointestinal sound, or heartbeat data, the acoustic sensor module 107a is detached, and the body temperature sensor module 107b is attached to measure the body temperature. Body temperature data may be obtained by measuring the body temperature.

In this way, health conditions may be comprehensively obtained from various aspects through a single healthcare device 100 and one or more detachable/attachable modules 106 and 107 .

In addition, the healthcare device 100 may be configured to enable accurate data acquisition by following a guide on the screen through an Exam Guide User Interface (EGUI) that supports the acquisition of health state data describing a more accurate state. In the integrated care system 1, EGUI may be provided through the output device 105 of the health care device 100 or the output device 105 of the caregiver's electronic device 110.

4A to 4C illustrate an EGUI screen displaying an operation guide according to an embodiment of the present application.

Referring to FIGS. 4A to 4C , the EGUI may display an operating guide of the healthcare device 100 for measuring corresponding health state data on a screen.

The screen of the EGUI may include a part area displaying the current measurement (or shooting) situation and another part displaying the manipulation guide. For example, as shown in FIG. 4 , the partial area may be displayed at the top of the screen and the other partial area may be displayed at the bottom of the screen.

The current measurement situation may be expressed as a preview or a current measurement value.

5 illustrates a real-time preview screen according to an embodiment of the present application.

Referring to FIG. 5 , the healthcare device 100 is interlocked with the caregiver's electronic device 110 or other devices 130, 140, 150 through pairing, etc., so that the image detected by the camera module 105 You can also receive a real-time preview of the screen. For example, while the examination is performed according to the in-ear examination guide of FIG. 4A , a currently detected image in the ear may be provided as a real-time preview as shown in FIG. 5 .

The healthcare device 100 may store an EGUI manipulation guide for each health state data in advance. For example, the operation guide of the EGUI includes a heart rate test guide, a body temperature test guide, a lung test guide, an ear test guide, a nose test guide, a mouth test guide, a heart test guide, a skin test guide, an electrocardiogram test guide, and a blood pressure test guide. , and one or more test guides among blood sugar test guides.

In the above example, the healthcare device 100 displays a 'heart rate check' button on the screen of the output device 105, and displays a heart rate check guide when a button selection command is received. In some embodiments, the heart rate test guide screen includes displaying an image or video of bringing the sensor module 107 of the healthcare device 100 near the heart. When the sensor module 107 approaches the heart, heart rate is measured. In some embodiments, the heart rate check guide includes deriving measurements for a plurality of contact points. As shown in FIG. 4B , this guidance may include displaying a contact sequence for a plurality of contact points on a guide screen. In this case, the heart rate may be measured based on the measured value for each point.

In addition, the healthcare device 100 displays a 'body temperature check' button on the screen of the output device 105, and displays a body temperature check guide when a button selection command is received. In some embodiments, the body temperature check guide screen includes displaying an image or video of bringing the sensor module 107 of the healthcare device 100 to the forehead. When the sensor module 107 touches the forehead, body temperature measurement is initiated.

In addition, the healthcare device 100 displays a 'lung examination' button on the screen of the output device 105, and displays a lung examination guide when a button selection command is received. In some embodiments, the lung examination guide screen includes displaying an image or video of bringing the sensor module 107 (eg, the acoustic sensor module 107a) of the healthcare device 100 to the back. When the sensor module 107 touches the back, lung measurement is initiated. In addition, the lung test guide displays a figure screen capable of inducing deep breathing for more accurate measurement during lung measurement. The figure screen may be, for example, a screen in which a circular figure is repeatedly enlarged and then reduced.

In addition, the healthcare device 100 displays the 'Ear Exam' button on the screen of the output device 105, and displays an ear examination guide screen when a button selection command is received. In some embodiments, the ear examination guide screen includes displaying an image or video of bringing the camera module 106 of the healthcare device 100 to the ear hole. In some embodiments, the output device 105 may display a possible view as a real-time preview. In some embodiments, the preview may be displayed on the screen of the electronic device 110 of the caregiver. The healthcare device 100 starts shooting when a view of the structure inside the ear is displayed as a preview.

In addition, the healthcare device 100 displays a 'nose examination' button on the screen of the output device 105, and displays a nose examination guide screen when a button selection command is received. In some embodiments, the nose examination guide screen includes displaying an image or video of bringing the camera module 106 of the healthcare device 100 into the nose. In some embodiments, the output device 105 may display a possible view as a real-time preview. The healthcare device 100 starts shooting when a view of the structure in the nose is displayed as a preview.

In addition, the healthcare device 100 displays the 'mouth examination' button on the screen of the output device 105, and displays the mouth examination guide when a button selection command is received. In some embodiments, the mouth examination guide screen includes displaying an image or video of bringing the camera module 106 of the healthcare device 100 into the mouth. In some embodiments, the output device 105 may display a possible view as a real-time preview. The healthcare device 100 starts shooting when a view of the structure in the mouth is displayed as a preview.

In addition, the health care device 100 displays a 'heart test' button on the screen of the output device 105, and displays a heart test guide when a button selection command is received. In some embodiments, the heart examination guide screen includes displaying an image or video of bringing the sensor module 107 of the healthcare device 100 near the chest. When the healthcare device 100 approaches the chest, heart sound or heart rate measurement starts.

In addition, the healthcare device 100 displays a 'skin test' button on the screen of the output device 105, and displays a skin test guide when a button selection command is received. In some embodiments, the skin examination guide screen includes displaying an image or video aiming the camera module 106 of the healthcare device 100 at the skin. In some embodiments, the output device 105 may display a possible view as a real-time preview. The healthcare device 100 starts shooting when a view of the skin is displayed as a preview.

In some embodiments, the healthcare device 100 may provide a start countdown before starting measurement or recording. The countdown result may be displayed using a dial shape.

In some embodiments, the healthcare device 100 may display a message saying 'Measurement has been completed' when normal measurement (or shooting) is performed, and may display shooting and completion buttons again. If the measurement (or shooting) fails, a “measurement failed” message may be displayed along with a shooting or completion button again.

The healthcare device 100 generates health state data of a care target based on sensing data accurately measured through EGUI, and sends the health state data to the electronic device 100, 140, 150 or the service server 130. can also supply

Also, in some embodiments, the healthcare device 100 may be configured to detect and correct shaking during the image data measurement process to generate a non-shake image. To this end, the healthcare device 100 may include a correction model including an artificial neural network structure configured to calculate a correction image from which shaking is removed by extracting features from an image.

In some embodiments, the healthcare device 100 includes a feature extraction unit and a shake extraction unit.

The feature extraction unit may be configured to extract feature values from an input image using a plurality of filters. The feature values are used to extract feature points in the input image.

The feature extraction unit may include, for example, a CNN structure, but is not limited thereto.

When a plurality of input images are input to the feature extraction unit, feature points based on characteristics of each input image are extracted and provided to the shake extraction unit.

The shake extraction unit is configured to receive feature points of each successive input image and compare feature points for each input image to calculate a degree of shake.

The shake extraction unit may include, for example, an LSTM or RNN structure, but is not limited thereto.

In some embodiments, the shake extractor may be configured to select feature points that move relatively little, excluding feature points that move relatively much, and calculate the degree of shake between successive input images based on the selected feature points.

In some embodiments, the feature point moving relatively little may be a feature point having a movement amount less than a threshold value. Also, in some embodiments, the threshold value may be a feature point threshold value due to shaking for a sequence consisting of the one or more previous input images and the current input image, calculated based on the movement value of the feature point for each one or more previous input images. .

Parameters of the calibration model may be learned such that a difference between an actual value and a predicted value (ie, a calibration result) is minimized. The calibration model may complete learning of the calibration model by finely adjusting parameters of the feature extraction unit and parameters of the shake extraction unit.

Using the correction model learned in this way, the healthcare device 100 may display a correction image from which shake is removed as a preview.

When the healthcare device 100 receives the frame image detected by the camera module 104 in real time as an input image in the pre-learned calibration model, it calculates the degree of shaking by calculating feature points for each frame, and based on the calculated degree of shaking It is also possible to generate a corrected image from which shaking is removed by correcting the input image.

Also, in some embodiments, the healthcare device 100 may be further configured to remove noise from sensing data. For example, the healthcare device 100 may be further configured to perform image noise filtering on the captured original image or signal noise filtering on the measured signal data (eg, sound noise filtering).

The caregiver's electronic device 110, the medical staff's electronic device 140, and the guardian's electronic device 150 are implemented as terminal devices configured to transmit/receive data with the service server 130 through the telecommunication network 120. . The electronic devices 110, 140, and 150 include at least one processor capable of processing data, a memory that stores data, and a communication unit that transmits/receives data. In certain embodiments, the electronic devices 110, 140, and 150 may include components similar to those of FIG. 2 . The electronic devices 110, 140, and 150 may include, for example, laptop computers, other computing devices, tablets, cellular phones, smart phones, smart watches, smart glasses, head mounted displays (HMDs), other mobile devices, and other wearable devices. Could be a device.

In certain embodiments, the electronic devices 110, 140, and 150 may install a care application that provides care services provided by the integrated care system 1 and other services related to health when executed by the processor. . The care application is configured to provide a menu for selecting and executing various actions.

The menu includes one or more of care diary operation, notice book operation, announcement operation, album operation, schedule operation, meal plan operation, medication request operation, attendance book operation, personal health record operation, disease prediction operation, health information portal operation, and smart order operation. and is configured to prompt receiving a user command to initiate an action.

The notification book operation is: When the caregiver's electronic device 110 receives the caregiver's care record information for the caregiver, a communication service shared with other electronic devices (eg, the caregiver's electronic device 150) is provided.

The notice operation: provides a service of sharing notices from the caregiver's electronic device 110 in the care task support system 1 60 to other electronic devices (eg, the guardian's electronic device 150).

The album operation provides a service of sharing an image of a care target captured by the caregiver's electronic device 110 with the caregiver's electronic device 150 . The image of the subject of care may include, for example, a daily life of the subject of care, a picture of a care service activity, and the like.

The schedule table operation provides a service for sharing the caregiver's schedule or the caregiver's schedule input into the caregiver's electronic device 110 with another electronic device (eg, the caregiver's electronic device 150).

The meal table operation provides a service of sharing meal information of a care target input into the caregiver's electronic device 110 with another electronic device (eg, the guardian's electronic device 150).

The operation of the medication request form: Provides a service for sharing medication information of a subject to be cared for, input into the electronic device 110 of the caregiver or the electronic device 140 of the medical staff, with another electronic device (eg, the electronic device 150 of the guardian). do.

The attendance unit operation: When the caregiver's electronic device 110 receives attendance confirmation information, attendance information is generated based on the received attendance confirmation information, and the attendance information is transmitted to the service server 130 so that the caregiver attends the attendance It provides a management service that updates details in real time. In some embodiments, the attendance confirmation information may be received by tagging the caregiver's electronic device 110 with an identification means assigned to the place where the caregiver resides. The electronic device 110 of the caregiver may obtain information on the attendance time and the attendance location through tagging, and may generate attendance information including the attendance confirmation, attendance time, and attendance location information.

The Personal Health Record (PHR) operation: A service for displaying various health state information included in the health state data acquired through the healthcare device 100 on the electronic devices 110, 140, and 150. to provide. In some embodiments, the health status information may be edited in a preset personal health record form and displayed on the screen of the corresponding electronic device 110 , 140 , or 150 .

The disease predicting operation: The integrated care system 1 provides a service of predicting a disease possibility of a care target based on the health state data. In some embodiments, the operation of analyzing the health state data to predict the possibility of the disease may be performed by the service server 130 . This will be described in more detail with reference to FIG. 6 below.

The operation of the health information portal: Provides a service that comprehensively supplies various health and disease information and health management method information.

The smart order information provides a service for purchasing various welfare products and health products.

The care diary operation provides a service of generating a care diary including health status information and care information obtained for each care date in the health care device 100 or the caregiver's electronic device 110 . The electronic device 110 of the caregiver may create a daily care diary including details of service provision, notification book, commuting record, inputted special matters, and the like.

The operation of each of the electronic devices 110, 140, and 150 will be described in more detail with reference to FIG. 10 below.

The service server 130 is a plurality of computer systems or computer software implemented as a network server. Here, the network server refers to a computer system and a computer that are connected to sub-devices capable of communicating with other network servers through a computer network such as a private intranet or the Internet to receive a task execution request, perform tasks in response, and provide performance results. Software (network server program). However, in addition to these network server programs, it should be understood as a broad concept including a series of application programs that operate on a network server and various databases built therein in some cases.

The service server 130 may function as a node connected to a blockchain network.

In some embodiments, the service server 130 may be configured to secure data integrity, confidentiality, and security through a cloud computing environment and authentication. In addition, the service server 130 implements communication (eg, Wifi-Direct) for real-time streaming of the care application of the device 100 and the devices 110, 140, and 150 to minimize latency while providing broadband coverage. It is configured to send/receive data. As such, the service server 130 built in the cloud environment may secure economic feasibility and flexibility of the service.

6 is a schematic diagram of a system architecture of a service server 130, according to an embodiment of the present application.

Referring to FIG. 6 , the service server 130 includes a device management unit 131, an application service unit 132, a social channel service unit 133, and a machine learning service unit 134. In some embodiments, the service server 130 includes a security service unit 135, a user session management unit 136, a ledger providing unit 137, a first database log unit 138, and a second database log unit ( 139) may further include some or all of them.

The device management unit 131 may include at least one processor that processes a communication service operation for the healthcare device 100 . The device management unit 131 receives health state data from the healthcare device 100 or the caregiver's electronic device 110 and processes the received health state data to obtain a photographed image, audio, and audio data included in the health state data. Health condition information such as a measurement signal, a body temperature value, and other measurement information is acquired.

In addition, the device management unit 131 stores the acquired health state information in the integrated care system 1 . In some embodiments, the device management unit 131 may be configured to store acquired health state data only when it is accurately measured. When the device management unit 131 receives a result determined to be accurate from the machine learning service unit 134, it may store corresponding health state information. When a determination result that is not accurate is received, the device management unit 131 rejects the acquired health state data without storing it. The service server 130 may transmit a re-measurement request to the healthcare device 100 .

In some embodiments, the device management unit 131 may process a message received from a healthcare device or electronic device using RabbitMQ. The device management unit 131 distributes nodes when transferring data between clients such as the healthcare device 100 and the devices 110, 140, and 150 and the server 130 and uses a broker to ensure speed. It may also be configured to use a message queue.

The device management unit 131 processes the health status inquiry message received from the electronic device through the RabbitMQ method, and inquires and provides registered health status data such as image, voice, and body temperature.

In some embodiments, the integrated management system 1 may include a distributed DB 201 and a relational DB 203. The device management unit 131 may store health state information in the distributed DB 201 and the relational DB 203 .

This will be described in detail with reference to FIG. 9 below.

The application service unit 132 interacts with the healthcare device 100 or the electronic devices 110 , 140 , and 150 to provide an operation of a care application, and performs overall operations.

The application service unit 132 may perform an operation of interacting with the caregiver's electronic device and the healthcare device in order to execute a specific application pre-installed on the caregiver's electronic device and the healthcare device. The application service unit 132 may transmit/receive data so that one or more operations of the care application are executed in the caregiver's electronic device 110 . For example, the application service unit 132 may transmit data for executing a care diary operation or data for executing an attendance book operation in the caregiver's electronic device 110, or may receive a result thereof.

In addition, the application service unit 132 may transmit/receive data so that one or more operations of the care application are executed in the guardian's electronic device 150 or the care task support system 1 60 . For example, the application service unit 132 may transmit corresponding data to the guardian's electronic device 150 or the care task support system 1 60 so that information on a subject to be cared for and care detail information may be confirmed.

In addition, the application service unit 132 may be configured to grant authority to each user of each component 100, 110, 140, 150, and 160 or manage user information.

The social channel service unit 133 may be configured to exchange messages related to a non-face-to-face medical treatment service between users of the electronic devices 110, 140, and 150 through a social media channel such as Kakao Talk, in conjunction with social media such as KakaoTalk. may be

When the social channel service unit 133 receives a message for requesting a non-face-to-face medical treatment service from the caregiver's electronic device 110 through a channel of social media, the social channel service unit 133 analyzes the message to provide remote information to the caregiver in charge of the caregiver. A medical appointment may be reserved, or the electronic device 140 of the primary care physician of the subject of care may be connected.

The social channel service unit 133 may transmit a channel message to the medical staff's electronic device 140 when reserving remote medical treatment or connecting to the attending physician. The channel message may include health state data and/or related data stored in the DBs 201 and 203.

The integrated care system 1 connects the healthcare device 100, the caregiver's electronic device 110, the service server 130, and the medical staff's electronic device 140 through the social channel service unit 133. The integrated care system 1 may provide a non-face-to-face medical service.

In some embodiments, the channel service unit is configured to be linked to an RDBMS, and may search health status data and/or related data of a corresponding care target from the linked DB and generate a channel message including the retrieved data.

The machine learning service unit 134 inputs the health state data of the object of care obtained from the healthcare device 100 into at least one pre-learned judgment model to determine the accuracy of the user's health state data or to determine the accuracy of the user's health state data. A prediction service for determining a health condition may be provided.

In some embodiments, the machine learning service unit 134 may include a first judgment model pretrained using a first training data set based on health state data obtained from the healthcare device 100 .

The first judgment model is a pre-learned machine learning model to determine whether the measured health state data has been accurately measured.

The first judgment model may include an artificial neural network structure that extracts features from input data and calculates a probability value for classifying whether the input data is an accurate measurement class or an inaccurate measurement class based on the extracted features. . The artificial neural network structure may be, for example, a CNN structure or other NN structure.

The first training data set may include a plurality of first training samples. Each first training sample is based on sample health state data obtained before learning from the healthcare device 100 . The first training sample includes sample health state data and label data indicating measurement accuracy of the corresponding health state data. The label data may include a first label value when the measurement of the health state data of the corresponding sample is accurate, and a second label value when the measurement is not accurate.

The label data is based on the type of sensing data included in the health state data.

The first judgment model may be trained by fine-tuning model parameters such that a difference between predicted values and actual values is minimized. Here, the predicted value is a classification result, and the actual value may be a value of label data.

When the machine learning service unit 134 determines that the health state data measured by the first judgment model is accurately measured, it may transmit the determination result to the device management unit 131 .

When the machine learning service unit 134 determines that the health state data measured by the first judgment model is not accurately measured, the device management unit does not immediately store the health state data determined to be not accurately measured, A re-measurement message may be transmitted to the healthcare device 100 to induce generation of accurate health state data.

Also, the machine learning service unit 134 may transmit the determination result to the application service unit 132 . The application service unit 132 may transmit a result of the determination and deliver whether or not the measurer (eg, caregiver) is properly using the application. For example, as shown in FIG. 5, a determination result of “good” may be provided.

Also, in some embodiments, the machine learning service unit 134 may include a second judgment model pre-learned using a second training data set based on health state data obtained from a healthcare device.

7 is a schematic diagram of a self-diagnosis service using a second judgment model according to an embodiment of the present application.

Referring to FIG. 7 , the second judgment model is pre-learned to determine the possibility of a disease of a care target based on measured health state data.

The second judgment model may include an artificial neural network structure that extracts features from input data and calculates a probability that the input data has a disease based on the extracted features as a probability value. The artificial neural network structure may be, for example, a CNN structure or other NN structure.

In some embodiments, the second judgment model may be configured to perform a multi-task operation capable of determining a plurality of disease possibilities. Then, as shown in FIG. 7 , the possibility of multiple diseases may be simultaneously calculated.

The second training data set may include a plurality of second training samples. Each second training sample is based on sample health state data obtained before learning from the healthcare device 100 . The second training sample includes sample health state data and label data including a disease value indicated by the corresponding health state. The label data may be a disease value that can be diagnosed from sample health state data.

The second judgment model may be learned by fine-tuning model parameters such that a difference between predicted values and actual values is minimized. Here, the prediction value is a probability value representing the possibility of a disease, and the actual value may be a value of label data.

The machine learning service unit 134 may calculate a diagnosis result for the care target based on the probability value of the disease calculated by the second judgment model. In some embodiments, the diagnosis result may include information about a disease that the subject of care is likely to have. The likely disease information may indicate a dangerous state. Whether or not the dangerous state may be determined when a probability value equal to or greater than a preset threshold probability value is calculated.

In addition, the machine learning service unit 134 may store prevention information for each disease in advance. When it is determined by the second judgment model that the subject of care is highly likely to have a specific disease, the machine learning service unit 134 may search pre-stored prevention information for the determined specific disease. The machine learning service unit 134 may provide diagnosis results and prevention information to the components 110 , 130 , and 140 related to the object of care.

As such, the service server 130 may provide a self-diagnosis service as a service within a care application by using a judgment model generated by learning images and signals captured by the healthcare device 100 . In addition, the service server 130 may provide preventive health information tailored to guardians based on learning results. These self-diagnosis results or basic healthcare information (eg, original measurement values) used to analyze them may be provided according to the life cycle in the form of a mobile health record (PHR, Personal Health Record) or customized for each subject of care. may be

In addition, the service server 130 is linked with the medical staff's electronic device 140 to share simple doctor consultation and health information of patients with reduced mobility, and to support first aid tasks necessary for patients at home by caregivers in case of emergency. do. To this end, as described above, the social channel service unit 133 transmits a channel message to the electronic device 140 of the medical staff. The channel message may include a diagnosis result based on the possibility of a disease of the second judgment model of the machine learning service unit 134 and health state data received through the device management unit 131 and stored in the DBs 201 and 203. may be

In some embodiments, the channel message may be transmitted to the medical staff's electronic device 140 including only partial health state information related to a disease determined to be at risk in the health state data. The social channel service unit 133 may filter some health condition information related to a disease. For example, if it is determined that the person has a mouth-related disease, the social channel service unit 133 may generate a channel message by filtering mouth-related information (eg, an image taken inside the mouth).

The security service unit 135 is configured to perform a security operation such as granting authority for each user account or authenticating authority. The security service unit 135 may be implemented to perform the security operation by executing, for example, OAUTH service, but is not limited thereto.

The security operation of the security service unit 135 may be referred to as a primary security operation performed prior to blockchain storage, which may be referred to as a secondary security operation when associated with blockchain storage.

The user session management unit 136 is configured to manage user sessions. In some embodiments, the user session management unit 136 may store user session information in the relational DB 203 .

8 is a schematic diagram of a process of storing blockchain data according to an embodiment of the present application.

Referring to FIG. 8 , the ledger providing unit 137 is configured to perform an operation of generating blockchain data and storing the health state data and personal information of the object of care on the blockchain network 20 . The health state data and personal information may be included in the body of FIG. 8 within a block.

For example, the ledger providing unit 137 generates blockchain data including some or all of the health state data and personal information of the subject of care, and performs a transaction for adding a new block including the blockchain data to the blockchain. It can also propagate to the blockchain network 20. Alternatively, the ledger providing unit 137 may propagate a transaction adding new blockchain data to a block of the blockchain to the blockchain network 20 . To this end, the ledger providing unit 137 may include a pre-issued private key.

The blockchain network 20 refers to a network that records transactions on the distribution, modification, or deletion of blockchain data including the health condition data, and includes a public blockchain and a private blockchain. Alternatively, it may be composed of at least one combination of hybrid blockchains.

The service server 130 encrypts the image, voice, signal, and other measurement information of the health condition data stored primarily in the relational DB 203 at or at a time preset by the ledger providing unit 137 and stores the information on the blockchain. Create blockchain data to be included as a ledger. In some embodiments, the ledger providing unit 137 may be configured to use a scheduler program such as Cron and/or a batch program to generate the blockchain data.

In addition, the service server 130 is configured to perform an operation of storing generated blockchain data on a blockchain network.

The service server 130 generates a transaction including the blockchain data on a blockchain network and propagates the blockchain data to other nodes. Propagated transactions are linked to each node's blockchain. Through this propagation process, the blockchain data may be stored on a blockchain network.

Also, the ledger providing unit 137 may include its own public key or other public key of the blockchain network. The ledger providing unit 137 protects personal information by sharing encrypted data when information is exchanged with other components 110, 140, 150, and 160 using a public key.

The first database log unit 138 is configured to manage storage logs of the distributed DB 201 . The second database log unit 139 is configured to manage storage logs of the relational DB 203 .

The service server 130 is connected to each DB (201, 203). The device management unit 110 stores measurement results (ie, health state information) in the distributed DB 201 and stores table key values of the health state information stored in the distributed DB 201 in the relational DB 203. may be

The distributed DB 201 stores original sources of large-volume health data, such as care-related information such as care records of care subjects, images included in health state data, sound measurement values, and other health information. The distributed DB 201 may be NoSQL, but is not limited thereto.

The integrated management system 1 utilizes the distributed DB 201 to store a large amount of data, but has an effect of lowering the storage speed and distributing the load of the server node. The distributed DB 201 may include a search engine and a DB cluster. The search engine may include, for example, elasticsearch, but is not limited thereto. The DB cluster may include, for example, a Cassandra cluster, but is not limited thereto.

In addition, the integrated management system 1 may further include an in-memory DB 205 .

9 is a schematic diagram illustrating an operation of an integrated care system utilizing a distributed DB and an in-memory DB according to an embodiment of the present application.

Referring to FIG. 9 , the in-memory DB 205 is configured to store information retrieved from a search engine of the distributed DB 201 and to return the stored information to the service server 130 when requested.

The back end (eg, the device management unit 131 or the application service unit 132) checks whether a previously searched result is stored in the in-memory DB 205 (S510). When the search result is confirmed (S521), the DB 205 may deliver data of the search result to the backend. If the search result is not confirmed, target data is searched in the search engine of the distributed DB 201 (S522). The searched data is stored in the in-memory DB 205 so that it can be immediately searched in the in-memory DB 205 in the next search (S530). Also, the searched target data may be stored in a search engine. After being stored in the in-memory DB 205, the data is again transmitted to the backend (S540).

The in-memory DB 205 is configured to have capacity and performance enough to sufficiently use server block resources even when the number of queries soars.

The relational DB 203 primarily stores health state data received from the device management unit 131 . The integrated care system 1 may first store health state data in the relational DB 203 and then store them in the distributed DB 201 . Also, the relational DB 203 may store table key values of health state data stored in the distributed DB 201 .

In addition, the relational DB 203 may delete the primarily stored health state data after it has been used to generate blockchain data. Since the original data is stored on the distributed DB 201 and the blockchain network, the storage space can be used more efficiently through such deletion.

Also, the relational DB 203 may be connected to the user session management unit 136 and the ledger providing unit 137 . The relational DB 203 may be configured to provide user session information and ledger information.

The care task support system (1) 60 is installed in a welfare institution that authorizes care-related tasks. The care work support system (1) 60 is configured to computerize various administrative tasks of home welfare institutions and manage care recipients and care managers.

It will be clear to those skilled in the art that the integrated care system 1 may include other components. For example, the integrated care system 1 may include other hardware elements required for the operations described herein, including an input device for data entry, and an output device for printing or other display of data. In addition, it may further include a network, a network interface, and a protocol connecting the integrated care system 1 and an external device (eg, a user terminal or an external database, etc.).

10 is a flowchart of an integrated care method according to another aspect of the present application.

The integrated care method of FIG. 10 may be performed by one or more computing devices such as the integrated care system 1 of FIG. 1 .

Referring to FIG. 10 , the integrated care method includes measuring the health status of the care target by the camera module 106 and the sensor module 107 of the healthcare device 100 and generating health state data of the care target. (S10) is included. In step S10, the health state data may be generated on a pre-installed care application.

In addition, the integrated care method includes obtaining at least one care information among diet information, care diary, and commuting information input to a care application installed on the caregiver's electronic device 110 (S20).

The information obtained in the steps S10 and S20 is transmitted to the service server 130.

The integrated care method includes a step (S30) of transmitting care information such as the received care log and commuting information to the care work support system (1) 60.

In addition, the integrated care method includes transmitting care information such as health information and a care diary of a care target to the guardian's electronic device 150 (S40).

The guardian's electronic device 150 directly shares the health state information obtained in step S10 and information related to the object of care in step S40 from the healthcare device 100 and the caregiver's electronic device 110. or shared from the health care device 100 or the caregiver's electronic device 110 through the service server 130. This sharing supports communication between caregivers and caregivers.

In addition, the integrated care method provides non-face-to-face medical treatment services based on the health condition information and care information generated by the care application installed on the health care device 100 and the electronic device 110 and the care task support system (1) 60. It may also include providing step (S50).

The non-face-to-face medical treatment service may include a remote medical treatment reservation service and a primary care physician connection service.

In some embodiments, the non-face-to-face medical treatment service may be initiated in response to receiving a non-face-to-face medical treatment service request message received from the caregiver's electronic device 110 through a social media channel.

In some embodiments, the non-face-to-face medical treatment service in step S50 may be performed based on the calculation result of the machine learning service unit 134 of the service server 130. The non-face-to-face medical treatment service may be performed when accurate health state data is measured in the first judgment model of the machine learning service unit 134 and/or when it is determined that the care target has a possibility of a disease in the second judgment model. there is.

The medical treatment history generated by performing the non-face-to-face medical treatment service may be stored as the care application. The stored medical treatment details may be provided to the guardian's electronic device 150 or may be used for additional medical treatment, chronic disease management, and the like.

In the case of implementing the embodiments of the present invention using hardware, application specific integrated circuits (ASICs) or digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable programmable PLDs (PLDs) configured to perform the embodiments of the present application. logic devices), field programmable gate arrays (FPGAs), etc. may be included in the components of the present application.

Operations by the integrated care system and method according to the embodiments of the present application described above may be at least partially implemented as a computer program and recorded on a computer-readable recording medium. For example, implemented together with a program product consisting of a computer-readable medium containing program code, which may be executed by a processor to perform any or all steps, operations, or processes described.

The computer-readable recording medium includes all types of recording devices in which data readable by a computer is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. In addition, computer-readable recording media may be distributed in computer systems connected through a network, and computer-readable codes may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing this embodiment can be easily understood by those skilled in the art to which this embodiment belongs.

The present invention reviewed above has been described with reference to the embodiments shown in the drawings, but this is only exemplary, and those skilled in the art will understand that various modifications and variations of the embodiments are possible therefrom. However, such modifications should be considered within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (13)

In the integrated care system,
A healthcare device that measures the health status of a subject to be cared for, acquires health status information, and generates health status data of the subject to be cared for - The healthcare device includes a plurality of types of sensor modules that measure each type of sensing data, respectively configured to generate health state data of the care target by using;
a caregiver's electronic device generating care information including a care diary for the care subject and requesting a non-face-to-face medical treatment service;
An electronic device of a medical staff providing a non-face-to-face medical treatment service based on the health condition information according to the request; and
Interact with the caregiver's electronic device and the healthcare device to execute a specific application pre-installed on the caregiver's electronic device and the healthcare device, receive the health state data and the caregiver's care diary, and receive the health state data A service server configured to store health state information included in at least one connected database and to provide a diagnosis service based on the health state data;
The service server,
a device management unit processing the received health state data to obtain health state information included in the health state data;
an application service management unit that performs an operation of interacting with the caregiver's electronic device and the healthcare device to execute a specific application pre-installed on the caregiver's electronic device and the healthcare device;
a social channel service unit configured to exchange a message related to a non-face-to-face medical treatment service between a caregiver's electronic device and a medical staff's electronic device through a channel on social media; and
Includes a machine learning service unit that inputs the acquired health state data of the object of care into at least one pre-learned judgment model to determine the accuracy of the user's health state data or to provide a prediction service for determining the user's health state, ,
The service server is connected to a distributed DB and a relational DB,
The device management unit,
After the health state information is primarily stored in a relational DB, the health state information is stored in a distributed DB,
The integrated care system,
Further comprising an in-memory DB configured to store information inquired from a search engine of the distributed DB and to return the stored information to the service server when requested;
The device management unit checks whether a previous search result is stored in the in-memory DB, and if the search result is confirmed, the in-memory DB transfers data of the search result to the device management unit, and the search result is If it is not confirmed, the target data is searched in the distributed DB, and the searched data is stored in the in-memory DB so that it can be immediately searched in the in-memory DB in the next search, stored in the in-memory DB, and then the data again Characterized in that is transmitted to the device management unit,
integrated care system. The method of claim 1, wherein the healthcare device,
a communication unit configured to transmit the health state data through electrical communication with the service server; and
Health that describes the health condition of the care subject by receiving a photographing result from a camera module that photographs the body composition of the care subject or receiving a result of measuring the health condition of the care subject from at least one sensor module among a plurality of types of sensor modules. a processor that generates state data;
The plurality of types of sensor modules include a sound sensor module, a body temperature sensor module, a blood pressure sensor module, and a blood sugar sensor module;
Characterized in that the health state data includes measurement data of one or more of lung sounds, gastrointestinal sounds, heartbeat data, body temperature data, blood pressure data, and blood sugar data.
integrated care system. According to claim 1,
One side of the camera module and one side of each of the plurality of types of sensor modules include a coupling member configured to be mechanically and electrically detachable from one side of the healthcare device,
The healthcare device
Obtaining an image of the body composition of the care subject in a state in which the camera module is attached,
After the attached camera module is detached, another measurement value is obtained through the newly attached sensor module,
Characterized in that it generates health state data including sequentially acquired images and measurement values,
integrated care system. According to claim 1,
The healthcare device,
further comprising a display displaying a screen of an operation guide that supports obtaining health condition data describing a more accurate health condition;
The operation guide is one of a heart rate test guide, a body temperature test guide, a lung test guide, an ear test guide, a nose test guide, a mouth test guide, a heart test guide, a skin test guide, an electrocardiogram test guide, a blood pressure test guide, and a blood sugar test guide. Including the above inspection guide,
The healthcare device,
Displays the 'lung test' button on the screen of the output device, displays the lung test guide when a button selection command is received,
The lung test guide screen is configured to display an image or video of bringing the acoustic sensor module to the back, and to display a figure screen capable of inducing deep breathing for more accurate measurement during lung measurement. The figure screen, Characterized in that the screen repeats that the circular shape increases and decreases,
integrated care system. The method of claim 4,
The healthcare device,
Displays the 'mouth test' button on the screen of the output device, displays the mouth test guide when a button selection command is received,
The mouth examination guide screen is configured to display an image or video of bringing the camera module of the healthcare device into the mouth,
Characterized in that the caregiver's electronic device linked to the healthcare device displays the shooting view of the camera module as a real-time preview,
integrated care system. delete delete According to claim 1,
The machine learning service unit includes a first judgment model pre-trained using a first training data set based on health state data;
The first judgment model includes an artificial neural network structure pre-learned to determine whether the measured health state data is accurately measured,
The artificial neural network structure is configured to extract features from the input data and calculate a probability value for classifying whether the input data is an accurate measurement class or an inaccurate measurement class based on the extracted features,
The first training data set consists of a plurality of first training samples, each first training sample is based on sample health state data obtained before learning from a healthcare device, and the first training sample is a sample health state data, and label data indicating the accuracy of the corresponding health condition data,
integrated care system. The method of claim 8, wherein the device management unit,
When the health state information is accurately measured according to the determination result of the first judgment model, storage of the health state information is started,
Characterized in that a re-measurement request is transmitted to the healthcare device when the health state information is inaccurately measured according to the determination result of the first determination model.
integrated care system. According to claim 1,
The machine learning service unit includes a second judgment model pre-trained using a second training data set based on health state data obtained from a healthcare device,
The second judgment model includes an artificial neural network structure pre-learned to determine the possibility of a disease of a care target based on the measured health state data,
The artificial neural network structure is configured to extract a feature from the input data and calculate a probability that the input data has a disease based on the extracted feature as a probability value,
The second training data set consists of a plurality of second training samples, each second training sample is based on sample health state data obtained before learning from the healthcare device, and the second training sample is a sample health state Characterized in that it includes label data including the data and the disease value indicated by the corresponding health condition,
integrated care system. The method of claim 10, wherein the social channel service unit,
Characterized in that, in order to proceed with non-face-to-face medical treatment service, a channel message including health status information of the care target and a diagnosis result using the second judgment model is transmitted to the electronic device of the medical staff,
integrated care system. delete According to claim 1,
The service server is connected to the blockchain network as a node of the blockchain network, and generates health status data and personal information of the subject of care as blockchain data using a private key, and converts the blockchain data to the blockchain network. Characterized in that it further comprises a ledger providing unit stored in
integrated care system.

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