KR102716304B1 - Health care apparatus for chronic disease patients and health care method for chronic disease patients - Google Patents

Abstract

A health management device and method for a patient with a chronic disease are disclosed. According to one aspect, the health management device for a patient with a chronic disease includes: a data acquisition unit that acquires one or more lifestyle data and one or more physical change data of a user; a graph generation unit that analyzes a correlation between each lifestyle and each physical change based on the acquired one or more lifestyle data and the acquired one or more physical change data, and generates a unidirectional graph based on the correlation between each lifestyle and each physical change; a subgraph extraction unit that extracts a subgraph including lifestyle and physical changes having a correlation of a certain level or higher from the generated unidirectional graph; and a feedback generation unit that generates feedback for inducing a change in lifestyle based on the extracted subgraph.

Description

{Health care apparatus for chronic disease patients and health care method for chronic disease patients}

Relates to health care devices and methods for patients with chronic diseases.

As we enter an aging society, the number of patients with chronic diseases is rapidly increasing. In particular, the prevalence of metabolic syndrome, which has a significant impact on the development of diabetes and cardiovascular disease, is rapidly increasing.

Recently, a significant number of the top ten causes of death in our country are deaths due to chronic diseases such as cerebrovascular disease, heart disease, and diabetes. These diseases have high mortality rates as well as high incidence and prevalence rates. These diseases can be a major cause of social problems such as a decline in quality of life or economic loss due to the disease itself.

Chronic diseases such as diabetes, hypertension, and dyslipidemia are diseases that require continuous management at home. When these diseases occur, they require continuous management and treatment throughout one's life, and they also have a significant impact on increasing medical expenses at the national level.

The purpose is to provide a health management device and method for patients with chronic diseases.

A health management device for a chronic disease patient according to an aspect of the present invention may include: a data acquisition unit that acquires one or more lifestyle data and one or more physical change data of a user; a graph generation unit that analyzes a correlation between each lifestyle and each physical change based on the acquired one or more lifestyle data and the acquired one or more physical change data, and generates a unidirectional graph based on the correlation between each lifestyle and each physical change; a subgraph extraction unit that extracts a subgraph including lifestyle and physical change having a correlation of a certain level or higher from the generated unidirectional graph; and a feedback generation unit that generates feedback for inducing a change in lifestyle based on the extracted subgraph.

The above lifestyle habits may include at least one of exercise, diet, smoking, and drinking, and the above physical changes may include at least one of blood pressure, blood sugar, weight, and sleep.

The above graph generation unit can analyze the correlation between each lifestyle habit and each physical change by accumulating each lifestyle habit data per day within a predetermined time window and calculating the correlation coefficient between each lifestyle habit data accumulated per day and each physical change data.

The above graph generation unit can generate the unidirectional graph by expressing each lifestyle habit and each physical change as a node and expressing the correlation between each lifestyle habit and each physical change as an edge.

The above feedback generation unit can analyze one or more of the acquired body change data to extract a body change requiring management, extract a lifestyle habit having a certain level or higher of correlation with the extracted body change from the extracted subgraph, and generate feedback to induce a change in the extracted lifestyle habit.

A health management method for a patient with a chronic disease according to another aspect may include the steps of: acquiring one or more lifestyle data and one or more physical change data of a user; analyzing a correlation between each lifestyle and each physical change based on the acquired one or more lifestyle data and the acquired one or more physical change data; generating a unidirectional graph based on the correlation between each lifestyle and each physical change; extracting a subgraph including lifestyle and physical change having a correlation of a certain level or higher from the generated unidirectional graph; and generating feedback for inducing a change in lifestyle based on the extracted subgraph.

The above lifestyle habits may include at least one of exercise, diet, smoking, and drinking, and the above physical changes may include at least one of blood pressure, blood sugar, weight, and sleep.

The step of analyzing the above correlation can analyze the correlation between each lifestyle habit and each physical change by accumulating each lifestyle habit data per day within a predetermined time window and calculating the correlation coefficient between each lifestyle habit data accumulated per day and each physical change data.

The step of generating the above unidirectional graph can generate the above unidirectional graph by expressing each lifestyle habit and each physical change as a node and expressing the correlation between each lifestyle habit and each physical change as an edge.

The step of generating the above feedback may include analyzing one or more of the acquired body change data to extract a body change requiring management, extracting a lifestyle habit having a certain level or higher of correlation with the extracted body change from the extracted subgraph, and generating feedback to induce a change in the extracted lifestyle habit.

By analyzing the correlation between lifestyle habits and physical changes using the lifestyle data and physical change data of patients with chronic diseases, we can provide users with customized feedback for improving lifestyle habits, thereby improving and managing the lifestyles of patients with chronic diseases.

FIG. 1 is a drawing illustrating a health management device for a chronic disease patient according to an exemplary embodiment.
Figures 2 and 3 are examples of daily cumulative data on lifestyle habits.
Figure 4 is an example of a one-way graph.
Figure 5 is an example of a subgraph extracted from a unidirectional graph.
FIG. 6 is a diagram illustrating a computing environment including a computing device suitable for use in exemplary embodiments.
FIG. 7 is a diagram illustrating a health management method for a chronic disease patient according to an exemplary embodiment.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the attached drawings. When adding reference numerals to components in each drawing, it should be noted that the same components are given the same numerals as much as possible even if they are shown in different drawings. In addition, when describing the present invention, if it is determined that a specific description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

Meanwhile, for each step, each step may occur in a different order than stated, unless the context clearly states a specific order. That is, each step may be performed in the same order as stated, may be performed substantially simultaneously, or may be performed in the opposite order.

The terms described below are terms defined in consideration of their functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, their definitions should be made based on the contents throughout this specification.

Although the terms first, second, etc. may be used to describe various components, the components should not be limited by the terms. The terms are used only to distinguish one component from another. The singular expression includes the plural expression unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" or "has" are intended to specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

In addition, the division of components in this specification is only a division by the main function of each component. In other words, two or more components may be combined into one component, or one component may be divided into two or more by more detailed functions. In addition to its own main function, each component may additionally perform some or all of the functions of other components, and some of the main functions of each component may be performed exclusively by other components. Each component may be implemented by hardware or software, or by a combination of hardware and software.

FIG. 1 is a diagram illustrating a health management device for a chronic disease patient according to an exemplary embodiment, FIGS. 2 and 3 are diagrams illustrating daily cumulative data of lifestyle habits, FIG. 4 is an example diagram of a unidirectional graph, and FIG. 5 is an example diagram of a subgraph extracted from a unidirectional graph.

The health management device (100) for a chronic disease patient of FIG. 1 (hereinafter, “health management device”) is a device that can provide a customized health management service for an individual patient by providing feedback to the patient for managing the health of a chronic disease patient, and can be mounted on an electronic device. Here, the electronic device can include a mobile phone, a smart phone, a tablet, a laptop, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a navigation device, an MP3 player, a digital camera, a wearable device, and the like, and the wearable device can include a glasses type, a wristwatch type, a wrist band type, a ring type, an earring type, a belt type, a necklace type, an ankle band type, a thigh band type, an arm band type, and the like. However, the electronic device is not limited to the examples described above, and the wearable device is also not limited to the examples described above.

According to an exemplary embodiment, an application for using a customized health care service may be installed in the electronic device, and in this case, the operations of the health care device (100) described below may be performed through the application installed in the electronic device.

Referring to FIGS. 1 to 5, the health management device (100) may include a data acquisition unit (110), a graph generation unit (120), a subgraph extraction unit (130), and a feedback generation unit (140).

The data acquisition unit (110) can acquire data on one or more lifestyle habits of the user (hereinafter, lifestyle data) and data on one or more physical changes (hereinafter, physical change data). Here, the user may be a patient with a chronic disease, and the chronic disease may include diabetes, hypertension, dyslipidemia, obesity, etc.

According to an exemplary embodiment, lifestyle habits are things that can be changed by the user's own will, and may include exercise, eating, smoking, and drinking, and physical changes are things that can be changed by lifestyle habits, and may include blood pressure, blood sugar, weight, and sleep, etc.

For example, among the lifestyle data, exercise data may include exercise date, exercise time, exercise type, exercise amount, subjective exercise intensity, etc., and among the lifestyle data, meal data may include meal time, meal type, meal amount, calorie intake, caffeine or beverage intake, etc., and among the lifestyle data, smoking data may include smoking type, smoking amount, etc., and among the lifestyle data, drinking data may include drinking type, drinking amount, etc. In addition, among the body change data, blood pressure data may include systolic blood pressure, diastolic blood pressure, pulse, presence of high/low blood pressure symptoms, etc., and among the body change data, blood sugar data may include fasting blood sugar, pre-prandial blood sugar, postprandial blood sugar, presence of high/low blood sugar symptoms, etc., and among the body change data, weight data may include weight, target weight, body fat, waist circumference, body mass index (BMI), etc., and among the body change data, sleep data may include presence of sleep disorder, date of occurrence of sleep disorder, sleep time, etc.

According to an exemplary embodiment, the data acquisition unit (110) can acquire lifestyle data and body change data by receiving lifestyle data and body change data from a user through an input means. At this time, the input means may include a key pad, a dome switch, a mouse, a touch pad, a jog wheel, a jog switch, hardware or software buttons, etc. In particular, when the touch pad forms a mutual layer structure with the display, it can be called a touch screen.

According to an exemplary embodiment, the data acquisition unit (110) can analyze and recognize the user's voice or image to acquire lifestyle data and body change data. For example, the user can record meals through the voice recognition function, and upload blood test results and/or prescriptions and record meals through images. To this end, the data acquisition unit (110) may include a microphone for receiving the user's voice data, and a voice recognition module for recognizing the input user's voice, or an image analysis module for receiving and analyzing an image.

According to an exemplary embodiment, the data acquisition unit (110) may acquire lifestyle data and body change data by receiving lifestyle data and body change data about the user from an external device. To this end, the data acquisition unit (110) may include a wired/wireless communication module. For example, the external device may include a blood pressure measuring device, a blood sugar measuring device, a weight measuring device, a body fat measuring device, an exercise amount measuring device, etc., as a device that measures and/or stores lifestyle data and/or body change data.

The graph generation unit (120) can analyze the correlation between each lifestyle habit and each physical change based on lifestyle habit data and physical change data.

According to an exemplary embodiment, the graph generation unit (120) can accumulate each lifestyle data on a daily basis within a predetermined time window, and analyze the correlation between each accumulated lifestyle data and each body change data. At this time, the predetermined time window can be 1 week, 2 weeks, 3 weeks, or 4 weeks, but is not limited thereto. For example, as illustrated in FIGS. 2 and 3, the graph generation unit (120) can accumulate each lifestyle data, such as exercise data (e.g., amount of exercise), meal data (e.g., amount of meal), smoking data (e.g., amount of smoking), and drinking data (e.g., amount of drinking), on a daily basis. In addition, the graph generation unit (120) can analyze the correlation between each lifestyle and each physical change by calculating the correlation coefficient between the daily cumulative value of each lifestyle data and each physical change data, such as blood pressure data (e.g., systolic blood pressure, diastolic blood pressure, etc.), blood sugar data (e.g., fasting blood sugar), weight data (e.g., weight), and sleep data (e.g., sleep time). At this time, the graph generation unit (120) can utilize various known correlation analysis techniques.

The graph generation unit (120) can generate a unidirectional graph expressed as nodes and edges using the analyzed correlation. For example, as illustrated in FIG. 4, the graph generation unit (120) can generate a unidirectional graph by expressing each lifestyle (e.g., exercise, diet, smoking, drinking) and each physical change (e.g., blood pressure, blood sugar, weight, sleep) as nodes, and expressing the correlation between each lifestyle and each physical change as edges. At this time, the thickness of the edge can be determined according to the absolute value of the correlation coefficient between the lifestyle and the physical change. For example, the higher the correlation, that is, the larger the absolute value of the correlation coefficient, the thicker the edge can be.

The subgraph extraction unit (130) can extract a subgraph including lifestyle habits and physical changes that have a certain level of correlation or higher from the generated unidirectional graph. For example, as illustrated in FIG. 5, the subgraph extraction unit (130) can determine lifestyle habits and physical changes whose absolute value of the correlation coefficient is higher than a predetermined threshold value (e.g., 0.5), and extract a subgraph including nodes representing lifestyle habits and physical changes whose absolute value of the correlation coefficient is higher than the predetermined threshold value, and edges connecting the nodes, from the unidirectional graph. In this case, the predetermined threshold value may be 0.5, but this is only one embodiment and is not limited thereto.

The feedback generation unit (140) can generate feedback for inducing a change in lifestyle habits based on the extracted subgraph. For example, the feedback generation unit (140) can analyze the user's body change data to extract a body change requiring management, and extract a lifestyle habit having a certain level or higher of correlation with the body change requiring management from the subgraph. In addition, the feedback generation unit (140) can generate feedback for inducing a change in lifestyle habits having a certain level or higher of correlation with the body change requiring management in order to improve the body change requiring management. At this time, the feedback can include voice data, text data, video data, etc. For example, the feedback can include a mediation message including a method for improving lifestyle habits, video content for inducing an improvement in lifestyle habits, etc.

According to an exemplary embodiment, the health care device (100) may further include a display unit (150).

The display unit (150) can display extracted subgraphs and generated feedback, etc. through a display.

The health management device (100) according to an exemplary embodiment analyzes the correlation between the lifestyle and physical changes using the lifestyle data and physical change data of the individual user and provides the user with feedback for improving the lifestyle or physical changes, thereby enabling customized analysis and feedback for the individual user. In addition, the health management device (100) can analyze the lifestyle with a high correlation with the physical changes requiring management based on the correlation between the lifestyle and physical changes, thereby enabling more simple generation and provision of feedback. In addition, the health management device (100) can analyze the data clearly even in data with many collected items by extracting and utilizing a subgraph that includes only items with a high correlation.

FIG. 6 is a diagram illustrating a computing environment including a computing device suitable for use in exemplary embodiments. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and the computing environment may include additional components other than those described below.

The illustrated computing environment (400) may include a computing device (410). According to one embodiment, the computing device (410) may include one or more components included in a health care device (100) as described with reference to FIGS. 1 to 5, for example.

The computing device (410) may include at least one processor (411), a computer-readable storage medium (412), and a communication bus (413). The processor (411) may cause the computing device (410) to operate according to the exemplary embodiments mentioned above. For example, the processor (411) may execute one or more programs (414) stored in the computer-readable storage medium (412). The one or more programs (414) may include one or more computer-executable instructions, which, when executed by the processor (411), may be configured to cause the computing device (410) to perform operations according to the exemplary embodiments.

The computer-readable storage medium (412) can store computer-executable instructions or program code, program data, and/or other suitable forms of information. A program (414) stored in the computer-readable storage medium (412) can include a set of instructions executable by the processor (411). In one embodiment, the computer-readable storage medium (412) can be a memory (volatile memory such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, any other form of storage medium that can be accessed by the computing device (410) and can store desired information, or a suitable combination thereof.

A communication bus (413) may interconnect various other components of the computing device (410), including a processor (411) and a computer-readable storage medium (412).

The computing device (410) may also include one or more input/output interfaces (415) that provide interfaces for one or more input/output devices (420) and one or more network communication interfaces (416). The input/output interfaces (415) and the network communication interfaces (416) may be coupled to the communication bus (413). The input/output devices (420) may be coupled to other components of the computing device (410) via the input/output interfaces (415). The input/output devices (420) may include, for example, input devices such as a pointing device (such as a mouse or a trackpad), a keyboard, a touch input device (such as a touchpad or a touchscreen), a voice or sound input device, various types of sensor devices, and/or photographing devices, and/or output devices such as a display device, a printer, speakers, and/or a network card. The input/output device (420) may be included within the computing device (410) as a component that constitutes the computing device (410), or may be connected to the computing device (410) as a separate device distinct from the computing device (410).

FIG. 7 is a diagram illustrating a health management method for a chronic disease patient according to an exemplary embodiment.

The health management method for a chronic disease patient of Fig. 7 can be performed by the health management device (100) of Fig. 1.

Referring to FIG. 7, the health management device can obtain one or more lifestyle data and one or more body change data of the user (510).

For example, a health management device can obtain lifestyle data and physical change data by receiving lifestyle data and physical change data from a user through an input means.

As another example, a health care device can analyze and recognize a user's voice or image to obtain lifestyle data and body change data.

As another example, the health management device can obtain lifestyle data and body change data by receiving lifestyle data and body change data about the user from an external device. For example, the external device may include a blood pressure measuring device, a blood sugar measuring device, a weight measuring device, a body fat measuring device, an exercise amount measuring device, etc., as a device that measures and/or stores lifestyle data and/or body change data.

The health management device can analyze the correlation between each lifestyle habit and each physical change based on lifestyle data and physical change data (520).

For example, the health management device can accumulate each lifestyle data on a daily basis within a predetermined time window and analyze the correlation between each accumulated lifestyle data and each body change data. At this time, the predetermined time window can be 1 week, 2 weeks, 3 weeks, or 4 weeks, but is not limited thereto. The health management device can utilize various known correlation analysis techniques.

The health management device can generate a unidirectional graph expressed by nodes and edges using the analyzed correlation (530). For example, the health management device can generate a unidirectional graph by expressing each lifestyle (e.g., exercise, diet, smoking, drinking) and each physical change (e.g., blood pressure, blood sugar, weight, sleep) as nodes, and expressing the correlation between each lifestyle and each physical change as edges. At this time, the thickness of the edge can be determined according to the absolute value of the correlation coefficient between the lifestyle and the physical change. For example, the higher the correlation, that is, the larger the absolute value of the correlation coefficient, the thicker the edge can be.

The health management device can extract a subgraph including lifestyle habits and physical changes that have a certain level of correlation or higher from the generated unidirectional graph (540). For example, the health management device can determine lifestyle habits and physical changes whose absolute value of the correlation coefficient is higher than a predetermined threshold value (e.g., 0.5), and extract a subgraph including nodes representing lifestyle habits and physical changes whose absolute value of the correlation coefficient is higher than the predetermined threshold value, and edges connecting the nodes, from the unidirectional graph.

The health management device can generate feedback for inducing a change in lifestyle habits based on the extracted subgraph (550). For example, the health management device can analyze the user's body change data to extract a body change requiring management, and extract a lifestyle having a certain level or higher of correlation with the body change requiring management from the subgraph. In addition, the health management device can generate feedback for inducing a change in the extracted lifestyle habits in order to improve the body change requiring management. At this time, the feedback can include voice data, text data, video data, etc. For example, the feedback can include an intervention message including a method for improving lifestyle habits, video content for inducing an improvement in lifestyle habits, etc.

The health care device can display the extracted subgraphs and generated feedback, etc. through a display (560).

The above-described embodiments can be implemented as computer-readable codes on a computer-readable recording medium. The computer-readable recording medium can include all kinds of recording devices that store data that can be read by a computer system. Examples of the computer-readable recording medium can include ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, optical disks, etc. In addition, the computer-readable recording medium can be distributed over network-connected computer systems, and can be written and executed as computer-readable codes in a distributed manner.

The present invention has been described so far with reference to preferred embodiments thereof. Those skilled in the art will understand that the present invention can be implemented in modified forms without departing from the essential characteristics of the present invention. Accordingly, the scope of the present invention should not be limited to the above-described embodiments, but should be interpreted to include various embodiments within a scope equivalent to the contents described in the claims.

100: Health care devices
110: Data entry section
120: Graph generation section
130: Subgraph Extraction Section
140: Feedback generation section
150: Display

Claims (10)

A data acquisition unit that acquires one or more lifestyle habit data and one or more body change data of a user;
A graph generation unit which analyzes the correlation between each lifestyle habit and each physical change by calculating a correlation coefficient between each lifestyle habit and each physical change based on one or more of the acquired lifestyle habit data and one or more of the acquired physical change data, and generates a one-way graph based on the correlation between each lifestyle habit and each physical change, wherein the correlation coefficient includes a positive correlation coefficient and a negative correlation coefficient;
In the above-mentioned generated unidirectional graph, a subgraph extraction unit extracts lifestyle habits and physical changes whose absolute value of the correlation coefficient is greater than a predetermined threshold value and generates a subgraph, wherein the correlation coefficient between the extracted lifestyle habits and physical changes is displayed together in the subgraph; and
A feedback generation unit that generates feedback to induce changes in lifestyle habits based on the generated subgraph;
Health care devices for patients with chronic diseases. In the first paragraph,
The above lifestyle habits include at least one of exercise, diet, smoking and drinking, and the above physical changes include at least one of blood pressure, blood sugar, weight and sleep.
Health care devices for patients with chronic diseases. In the first paragraph,
The above graph generation unit,
Accumulate each lifestyle data within a given time window on a daily basis and calculate the correlation coefficient based on each lifestyle data accumulated on a daily basis and each body change data.
Health care devices for patients with chronic diseases. In the first paragraph,
The above graph generation unit,
Each of the above lifestyle habits and each of the above physical changes are expressed as nodes, the correlation between each of the above lifestyle habits and each of the above physical changes is expressed as edges, and the correlation coefficient between each of the above lifestyle habits and each of the above physical changes is displayed together to create the above one-way graph.
Health care devices for patients with chronic diseases. In the first paragraph,
The above feedback generation unit,
By analyzing one or more of the acquired body change data, a body change requiring management is extracted, and from the extracted subgraph, a lifestyle habit having a certain level of correlation with the extracted body change is extracted, and feedback is generated to induce a change in the extracted lifestyle habit.
Health care devices for patients with chronic diseases. A step of acquiring one or more user's lifestyle data and one or more body change data;
A step of analyzing the correlation between each lifestyle habit and each physical change by calculating a correlation coefficient between each lifestyle habit and each physical change based on one or more of the acquired lifestyle habit data and one or more of the acquired physical change data, wherein the correlation coefficient includes a positive correlation coefficient and a negative correlation coefficient;
A step of generating a one-way graph based on the correlation between each of the above lifestyle habits and each of the above physical changes;
In the above-mentioned generated one-way graph, a step of extracting lifestyle habits and physical changes whose absolute value of the correlation coefficient is greater than a predetermined threshold value and generating a sub-graph, wherein the sub-graph displays the correlation coefficient between the extracted lifestyle habits and physical changes; and
A step of generating feedback for inducing a change in lifestyle habits based on the generated subgraph; including;
Health management methods for patients with chronic diseases. In Article 6,
The above lifestyle habits include at least one of exercise, diet, smoking and drinking, and the above physical changes include at least one of blood pressure, blood sugar, weight and sleep.
Health management methods for patients with chronic diseases. In Article 6,
The steps for analyzing the above correlation are:
Accumulate each lifestyle data within a given time window on a daily basis and calculate the correlation coefficient based on each lifestyle data accumulated on a daily basis and each body change data.
Health management methods for patients with chronic diseases. In Article 6,
The steps for generating the above unidirectional graph are:
Each of the above lifestyle habits and each of the above physical changes are expressed as nodes, the correlation between each of the above lifestyle habits and each of the above physical changes is expressed as edges, and the correlation coefficient between each of the above lifestyle habits and each of the above physical changes is displayed together to create the above one-way graph.
Health management methods for patients with chronic diseases. In Article 6,
The steps for generating the above feedback are:
By analyzing one or more of the above-obtained body change data, a body change requiring management is extracted, and from the extracted subgraph, a lifestyle habit having a certain level of correlation with the extracted body change is extracted, and feedback is generated to induce a change in the extracted lifestyle habit.
Health management methods for patients with chronic diseases.