KR20210008267A - System for monitoring health condition of user and analysis method thereof - Google Patents

Abstract

The present invention relates to a system for monitoring a health condition of a user and an analysis method thereof which induce comprehensive life habit improvement. According to one embodiment of the present invention, the system comprises: a data collection unit to collect biometric data and living data for each user during a prescribed period, and manage the collected data through a database; a first data analysis unit to analyze a change the trend of blood sugar and blood pressure for each user based on the biometric data, and predict future blood pressure and blood sugar values for each user based on the analyzed result; a data learning unit to use machine learning to learn a correlation between changes in blood pressure and blood sugar level and a plurality of factors included in the biometric data and the living data; and a second data analysis unit to predict blood pressure and blood sugar values of a user based on a learned result when biometric data and living data of the user are inputted, and analyze feedback information associated with life habit improvement of the user.

Description

User's health status monitoring system and its analysis method {SYSTEM FOR MONITORING HEALTH CONDITION OF USER AND ANALYSIS METHOD THEREOF}

The present invention relates to a user's health condition monitoring system and its analysis method, and more specifically, by analyzing individual changes in blood pressure and blood sugar based on various information collected in real life, comprehensive nutrition, physical activity, medication and sleep, etc. It relates to a system that can induce customized improvement for a typical lifestyle and an analysis method thereof.

According to the results of the study, the proportion of the elderly over 80 years of age has been continuously increasing in the last 10 years. In particular, it is noteworthy that more than half of the elderly over 65 have three or more chronic diseases.

Chronic disease is a disease that develops and recurs over a long period of time, and high blood pressure, arthritis, and diabetes are representative chronic diseases. The causes of chronic diseases include genetic, smoking, lack of exercise, dietary habits, changes in physiological mechanisms of the body such as constant stress, variables in life, and environmental causes such as environmental pollution.

Since serious chronic diseases cause crisis and negative emotions in life itself beyond the physical problems caused by the disease itself, it is desirable to prevent chronic diseases in advance through self-care and monitoring for the whole life. Do. In other words, since chronic diseases are caused by a combination of various causes, it is essential to prevent the onset of chronic diseases by maintaining a healthy lifestyle.

Republic of Korea Patent Publication No. 10-2015-0090941 (2015.08.07)

The present invention was conceived according to the necessity as described above, and since chronically ill patients need to improve lifestyles for disease management as well as periodic checkups, a medication notification service and a medication notification service for improving lifestyles through feedback from self and medical staff The purpose is to provide a system and an analysis method capable of providing nutrition and exercise recording services.

Another object of the present invention is to provide a system and analysis method for providing user-customized feedback by managing data collected in real life of a user, and inducing comprehensive lifestyle improvement such as nutrition, physical activity, medication, and sleep.

The health status monitoring system of a user according to an embodiment of the present invention includes a data collection unit that collects biometric data and life data for each user for a predetermined period and manages the collected data through a database, and a user based on the biometric data. A first data analysis unit that analyzes the trend of blood pressure and blood sugar fluctuations for each and predicts future blood pressure and blood sugar value values for each user based on the analyzed results, and multiples included in each of the biometric data and life data using machine learning. When the data learning unit that learns the correlation between the factors of the dogs and changes in blood pressure and blood sugar, and the user's biometric data and life data are input, the user's blood pressure and blood sugar values are predicted based on the learned results, and the user's life It may include a second data analysis unit that analyzes the feedback information on the improvement of the habit.

According to an embodiment of the present invention, a plurality of factors included in the biometric data include weight, height, age, sex, chronic disease, family history, blood pressure, and blood sugar, and a plurality of factors included in life data May include nutrient intake information, exercise amount information, medication information, sleep information, and environmental information.

The second data analysis unit according to an embodiment of the present invention selects factors reflecting the user's characteristics from the user's biometric data and life data input based on the learned result, and applies a weight to the selected factors. The user's blood pressure and blood sugar levels can be predicted.

The second data analysis unit according to an embodiment of the present invention estimates an average value for a predetermined period for each of a plurality of factors included in the input user's biometric data and life data, and estimates the estimated value for a predetermined period. Factors with irregular change values based on the average value can be selected as factors reflecting the user's characteristics.

The health condition monitoring system of a user according to an embodiment of the present invention includes a monitoring terminal that receives and outputs the blood pressure and blood sugar numerical values predicted by the second data analysis unit and the feedback information analyzed by the second data analysis unit. It may contain more.

The analysis method of the health condition monitoring system of a user according to an embodiment of the present invention includes the steps of: a data collection unit collecting biometric data and life data for each user for a predetermined period, and a first data analysis unit for each user based on the biometric data. Analyzing the trend of blood pressure and blood sugar fluctuations, and predicting future blood pressure and blood sugar values for each user based on the analyzed result, a plurality of factors included in each of the biometric data and life data by the data learning unit using machine learning Learning the correlation between changes in blood pressure and blood sugar and the second data analysis unit, when the user's biometric data and life data are input, predicts the user's blood pressure and blood sugar value based on the learned result, It may include the step of analyzing feedback information on improvement of lifestyle habits.

In the step of analyzing the feedback information by the second data analysis unit according to an embodiment of the present invention, the factors reflecting the user's characteristics are selected from the user's biometric data and life data input based on the learned result of the second data analysis unit. And predicting the blood pressure and blood glucose values of the user by applying a weight to the selected factors by the second data analysis unit.

In the step of selecting factors that reflect the user's characteristics from the user's biometric data and life data input based on the learned result of the second data analysis unit according to an embodiment of the present invention, the second data analysis unit Factors reflecting the user's characteristics by estimating an average value for a predetermined period for each of a plurality of factors included in biometric data and living data, and factors having irregular change values based on the estimated average value for a predetermined period Can be selected as.

Meanwhile, according to an embodiment of the present invention, a computer-readable recording medium in which a program for executing the above-described method on a computer is recorded may be provided.

According to the monitoring system and analysis method provided as an embodiment of the present invention, the user can adjust health-related values such as blood pressure and blood sugar in a stable state through maintaining an appropriate lifestyle, and weight control for preventing chronic diseases. You can also get help.

In addition, according to the monitoring system and analysis method provided as an embodiment of the present invention, it is possible to reduce the dosage of blood pressure drugs and insulin for controlling blood sugar by improving blood pressure and blood sugar levels, so that users can maintain a healthy life. I can help.

1 is a block diagram showing a system for monitoring a user's health condition according to an embodiment of the present invention.
2 is a conceptual diagram illustrating a system for monitoring a user's health condition according to an embodiment of the present invention.
3 is a conceptual diagram illustrating a process of predicting blood sugar and blood pressure values of a second data analysis unit according to an embodiment of the present invention.
4 is a flowchart illustrating a method of analyzing a health condition monitoring system of a user according to an embodiment of the present invention.
5 is a flowchart illustrating a process of predicting blood sugar and blood pressure values by a second data analysis unit according to an embodiment of the present invention.

The terms used in the present specification will be briefly described, and the present invention will be described in detail.

The terms used in the present invention have been selected from general terms that are currently widely used while considering functions in the present invention, but this may vary depending on the intention or precedent of a technician working in the field, the emergence of new technologies, and the like. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning of the terms will be described in detail in the description of the corresponding invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall contents of the present invention, not a simple name of the term.

When a part of the specification is said to "include" a certain component, it means that other components may be further included rather than excluding other components unless otherwise stated. In addition, terms such as "... unit" and "module" described in the specification mean units that process at least one function or operation, which may be implemented as hardware or software, or as a combination of hardware and software. . In addition, when a part is said to be "connected" with another part throughout the specification, this includes not only a case in which it is "directly connected", but also a case in which a part is connected "with another configuration in the middle."

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are assigned to similar parts throughout the specification.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram showing a system for monitoring a user's health condition according to an embodiment of the present invention.

Referring to FIG. 1, the health condition monitoring system of a user according to an embodiment of the present invention collects biometric data and life data for each user for a predetermined period, and manages the collected data through the database 200. A collection unit 10, a first data analysis unit 20 that analyzes the trend of blood pressure and blood sugar fluctuations for each user based on the biometric data, and predicts future blood pressure and blood sugar values for each user based on the analyzed result, When a data learning unit 30 that learns the correlation between changes in blood pressure and blood sugar and a plurality of factors included in each of the biometric data and life data using machine learning, and the user's biometric data and life data are input, the learned It may include a second data analysis unit 40 that predicts the user's blood pressure and blood sugar level values based on the result, and analyzes feedback information on improvement of the user's lifestyle.

Referring to FIG. 1, the above-described data collection unit 10, first data analysis unit 20, data learning unit 30, and second data analysis unit 40 may be included in the analysis device 100, The analysis device 100 may be linked to a database 200 for storing and managing data and a monitoring terminal 300 to be described later through wired/wireless network communication. The database 200 may be formed in a separate configuration from the analysis device 100 as shown in FIG. 1, but may be included in the analysis device 100 as a data storage of the analysis device 100.

At this time, referring to FIG. 1, the analysis apparatus 100 according to an embodiment of the present invention may include a communication unit 50 for wired/wireless network communication. Through several communication modules included in the communication unit 50, the data collection unit 10 transmits and receives data to and from a wearable terminal (e.g., a smartwatch, etc.) or a medical record management server that generates the user's biometric data and life data. can do. In addition, through various communication modules included in the communication unit 50, the second data analysis unit 40 may transmit the user's blood pressure and blood sugar level values, feedback information, and the like to the monitoring terminal 300.

For example, the communication unit 50 includes a short-range communication module, such as a Bluetooth communication module, a Bluetooth low energy (BLE) communication module, a Zigbee communication module, a beacon communication module, etc., and Wi-Fi communication. A module, an ultra wideband (UWB) communication module, a LoRaWAN communication module, and the like may be included, but are not limited to the above example.

Meanwhile, referring to FIG. 1, a system for monitoring a user's health status according to an embodiment of the present invention includes a blood pressure and blood glucose value predicted by a second data analysis unit 40 and a second data analysis unit 40. It may further include a monitoring terminal 300 for receiving and outputting the feedback information analyzed by.

For example, the monitoring terminal 300 is a terminal capable of wired or wireless network communication, and includes a smartphone, a smart watch, a portable multimedia player (PMP), a personal digital assistant (PDA), a desktop PC, and a laptop. ) It may be a PC or a tablet PC.

The user of the monitoring terminal 300 according to an embodiment of the present invention may be a patient suffering from a chronic disease as well as a medical staff who checks and diagnoses a patient's chronic disease. That is, the monitoring terminal 300 can enable the medical staff to check and monitor the patient's condition in real time by running the application, and the patient himself checks the analyzed result according to the patient's condition through the analysis device 100 You can try to improve your habits.

In other words, through the monitoring terminal 300, a person with chronic disease checks the results of analysis of information on overall lifestyles, such as meals, exercise, medications, and sleep conditions, which affect their health status, and self-correction for lifestyles. And maintenance can be performed. In addition, through the monitoring terminal 300, the attending physician of the chronically diseased person can continuously check changes in health status of the chronically diseased person, so that more accurate diagnosis and treatment can be performed for each patient.

2 is a conceptual diagram illustrating a system for monitoring a user's health condition according to an embodiment of the present invention. Hereinafter, the overall process of the monitoring service provided by the system will be described in detail with reference to FIG. 2.

The data collection unit 10 of the analysis apparatus 100 according to an embodiment of the present invention determines the biometric data related to the user's genetic and physical elements and the life data related to the user's lifestyle from various terminals or servers. It can be collected for a period of time (eg, 1 month, etc.). The analysis apparatus 100 may provide information on improving lifestyles appropriate for each user in consideration of biometric information such as physical conditions and genetic factors of the user by collecting life data as well as biometric data.

Referring to FIG. 2, the biometric data may include a plurality of factors such as weight, height, age, sex, chronic disease, family history, blood pressure, and blood sugar. In addition, the life data may include a plurality of factors such as nutrient intake information, exercise amount information, medication information, sleep information, and environment information.

For example, among various factors of biometric data, blood pressure and blood sugar may be collected by transmitting a biometric signal measured by a user's worn terminal (eg, a smart watch) to the data collection unit 10. In addition, body weight, height, age, sex, presence of chronic diseases and family history excluding blood pressure and blood sugar may be directly input from the user through the monitoring terminal 300 or be collected by being transmitted to the data collection unit 10 from the medical record management server. I can. In this case, when directly input from the user through the monitoring terminal 300, a procedure in which unique information such as a login ID and password is input in advance by the user may be performed to identify user information.

Referring to FIG. 2, among various factors of life data, the sleep information comprehensively analyzes information on the user's sleeping habits such as the degree of turning and snoring, and information on the user's sleeping environment such as temperature, humidity, and illumination. Is the result. When the information on the user's sleeping habits and information on the sleeping environment measured by the user's wearing terminal (e.g., a smart watch) or a sensor installed in the user's sleeping place is transmitted to the data collection unit 10, the data collection unit ( In 10), sleep information indicating the user's sleep time and sleep state may be generated by analyzing the above-described information.

Meanwhile, nutrient intake information, exercise amount information, medication information, and environmental information excluding sleep information are measured at the user's wearing terminal (eg, smart watch, etc.), input by the user through the monitoring terminal 300, or data from an external server, etc. It may be collected by being transferred to the collection unit 10.

For example, the nutrient intake information may be collected by inputting (or recording) meal information that the user ate breakfast, lunch, and dinner through the monitoring terminal 300 as meal information. The amount of exercise information may be collected by measuring the number of steps, exercise time, and intensity according to the type of exercise through a user's worn terminal (eg, a smart watch). The medication information may be collected by inputting whether medication is taken by a user through the monitoring terminal 300, and transmitting a medication achievement rate based on the results of the examination from a medical record-related server. The environmental information may be collected by transferring the fine dust information for each region stored in the Meteorological Administration server to the data collection unit 10.

A plurality of factors included in the aforementioned biometric data and life data are selected as major factors that can directly affect lifestyle improvement for the prevention of chronic diseases. For example, blood pressure and blood sugar are major biomarkers that accurately show the onset and state of chronic diseases. In addition, diet, exercise, medication, sleep, and environment are key life indicators directly related to the treatment of chronic diseases.

2, when biometric data and life data are collected for a predetermined period (eg, 1 month, etc.) by the data collection unit 10 according to an embodiment of the present invention, the first data analysis unit 20 May analyze the trend of blood pressure and blood sugar fluctuations and future numerical values based on the biometric data (ie, health status analysis). The future referred to herein refers to a point in time after a certain period of time has elapsed based on the point in time at which the analysis is performed, which may be preset or changed by the user on a daily, weekly, monthly or annual basis.

For example, the first data analysis unit 20 may analyze how the user's blood pressure and blood sugar have changed over time based on information on blood pressure and blood sugar included in the biometric data. That is, the first data analysis unit 20 may analyze a change trend of a user's blood pressure and blood sugar through a time series analysis of blood pressure and blood sugar included in the biometric data.

In addition, the first data analysis unit 20 may predict future numerical values of blood pressure and blood sugar indicating how the user's blood pressure and blood sugar will change in the future based on the change trend of the user's blood pressure and blood sugar. The future blood pressure and blood sugar values predicted by the first data analysis unit 20 indicate how the blood pressure and blood sugar values change in the future when the current state of the user is continuously maintained. That is, as described above, blood pressure and blood sugar correspond to major biomarkers that accurately show the onset and state of chronic diseases, so the future blood pressure and blood sugar values predicted by the first data analysis unit 20 are It can be used as an index to predict the progression of chronic diseases.

Meanwhile, although not shown in FIG. 2, the data learning unit 30 according to an embodiment of the present invention includes a plurality of factors included in each of the biometric data and the life data, and analyzed by the first data analysis unit 20. You can learn the correlation between changes in blood pressure and blood sugar.

For example, the data learning unit 30 determines how the user's blood pressure and blood sugar fluctuations change according to changes in weight, height, etc., according to changes in factors such as weight, height, age, family history, etc. And how the future numerical value of blood sugar may change. In addition, the data learning unit 30 determines how the blood pressure and blood sugar fluctuations change according to the factors included in the life data, such as the user's nutrient intake and ratio, exercise amount, medication achievement rate, sleep time, and the concentration of fine dust in the user's active area. You can analyze whether it shows a pattern or not.

Through the above-described process, the data learning unit 30 may comprehensively reflect different biometric information and lifestyle information for each user to the blood pressure and blood glucose fluctuation trends and future numerical values analyzed for each user. In other words, the data learning unit 30 is based on the information on the blood pressure and blood sugar numerical values analyzed for each user, based on the correlation indicating how each factor included in the biometric data and the life data affects the changes in blood pressure and blood sugar. You can learn comprehensively.

In this case, according to an embodiment of the present invention, a machine learning algorithm may be used in the learning process of the data learning unit 30 described above. For example, the machine learning algorithm may include a K-means clustering algorithm, a support vector machine algorithm, or a deep neural network algorithm suitable for correlation analysis.

2, the second data analysis unit 40 according to an embodiment of the present invention is based on the analysis and learning results by the first data analysis unit 20 and the data learning unit 30 Data analysis can be performed to predict health changes and provide health management information according to the predicted results.

That is, the second data analysis unit 40 may predict the user's blood pressure and blood sugar level values based on the user's biometric data and life data collected or input in real time. In addition, the second data analysis unit 40 may generate feedback information for managing blood sugar and blood pressure based on the predicted user's blood pressure and blood sugar value.

For example, the second data analysis unit 40 may predict future blood sugar and blood pressure values based on the biometric data input in real time and the previously collected biometric data. The second data analysis unit 40 analyzes the correlation between the predicted blood glucose and blood pressure values and the factors included in the life data such as meals, exercise, medication, and sleep using the learning results by the data learning unit 30. After analyzing, it is possible to generate feedback information including contents of improvement directions for each factor included in the living data according to the correlation.

That is, in the feedback information generated by the second data analysis unit 40, what diet should be eaten tomorrow based on the current blood sugar level, and how much physical activity is needed to improve the current blood pressure and blood sugar level. , Depending on the type of chronic disease (eg, hypertension, diabetes, hyperlipidemia, obesity, etc.), information on how to comprehensively manage each factor may be included.

For example, such feedback information may be expressed as an accurate numerical value for improvement directions such as nutrient intake and ratio, exercise intensity, exercise time, number of medications, medication achievement rate, and sleep time. It can be expressed in text form such as "It is recommended to increase it".

Also, referring to FIG. 2, feedback information may be separately generated for each of blood pressure and blood sugar. That is, the second data analysis unit 40 may generate one piece of feedback information for comprehensively managing blood pressure and blood sugar, and generates two pieces of feedback information as feedback information on blood pressure and feedback information on blood sugar. You may. When the blood pressure and blood sugar are separated from each other to generate feedback information, the blood pressure and blood sugar can be managed more closely by the user.

Hereinafter, a process of predicting blood sugar and blood pressure values by the second data analysis unit 40 will be described in more detail with reference to FIG. 3.

3 is a conceptual diagram illustrating a process of predicting blood sugar and blood pressure numerical values of the second data analysis unit 40 according to an embodiment of the present invention.

Referring to FIG. 3, the second data analysis unit 40 according to an embodiment of the present invention selects factors reflecting the user's characteristics from the user's biometric data and life data input based on the learned result, By applying weights to the selected factors, the user's blood pressure and blood glucose level values can be predicted.

Since not only different genetic and physical factors affecting chronic diseases are different for each user, but also different lifestyle and environment, the appropriate feedback information can be provided for each user only if this can be properly reflected in the analysis process. Accordingly, the second data analysis unit 40 may first select factors reflecting the user's characteristics in the process of predicting the user's blood pressure and blood sugar level values.

For example, as shown in FIG. 3, the second data analysis unit 40 includes (1) the calorie intake rate and the intake rate of major nutrients (ie carbohydrates, protein, fat), (2) high-intensity exercise time, calorie consumption and number of steps, (3) Medication achievement rate, (4) Sleep time, (5) Fine dust concentration, (6) Three factors reflecting the characteristics of each user can be selected from among the six factors of biometric information including weight, age, and height. have. For User 1, factors (1), (3) and (6) can be selected, and for User 2, factors (1), (3) and (6) can be selected. have.

At this time, the second data analysis unit 40 estimates an average value for a predetermined period for each of the input user's biometric data and a plurality of factors included in the life data, and based on the estimated average value for the predetermined period. As a result, factors with irregular change values can be selected as factors reflecting the user's characteristics.

In this case, the change value refers to a change in the deviation value according to the result of comparing the average value estimated for a predetermined period with the numerical value for each time point. And, the irregular change value means that the result of comparing the deviation values for each time point according to the passage of time does not maintain a constant value. For example, deviation values of 4, 6, 4, 6, ?? And 4, 8, 3, 5, ?? In the case of fluctuations as described above, in the former case, the comparison result of the deviation values at each time point remains constant at 2, but in the latter case, the comparison result of the deviation values at each time point fluctuates to 4, 5, 2, etc. In the case of, the second data analysis unit 40 may determine that it has an irregular change value.

In order to effectively prevent and treat chronic diseases, it is important to maintain a regular lifestyle, so factors having irregular change values as in the above example can be considered to have a direct effect on chronic diseases. Accordingly, the second data analysis unit 40 according to an embodiment of the present invention may select factors having irregular change values as factors reflecting a user's characteristics related to chronic diseases.

Referring to FIG. 3, after selecting factors reflecting user characteristics, the second data analysis unit 40 according to an embodiment of the present invention may assign weights to the selected factors. As described above, since the factors reflecting the user's characteristics are factors that directly affect chronic diseases, the blood sugar and blood pressure values for each user can be predicted by weighting the corresponding factors.

In addition, the second data analysis unit 40 according to an embodiment of the present invention may apply different weights of selected factors according to the presence or absence of a chronic disease and a family history among various factors of the biometric data. For example, in the case of a user suffering from a chronic disease, a larger weight value may be assigned than a user who does not. In addition, in the case of a user having a family history, a larger weight value may be assigned than a user who does not.

4 is a flowchart illustrating a method of analyzing a health condition monitoring system of a user according to an embodiment of the present invention.

Referring to FIG. 4, in the analysis method of the health condition monitoring system of a user according to an embodiment of the present invention, the data collection unit 10 collects biometric data and life data for each user for a predetermined period (S410). , The first data analysis unit 20 analyzes the fluctuation trend of blood pressure and blood sugar for each user based on the biometric data, and predicts future blood pressure and blood sugar value values for each user based on the analyzed result (S420), A step of learning, by the data learning unit 30, a correlation between changes in blood pressure and blood sugar, and a plurality of factors included in each of the biometric data and life data using machine learning (S430) and the second data analysis unit 40 When the user's biometric data and life data are input, predicting the user's blood pressure and blood sugar level values based on the learned result, and analyzing feedback information regarding the improvement of the user's lifestyle (S440). have.

5 is a flowchart illustrating a process of predicting blood sugar and blood pressure numerical values by the second data analysis unit 40 according to an embodiment of the present invention.

Referring to FIG. 5, the step of analyzing the feedback information by the second data analysis unit 40 according to an embodiment of the present invention (S440) is input based on a learned result by the second data analysis unit 40. Selecting factors reflecting the user's characteristics from the user's biometric data and life data (S510), and the second data analysis unit 40 applies weights to the selected factors to determine the user's blood pressure and blood sugar values. It may include a predicting step (S520).

In the step S510 of selecting factors reflecting the user's characteristics from the user's biometric data and life data input based on the learned result by the second data analysis unit 40 according to an embodiment of the present invention, the second The data analysis unit 40 estimates an average value for a predetermined period for each of the input user's biometric data and a plurality of factors included in the life data, and an irregular change value based on the estimated average value for a predetermined period. Factors having a can be selected as factors reflecting the user's characteristics.

In connection with the method according to an embodiment of the present invention, the contents of the above-described system may be applied. Accordingly, with respect to the method, descriptions of the same contents as those of the above-described system have been omitted.

Meanwhile, according to an embodiment of the present invention, a computer-readable recording medium in which a program for executing the above-described method on a computer is recorded may be provided. In other words, the above-described method can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer operating the program using a computer-readable medium. Further, the structure of the data used in the above-described method may be recorded on a computer-readable medium through various means. A recording medium for recording executable computer programs or codes for performing various methods of the present invention should not be understood as including temporary objects such as carrier waves or signals. The computer-readable medium may include a storage medium such as a magnetic storage medium (eg, ROM, floppy disk, hard disk, etc.), and an optical reading medium (eg, CD-ROM, DVD, etc.).

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. .

10: data collection unit
20: first data analysis unit
30: Data Learning Department
40: second data analysis unit
50: communication department
100: analysis device
200: database
300: monitoring terminal

Claims (10)

In the user's health condition monitoring system,
A data collection unit that collects biometric data and life data for each user for a predetermined period and manages the collected data through a database;
A first data analysis unit that analyzes a trend of blood pressure and blood sugar fluctuations for each user based on the biometric data and predicts future blood pressure and blood sugar values for each user based on the analyzed result;
A data learning unit that learns a correlation between changes in blood pressure and blood sugar and a plurality of factors included in each of the biometric data and life data using machine learning; And
When the user's biometric data and life data are input, the second data analysis unit predicts the blood pressure and blood sugar level values of the user based on the learned result, and analyzes feedback information regarding the improvement of the user's lifestyle. User's health condition monitoring system, characterized in that.
The method of claim 1,
The plurality of factors included in the biometric data include weight, height, age, sex, chronic disease, family history, blood pressure and blood sugar,
The health condition monitoring system of the user, characterized in that the plurality of factors included in the living data include nutrient intake information, exercise amount information, medication information, sleep information, and environment information.
The method of claim 1,
The second data analysis unit,
Based on the learned result, factors reflecting the user's characteristics are selected from the input user's biometric data and life data, and the user's blood pressure and blood glucose values are predicted by applying weights to the selected factors. User's health condition monitoring system, characterized in that.
The method of claim 3,
The second data analysis unit,
Estimating an average value for a predetermined period for each of a plurality of factors included in the input user's biometric data and life data,
The health condition monitoring system of a user, characterized in that, based on the estimated average value for a predetermined period, factors having irregular change values are selected as factors reflecting the characteristics of the user.
The method of claim 1,
And a monitoring terminal receiving and outputting the blood pressure and blood glucose values predicted by the second data analysis unit and the feedback information analyzed by the second data analysis unit.
In the analysis method of the user's health condition monitoring system,
Collecting, by a data collection unit, biometric data and life data for each user for a predetermined period;
Analyzing, by a first data analysis unit, changes in blood pressure and blood sugar for each user based on the biometric data, and predicting future blood pressure and blood sugar values for each user based on the analyzed result;
Learning, by a data learning unit, a correlation between changes in blood pressure and blood sugar and a plurality of factors included in each of the biometric data and life data using machine learning; And
When the user's biometric data and life data are input by the second data analysis unit, predicting the user's blood pressure and blood sugar level values based on the learned result, and analyzing feedback information regarding the improvement of the user's lifestyle Analysis method of the health status monitoring system of the user comprising a.
The method of claim 6,
The plurality of factors included in the biometric data include weight, height, age, sex, chronic disease, family history, blood pressure and blood sugar,
A method of analyzing a health condition monitoring system of a user, characterized in that the plurality of factors included in the living data include nutrient intake information, exercise amount information, medication information, sleep information, and environment information.
The method of claim 6,
Analyzing the feedback information by the second data analysis unit,
Selecting, by the second data analysis unit, factors reflecting the characteristics of the user from the input biometric data and life data of the user based on the learned result; And
And predicting the blood pressure and blood glucose values of the user by applying weights to the selected factors by the second data analysis unit.
The method of claim 8,
In the step of selecting factors reflecting the characteristics of the user from the input biometric data and life data of the user based on the learned result by the second data analysis unit,
The second data analysis unit estimates an average value for a predetermined period for each of a plurality of factors included in the input biometric data and life data of the user, and irregular changes based on the estimated average value for the predetermined period. A method of analyzing a health condition monitoring system of a user, characterized in that factors having values are selected as factors reflecting the characteristics of the user.
A computer-readable recording medium on which a program for implementing the method of any one of claims 6 to 9 is recorded.

Images