KR102187952B1 - Health enhancement information providing apparatus and method - Google Patents

Abstract

It relates to an apparatus and method for providing health information. More specifically, a storage unit for storing a 3D body image related to human body data; A processor that generates a 3D body type image corresponding to the user's human body data, and determines a type of body type based on the 3D body type image; And a display unit that displays the 3D body image and the body shape.

Description

Device and method for providing health promotion information {HEALTH ENHANCEMENT INFORMATION PROVIDING APPARATUS AND METHOD}

It relates to a device and method that provides customized health promotion information to users, and more specifically, result data, medical history, and life log (sleep/eating/drinking/smoking/exercise/activity) provided by the PCR (Polymerase Chain Reaction) diagnostic kit. )Data, indoor/outdoor air quality (ultra-fine dust/chemicals/temperature/humidity/ultraviolet) data, personalized exercise prescription, lifestyle change prescription, customized wellness (cosmetics/scalp care/diet) product recommendation counseling and customized It relates to an apparatus and method for providing a regular delivery service of a product.

As the human genome map has been completed, it is possible to measure the risk of disease, diagnose or predict disease, and predict response to drugs. In addition, recent studies have revealed the functions of various genes in humans.

Accordingly, there is a growing interest in technology that uses PCR technology to perform mutation analysis on a specific gene of a specific person, predicts the degree of risk for a specific disease, and induces the disease to be prevented. In Korea, where there were relatively many restrictions, the Bioethics and Safety Act was revised on June 30, 2016, and six health-related obesity, cholesterol, triglycerides, blood sugar and blood pressure, caffeine metabolism and beauty-related six vitamin C metabolism, skin Individually requested genetic testing (DTC: Direct-TO-Consumer) was allowed for 46 genetic tests for a total of 12 items, including pigmentation, skin aging, skin elasticity, hair loss, and hair thickness. However, PCR diagnosis today has a limitation in that it cannot provide a service that provides customized prescription information using result data while putting an expensive price burden on the user. It is impossible to discriminate and predict the high-risk group simply through the gene information, and in order to calculate the exact probability of incidence, a single base of the individual whole genome other than the information on the single-nucleotide polymorphism (SNP) of the corresponding gene. Sequence polymorphism (SNP: Single-Nucleotide polymorphism) correlation analysis, military history, lifestyle changes, indoor/outdoor atmospheric environment changes, etc.A database (DB) that combines all information on the interaction of gene-living environments and deep neural network inference based machine learning algorithm It is possible only by securing. By converging genome big data and artificial intelligence analysis technology, individuals at high risk of health are identified through the correlation analysis of individual genotypes and lifestyle changes, and exercise prescriptions, lifestyle changes prescriptions, and customized beauty health (cosmetics/diet) / Scalp care) The need for technology that provides product recommendation counseling and customized product regular delivery service is increasing day by day.

Korean Patent Laid-Open Publication No. 10-2018-0060962 (published on June 7, 2018) proposes a health data collection device, a health evaluation method using the same, and a health evaluation system including a health data collection device.

According to an embodiment, a storage unit for storing a 3D body image related to human body data; A processor that generates a 3D body type image corresponding to the user's human body data, and determines a type of body type based on the 3D body type image; And a display unit for displaying the 3D body image and the body shape.

According to another embodiment, the human body data includes a device for providing health information including at least one of height, weight, waist circumference, body mass index (BMI), body fat mass, muscle mass, and skeletal muscle mass.

According to another embodiment, the processor may generate a future 3D body image using the user's lifestyle information.

According to one side, a communication unit that receives at least one of human body data and life log using a wireless communication method; A deep learning unit that receives at least one of the received human body data and a life log and performs unsupervised learning to output health promotion information; And a processor that receives at least one of a user's human body data and a life log and provides health promotion information using the deep learning unit.

According to the other side, the human body data may include disease susceptibility information related to a result of a gene PCR (polymerase chain reaction) test, a family history of a disease, and a result of a health examination; And body measurement information related to height, weight, body mass index, body fat mass, muscle mass, and skeletal muscle mass.

In addition, the life log may include physical activity information related to sleep and exercise activity; Nutritional balance information related to meals and beverages; Stress information related to drinking and smoking; And air environment information related to indoor and outdoor air quality.

According to an embodiment, a communication unit for receiving at least one of human body data and life log using a wireless communication method; A storage unit for storing a 3D body image related to the human body data; A deep learning unit that receives at least one of the received human body data and a life log and performs unsupervised learning to output health promotion information; And receiving at least one of a user's human body data and a life log, providing health promotion information using the deep learning unit, generating a 3D body image corresponding to the human body data, and the type of body type based on the 3D body image A health information providing device including a processor that determines it is also presented.

According to another embodiment, the storage unit storing a 3D body image related to the human body data; Generating, by a processor, a 3D body type image corresponding to the user's human body data, and determining a type of body type based on the 3D body type image; And displaying the 3D body shape image and the body shape by a display unit.

According to another embodiment, the human body data may include at least one of height, weight, waist circumference, body mass index (BMI), body fat mass, muscle mass, and skeletal muscle mass.

According to one side, there is also disclosed a method of providing health information further comprising the step of generating, by the processor, a future 3D body image using the user's lifestyle information.

According to the other side, the communication unit receiving at least one of the human body data and the life log using a wireless communication method; Performing unsupervised learning by a deep learning unit to output health promotion information by receiving at least one of the received human body data and life log; A method of providing health information comprising the step of a processor receiving at least one of a user's human body data and a life log and providing health promotion information using the deep learning unit is provided.

According to another aspect, the human body data may include disease susceptibility information related to a genetic polymerase chain reaction (PCR) test result, a family history of a disease, and a medical examination result; And a method including body measurement information related to height, weight, body mass index, body fat mass, muscle mass, and skeletal muscle mass.

Alternatively, the life log may include physical activity information related to sleep and exercise activity; Nutritional balance information related to meals and beverages; Stress information related to drinking and smoking; And a method of providing health information including air environment information related to indoor and outdoor air quality.

According to an embodiment, the communication unit receiving at least one of the human body data and the life log using a wireless communication method; Storing a 3D body image related to the human body data by a storage unit; Performing unsupervised learning of receiving at least one of the human body data and the life log received by the deep learning unit and outputting health promotion information; Receiving, by a processor, at least one of a user's human body data and a life log and providing health promotion information using the deep learning unit; Generating a 3D body image corresponding to the human body data; And determining a type of a body shape based on the 3D body shape image.

In addition, a computer-readable recording medium having a program for performing the health information providing methods is also possible.

1 schematically shows an entire system of an apparatus for providing health information according to an embodiment.
2 is a block diagram showing a configuration of an apparatus for providing health information according to an embodiment.
3 shows another configuration of a health information providing apparatus according to an embodiment.
4 is a flowchart illustrating a method of providing health information according to an embodiment.
5 is a flowchart illustrating a method of providing health information outputting a 3D body image according to an exemplary embodiment.
6 is a diagram illustrating a method of providing health information for providing a future 3D body image according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the rights is not limited or limited by these embodiments. The same reference numerals in each drawing indicate the same members.

The terms used in the description below have been selected as general and universal in the related technology field, but there may be other terms depending on the development and/or change of technology, customs, preferences of technicians, and the like. Therefore, terms used in the following description should not be understood as limiting the technical idea, but should be understood as exemplary terms for describing embodiments.

In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, detailed meanings will be described in the corresponding description. Therefore, terms used in the following description should be understood based on the meaning of the term and the contents throughout the specification, not just the name of the term.

Explanation of terms

In the following description, machine learning based on deep neural network inference is the genetic information, medical history, and life log (sleep/eating/drinking/smoking/exercise) included in the result data of PCR (Polymerase Chain Reaction) test. /Activity) It refers to a technology that creates customized health promotion information tailored to the user by fusing various big data such as changes in the indoor and outdoor air environment (ultra-fine dust/chemicals/temperature/humidity/ultraviolet rays). In the process of implementing machine learning based on deep neural network inference, the k-medoids segmentation algorithm, Self Organizing Map (SOM), and Group Sparse are used as examples of unsupervised learning. ) Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long and Short Memory Network (Long), which are used as examples of deep learning such as Independent Components Analysis (ICA) algorithms, etc. Various types of algorithms such as Short Term Network, LSTM) can be fused and used as an ensemble model. Provides customized health promotion information using deep neural network inference machine learning, customized wellness (cosmetics/diet/scalp care) product recommendation counseling, and various descriptions of provisional devices and methods for customized product regular delivery services are described below.

Service subscription process

There is a user terminal associated with a specific user and a server that provides customized health information to the user. First, the user may transmit the result data of his/her PCR examination, medical history, personal lifestyle life log data, indoor air quality sensor data, etc. to the server through the user terminal. Between the user and the service company operating the server, the result data of PCR examination, medical history, life log (sleep/eating/drinking/smoking/exercise/activity) data and indoor air quality (ultrafine dust/chemicals/temperature/ Humidity/ultraviolet rays) A pre-contract may be signed for the provision of sensor data and health promotion management service. Specifically, the result data of the PCR examination may include information such as a gene number possessed by a specific user, a genetic trait represented by each gene number, and a disease that is likely to occur according to the corresponding genetic trait. The result data of PCR examination includes single nucleotide polymorphism (SNP) sequence, SNP comment number, allele, chromosome location, SNP location, related gene, related gene ID, etc.

The medical history data of a specific user is automatically converted to text using OCR (Optical Character Recognition) software and database (DB) when the user uploads a PDF file of the National Health Insurance Corporation webpage personal complaint medical examination result inquiry to the business server. Is stored in. A specific user's personal lifestyle life log data is automatically collected, processed, and stored through the sharing link of Google Photo Map, a storage box of meals/exercises/activity photos taken at the terminal used by a specific user, and capture images. , Can provide. Life log data produced by various digital sensors (walking/sleeping/activity mass meter, body composition meter, blood pressure meter, blood glucose meter, indoor air quality sensor) used by a specific user can be automatically collected, processed, stored, and provided through a device-linked open API. I can. In addition, it is possible to automatically collect, process, store, and provide data of ultrafine dust, yellow sand, ultraviolet rays, temperature, humidity, precipitation, cloud cover, and wind speed through an open API from the Meteorological Administration web server that provides an open portal for meteorological data. On the condition that the life log data and digital sensor data produced by the user are faithfully provided to the operator for a certain period of time, PCR test result data and customized health promotion information can be provided for free for a certain period of time. For the purpose of improving the quality of health promotion information provided by the operator's platform, the rate of accumulation of point tokens provided as motivation and rewards to users is differentially applied according to the period of data provision, the type of data provided, and the accuracy, consistency, and validity of the data. can do.

Classification

The server may deliver an encrypted data packet including the result data of the PCR examination to the user. Specifically, the encrypted data packet may be encrypted in a manner in which the user terminal and the server each share an asymmetric public key-secret key based on the blockchain technology. In the following description, a block chain may represent a ledger in which specific information is generated as one block and the generated blocks are sequentially connected. Typically, blockchain technology is used as a transaction ledger that sequentially includes transactions for the generated block by generating transaction information as a single block. However, in this embodiment, the blockchain technology can be applied to a process in which the result data of PCR examination and customized health information generated using the result data are updated in real time. User terminals and servers can encrypt and store individual health information by sharing a blockchain with each other.

Also, the server may provide a point token to the user terminal. The server may manage to increase point tokens when a user provides personal life log data and indoor air quality sensor data through a user terminal for a predetermined period of time. For example, the personal life log data includes sleep (entrance time, REM, Non-REM, Awakenings, sleep time, bedtime), exercise (type, time, frequency), meals (time, diet, calories), drink (time, time). Includes personal lifestyle data related to health such as type, volume), water (time, volume), drinking (time, type, amount of drinking), smoking (main smoking cigarette, amount of smoking, number of times, smoking start age) can do. In addition, indoor air quality sensor data may include ultrafine dust, chemicals, temperature, and humidity data. The user can receive personalized health promotion information in real time using the point token stored in the user terminal. The server may deduct points stored in the point token and provide customized health promotion information for a specific user to the user terminal. In addition, the user can receive a customized wellness (esthetic/diet/scalp care) product regular delivery service using the point token 130 stored in the user terminal. The server may deduct the points stored in the point token and provide a customized product regular delivery service for a specific user to the user terminal. In addition, PCR companies, cosmetics companies, and wellness care companies and door-to-door sales companies can provide product advertisements and discount coupons to users who have subscribed as individual members by signing up as a business member on the business's e-commerce platform.

Service delivery process

In the case of a conventional PCR examination, despite the high cost of about 500,000 won or more, there is a problem that it is not possible to provide a post management service using the result data. Wellness is a compound word of well-being and fitness, and means an ideal state in which physical, mental, and social health are in harmony. In today's society, interest in wellness is increasing day by day. The server according to the present embodiment may provide personalized health information to a user in real time by using data from PCR examination results, personal life habits, and environmental change data. Specifically, the customized health information may represent information for managing a customer's weight, eating habits, skin, hair, stress, etc. so that the user can maintain comprehensive health and achieving a health goal. For example, customized health information is a sleep enhancement program for users (sleep time push alarm, light level noise exposure intensity control alarm, night meal/coffee/TV/smartphone refrain from watching push alarm), nutrients and nutrients necessary for each individual lifestyle environment change. Prescribe a nutritional balance diet consisting of customized ingredients that maximize absorption, suggest appropriate basic metabolic/body fat/muscle control therapy, aerobic/strength/flexible exercise prescription tailored to individual exercise intensity, healthy hydration method (recommended time to drink, recommended drinking volume, carbonated drinks) /Caffeine drink refraining alarm) Health promotion information such as suggestions can be displayed.

An apparatus for providing health information implemented by a server according to an embodiment is shown. The health information providing device may be generated by fusion analysis of the result data of PCR examination and metadata of a plurality of users.

More specifically, the apparatus for providing health information may be generated in a manner that predictively analyzes a relationship between genetic information and genetic traits of a plurality of users, disease risk groups associated with the corresponding result data, lifestyle habits, and environmental changes. In addition, the health information providing device designates the result data of PCR examination on a plurality of users as an input value, and public databases that build ohmic data and clinical information such as genome/transcript/epigene/protein ( GTEx, NCI-60, ENCODE, ICGC, 1000 Genome, NIH Epigenomics Project, GIANT) is used to perform deep neural network inference-based machine learning, and is created by specifying the relationship (disease risk, etc.) to the result data as an output value. can do.

The health information providing apparatus may transmit the generated health information to a designated user terminal based on the predicted health information through machine learning based on deep neural network inference. For example, y1 represents the first health information determined by the first genetic information, and in this way yn may represent the nth health information determined by the nth genetic information. The health information providing device provides a personalized health promotion solution as health information using the result data and metadata of PCR examination. For example, the health information providing device includes 1) aesthetic health information that manages the user's skin care status, wrinkle elasticity, damage and irritation, and 2) diet health that manages the user's body fat management, overweight, and obesity management. Information and 3) hair loss prevention/treatment information can be provided to users.

Below, the entire system of the health information providing apparatus and the operation of each component will be described in detail.

1 schematically shows an entire system of an apparatus for providing health information according to an embodiment. The health information providing device 100 may collect various information of the user 110 and provide customized health information optimized for the user 110 by using the collected information.

According to an embodiment, various pieces of information include disease susceptibility information 121, body activity information 122, nutritional balance information 123, stress information 124, and air environment information 125 by way of example.

The disease susceptibility information 121 may include genetic information, family history information, health examination information, and the like, and the physical activity information 122 may include sleep-related information and exercise activity-related information. The nutritional balance information 123 may include food eaten, water and beverage information, and the stress information 124 may include information related to drinking and smoking. The air environment information 125 may include indoor and outdoor air quality information.

The health information providing apparatus 100 may perform unsupervised machine learning through machine learning using the information, and may output health promotion information corresponding to input user information.

A detailed configuration of the health information providing device will be described with reference to FIGS. 2 and 3.

2 is a block diagram showing a configuration of an apparatus for providing health information according to an embodiment. The health information providing apparatus 200 may include a processor 210, a communication unit 220, and a deep learning unit 230.

The communication unit 220 receives health information from an external user terminal or server. The received health information may be disease susceptibility information 121, body activity information 122, nutritional balance information 123, stress information 124, and air environment information 125 as exemplarily described in FIG. 1.

The deep learning unit 230 performs machine learning using the information received by the communication unit 220. These are information for receiving input data, and the output data may be health promotion information corresponding to the information.

Health promotion information is information that can improve the user's health, for example,'(Increase/decrease) (intensity/time/frequency of exercise)','(food/water/beverage) It may be information such as'Please reduce)','Go to bed at 11','Reduce the amount of smoking'. That is, information for enhancing a health condition may be extracted from the data collected by the communication unit 220 using a deep learning method.

The processor 210 may provide health promotion information corresponding to the user's health information by using the learned deep learning unit 230. Illustratively, but not limitedly, the deep learning unit 230 provides information on a user having an irregular sleep time, and the deep learning unit provides information on'Go to bed at a regular time' based on the machine-learned content. It can be provided to the user's terminal.

Alternatively, according to another embodiment, when the intake of food is large and the intake of calories is large, but specific nutrients of the corresponding diet are insufficient,'reduce carbohydrate intake and increase protein intake' based on machine learning of the deep learning unit. Health promotion information can be provided.

3 shows another configuration of a health information providing apparatus according to an embodiment. The health information providing apparatus 300 according to another embodiment may include a processor 310, a storage unit 320, and a display unit 330.

The storage unit 320 may store 3D body shape information related to various user's body dimensions. Specifically, it is possible to store body dimensions of multiple users and 3D body image corresponding thereto.

The body dimensional data stored by the storage unit 320 may be height, weight, body fat percentage, skeletal muscle mass, muscle mass, body mass index, arm/leg muscle mass, arm/leg body fat mass, and the like. Also, based on the data, a 3D body image related thereto may be stored. That is, a predicted 3D body image corresponding to the data may be stored.

For example, but not limited to, the data format associated with the 3D body shape information and the body dimensions stored by the storage unit 320 may be as follows. Height: 170cm, weight: 70kg, body fat percentage: 15%, skeletal muscle mass: 40kg, and 3D body image related to each of the above data can be stored.

The processor 310 searches for the most similar data stored in the storage unit 320 using the input user's body size data. As a result of the search, the 3D body image of the most similar data can be determined as the current body image of the user.

In addition to the input user's body dimension data, the processor 310 may provide a future 3D body image of the user when changing, such as a method of presenting a life log.

Specifically, when life log data is input in addition to the user's current body dimensional data, and the current 3D body image is a body shape based on the current life log data, a future 3D body shape that can be transformed by changing the life log. Images can be provided.

For example, if you drink 3 times a week in the life log input from the user and the amount of meal is 3000 kcal, if you reduce drinking to once a week and reduce the amount of food to 2000 kcal, the future 3D body shape will change (reduce body fat). Images can be provided. This is only an example, and it is also possible to provide a future 3D body image with increased muscle when presenting to increase the protein intake than the input life log.

In addition, a type of a corresponding germ-related body shape may be determined according to the body size data.

The germinal layer is a generic term for the three cell layers, endoderm, mesoderm, and ectoderm, which appear when cells divide in the process of developing and growing fertilized eggs in animals.

The endoderm is located at the innermost of the three germs, and is formed later by differentiating the digestive and excretory organs. On the other hand, the ectoderm is the outermost layer among the three germs and develops into the nervous and skin tissues of our body. And in the mesoderm located between the endoderm and ectoderm, connective tissue components such as bones, cartilage, and muscles are formed. The body type can be classified on the basis of the germ and can be classified into three types: endoderm type, ectoderm type, and mesoderm type.

Endomorphs are cases in which the internal organs, especially the digestive organs, are well developed, originating from the embryonic endoderm in the body. An endoderm type person has a large body with a round face, and is a typical obese constitution, especially with a lot of fat accumulation in the center of the body.

Ectomorphs are cases in which neural tissues and skin tissues, which are tissues arising from the ectoderm in the embryonic period, develop. Ectodermal type people maintain a slim and slim body, but lack muscle mass.

Mesoderm type (mesomosphs) is a body type with thick skeleton and well developed muscle and skeleton, and is born with wide shoulders and constricted waist, and has active metabolism. The mesoderm type, which is a typical sportsman, can control weight well with a little exercise and has a high metabolic rate. There is not much change in weight, the percentage of body fat is low, and the abdominal muscles are clear.

Accordingly, the processor 310 may determine a corresponding user's germline body based on the input body size.

The display unit 330 may display a 3D body image and/or germline body type information generated by the processor 310. As an example, it is possible to display the current 3D body type image of the user and display the germline body type information together. In addition, according to another embodiment, a future 3D body image of the user generated by the processor 310 may be displayed.

4 is a flowchart illustrating a step of performing machine learning by a health information providing apparatus according to an exemplary embodiment. A method of providing health information according to an embodiment may include a body data acquisition step 411, a life log acquisition step 412, a data classification step 420, a machine learning step 430, and a health information output step 440. I can.

The human body data acquisition step 411 and the life log acquisition step 412 may acquire body data and life logs of various users. The human body data may be information related to the user's body such as height, weight, body mass index, body fat mass, muscle mass, skeletal muscle mass, chest circumference, waist circumference, hip circumference, and the like.

Meanwhile, the life log may include physical activity information related to a user's sleep and exercise activity, nutritional balance information related to food and beverage, stress information related to drinking/smoking, air environment information related to indoor/outdoor air quality, and the like. In addition, it may include the user's genetic information, genetic disease information, family history information, etc. based on the user's PCR (Polymerase Chain Reaction) test result.

That is, in the body data acquisition step 411 and the life log acquisition step 412, various pieces of information necessary for machine learning are acquired.

The data classification step 420 is a step of classifying the acquired data into a predefined category. The obtained human body data or life log can be classified by each category and input as machine learning data.

In particular, the life log may include disease susceptibility information, physical activity information, nutritional balance information, stress information, and air environment information, and thus each information can be classified into categories. Specific category classification will be described later.

The machine learning step 430 is a step of performing machine learning using the data for each category classified in the data classification step 420. Machine learning can be performed to output health information based on various human body data and life log data. As described above, the machine learning method may use a CNN, an RNN method, etc., which are exemplary and not limited thereto. Various unsupervised machine learning methods can be used.

Various classified data may be used as input data, and the output data may be health information corresponding to the input data, and more specifically, health promotion information.

The health promotion information is information that can improve the health of a user corresponding to the input data, and may be information optimized for each corresponding user.

The health information output step 440 is a step in which health information is output as an output of the machine learning step 430. The health information providing apparatus repeats the entire process of the machine learning, thereby increasing the quality of the output health information.

When the human body data and the life log are described in detail, the human body data may be data related to the user's body size. For example, height and weight can be exemplified.

The life log refers to a data log including disease susceptibility information, physical activity information, nutritional balance information, stress information, and air environment information. Each information is described in detail one by one.

The disease susceptibility information may include genetic information, family history information, and health examination information that can be known through PCR test. Genetic information refers to genetic information on obesity, blood pressure, blood sugar, lipids, skin, and hair loss. The family history information refers to family history information on osteoporosis, high blood pressure, diabetes, heart disease, and female diseases. In addition, the health checkup information refers to information about obesity, blood pressure, blood sugar, lipids, skin, hair loss, etc. that can be known during a health checkup.

The difference between the genetic information and the health checkup information is that the gene information refers to information about the contents that can be expressed by the gene through the PCR test, and the health checkup information is information about the current partner according to the actual health checkup result. There is a difference in that.

That is, in the case of obesity information, if the genetic information means whether or not a gene for obesity is present, the obesity information of the health examination information may be information on whether or not the current obesity is based on the waist circumference, body fat percentage, and body mass index.

In the health checkup information, blood pressure is information on systolic and diastolic blood pressure, and pulse pressure, and blood sugar is information on fasting blood sugar, lipids is LDL, triglyceride level, skin is skin type according to Bowman's skin classification method, and hair loss is related to hair loss type according to BASP classification method. It could be information.

Physical activity information is information about sleep and exercise. Sleep information may include information on sleep efficiency, elevation delay time, sleep satisfaction, bedtime, and number of awakenings. In addition, sleep satisfaction may be satisfaction information expressed in five levels: very satisfied, satisfied, moderate, dissatisfied, and very dissatisfied.

The exercise information may be information about an exercise type, exercise time, and exercise intensity. The type of exercise may be an aerobic or anaerobic exercise, the exercise time may be the number of exercise per week and the exercise time per session, and the exercise intensity may be information expressed as strong, medium, or weak.

The nutritional balance information may be information about a meal or water/beverage. Meals are information about the time, place, menu, and quantity of meals, and so are water/drinks.

Stress information refers to information about stress caused by drinking and smoking. Drinking and smoking can also be subdivided into time, place, type, and amount of drinking/smoking.

Finally, the air environment information is information on indoor and outdoor air quality. Outdoor air quality may be information on fine dust (PM10), ultrafine dust (PM2.5), ozone, carbon monoxide, sulfur dioxide and carbon dioxide, and indoor air quality is fine dust, ultrafine dust, ozone, carbon monoxide, radon, volatile It may be information about organic compounds (VOC) and formaldehyde.

5 is a flowchart illustrating a method of providing health information outputting a 3D body image according to an exemplary embodiment. The method of providing health information according to an embodiment may include a body data acquisition step 510, a 3D body image generation step 520, a body type determination step 530, and a 3D body image and body shape display step 540. .

The human body data acquisition step 510 is a step of acquiring human body data of a user who wants to display a 3D body image. Specifically, it is a step of receiving various data for generating a 3D body image based on the user's height, weight, body fat mass, waist circumference, and chest circumference.

The human body data can be inputted through a user terminal, and in some cases, a method of receiving it from an external server is also possible. In some cases, a method of simultaneously receiving data from a user terminal and an external server is possible by mixing the two cases. For example, through the user terminal, the user may receive input of height and weight, and obtain information such as body fat percentage, skeletal muscle mass, and/leg muscle mass from an external server.

The 3D body image generation step 520 is a step of generating a 3D body image from the obtained information. As an example, a method of generating a 3D body image may use Google's 3D image API, and a method of generating the most similar 3D body image as an input 3D body image of the user based on the stored data is also possible.

The body type determination step 530 is a step of determining the type of germline body type. As described above, it is a step of determining one of ectoderm, endoderm, and mesoderm based on the user's human body data.

Finally, the 3D body image and body type display step 540 is a step of displaying the 3D body image and the determined body type generated in the 3D body image generation step 520 and the body type determination step 530 using the display unit.

The 3D body image that is basically output may be a current 3D body image of the user, or, in some cases, a future 3D body image. That is, a current 3D body image may be output based on the user's current human body data, and a future 3D body image may be output in consideration of a separate life log improvement. A method of outputting a future 3D body image will be described in detail in FIG. 6.

6 is a diagram illustrating a method of providing health information for providing a future 3D body image according to an embodiment. The health information providing method for providing a future 3D body image according to an embodiment includes a human body data acquisition step 610, a 3D body image generation step 620, a life log acquisition step 630, and a life log improvement method determination step ( 640), a future 3D body image generation step 650 and an improvement method, and a future 3D body image output step 660 may be included.

The human body data acquisition step 610 is a step of acquiring human body data of a user who wants to output a 3D body image, as in FIG. 5.

Next, the 3D body image generation step 620 is also a step of generating a 3D body image using the obtained human body data.

Life log acquisition step 630 is a step of acquiring a life log to generate a future 3D body image. This is because a future 3D body image can be created by acquiring and analyzing the user's life log.

In the life log acquisition step 630, physical activity information, nutritional balance information, stress information, air environment information, and the like related to the user may be acquired.

The life log improvement method determination step 640 is a step in which the processor determines a life log improvement method based on the acquired life log. The processor may determine a portion to be improved and determine an improvement method based on various information acquired from the life log.

Specifically, it can be determined by synthesizing the life log and human body data. When the user's body type according to the human body data is obese, it may be determined to increase the exercise intensity and time in order to lose weight in the life log.

According to another embodiment, when the user has a family history of lung disease, it may be determined to improve indoor air quality. Alternatively, an improvement method may be suggested to refrain from going out on days when the outdoor fine dust level is high.

The future 3D body image generation step 650 may generate a future 3D body image when performing as an improved life log in the current 3D body image. In the case of obese people, increasing the intensity and time of exercise can create an image of a standard body shape in the future 3D body image. Alternatively, if there is a family history of lung disease, improvement of indoor air quality can be generated by different colors, etc., that the health of the lung area in the future 3D body image will be improved. For example, if the current 3D body image was created to indicate that the health of the lung area is dangerous in red, the future 3D body image may be generated to indicate that the health of the lung area is improved in green.

The improvement method and future 3D body image output step 660 is a step of displaying the information generated in steps 640 and 650. In more detail, the life log improvement method generated in step 640 may be output using the display unit. In addition, the future 3D body image generated in step 650 may be output.

According to another embodiment, a method of outputting a 3D body image together with a present and a future 3D body image is also possible. By way of example, but not limitation, a current 3D body image is displayed on the left side of the screen displayed by the display unit, and a future 3D body image image is displayed on the right side, so that the user can grasp the shape of the body shape to be changed at a glance. .

By intuitively showing the body image and health information (life log improvement method) to the user, the user's health can be improved. Since users can easily check the contents of future body shape and health improvement, their willingness to improve the life log can be increased.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It can be implemented using one or more general purpose computers or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

Although the embodiments have been described by the limited drawings, various modifications and variations are possible from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

Claims (15)

A storage unit for storing a 3D body image related to the body dimensions;
As body dimensional data is input from the user, a 3D body image corresponding to the body dimensional data is searched for from the storage unit, determined as the current body dimensional image of the user, and life log data is received from the user A processor for transforming the current body image into a future 3D body image by changing the life log data using gene information, family history information, and health examination information included in disease susceptibility information; And
A display unit for displaying the future 3D body image according to the life log data changed in association with the disease susceptibility data together with the current body image
Including,
The processor,
Ⅰ) For the life log data changed in connection with the disease susceptibility data, by reducing the amount of drinking and eating, increasing exercise intensity and exercise time, the current body image is converted into a future 3D body image image Transform,
Ii) For the life log data changed in connection with the disease susceptibility data, the current body image is transformed into a future 3D body image with increased muscle by increasing protein intake and increasing exercise intensity and exercise time. doing
Health information provision device. delete delete The method of claim 1,
The processor,
Determine the germ layer corresponding to the disease risk according to the body size data,
Based on the determined germline, the user's body type is classified into any one of an endomorphs, an ectomorphs, and a mesoderm type,
The display unit,
Displaying the divided user's body shape together with the current body type image
Health information providing device comprising a. The method of claim 1,
The health information providing device,
A communication unit for receiving at least one of human body data and life log using a wireless communication method; And
Deep learning unit that receives at least one of the received human body data and life log, performs unsupervised learning, and outputs health promotion information
Including more,
The processor,
Receiving at least one of the human body data and the life log and providing health promotion information using the deep learning unit
Health information provision device. The method of claim 5,
The human body data,
Disease susceptibility information related to genetic PCR (Polymerase Chain Reaction) test results, disease family history, and health examination results; And
Body measurement information related to at least one of height, weight, chest circumference, waist circumference, hip circumference, body mass index (BMI), body fat mass, muscle mass, skeletal muscle mass, limb muscle mass, and limb fat mass
Health information providing device comprising a. The method of claim 5,
The life log,
Physical activity information related to sleep and exercise activities;
Nutritional balance information related to meals and beverages;
Stress information related to drinking and smoking; And
Air environment information related to indoor and outdoor air quality
Health information providing device comprising a. Storing, in the storage unit, a 3D body image related to body dimensions;
In the processor, as body dimensional data is input from the user, searching for a 3D body shape image corresponding to the body dimensional data from the storage unit, and determining a current body shape image of the user;
Displaying the current body image on the display unit;
Receiving, at the processor, life log data from the user;
Transforming the current body image into a future 3D body image by changing the life log data using gene information, family history information, and health examination information included in the disease susceptibility information, in the processor; And
In the display unit, displaying the future 3D body image according to the change in the life log data changed in connection with the disease susceptibility data together with the current body image
Including,
The step of transforming the future 3D body image,
For the life log data changed in connection with the disease susceptibility data, the current body image is transformed into a future 3D body image with reduced body fat by reducing the amount of drinking and eating, increasing exercise intensity and exercise time. step; And
Transforming the current body image into a future 3D body image with increased muscles by making changes to increase protein intake, exercise intensity and exercise time for the life log data changed in connection with the disease susceptibility data
Health information providing method comprising a. delete delete The method of claim 8,
Determining, in the processor, a germline corresponding to a disease risk according to the body size data;
Dividing the user's body type into one of an endoderm type, an ectoderm type, and a mesoderm type, based on the determined germ layer, in the processor; And
In the display unit, displaying the divided user's body shape together with the current body shape image
Health information providing method further comprising. The method of claim 8,
Receiving at least one of a human body data and a life log using a wireless communication method, at the communication unit;
Receiving at least one of the received human body data and a life log, by a deep learning unit, performing unsupervised learning and outputting health promotion information; And
In the processor, receiving at least one of the human body data and a life log and providing health promotion information using the deep learning unit
Health information providing method further comprising. The method of claim 12,
The human body data,
Disease susceptibility information related to genetic PCR (Polymerase Chain Reaction) test results, disease family history, and health examination results; And
Body measurement information related to at least one of height, weight, chest circumference, waist circumference, hip circumference, body mass index (BMI), body fat mass, muscle mass, skeletal muscle mass, limb muscle mass, and limb fat mass
Health information providing method comprising a. The method of claim 12,
The life log,
Physical activity information related to sleep and exercise activities;
Nutritional balance information related to meals and beverages;
Stress information related to drinking and smoking; And
Air environment information related to indoor and outdoor air quality
Health information providing method comprising a. A computer-readable recording medium containing a program for performing the method of providing health information according to any one of claims 8 and 11 to 14.

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