JP2014174954A - Action support system, terminal device of action support system, and server - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an action support system which generates and provides effective advice for supporting a subject in health guidance and exercise guidance on the basis of biological data.SOLUTION: An action support system comprises: a terminal device 20 which transmits personal data that includes biological data including a pulse rate, exercise data as an index of an amount of exercise, and personal attribute data; a server 50 which receives and stores the personal data and population data composed of pieces of prestored data; and a PC 70 in which a support program is installed for generating personal advice on the basis of the personal data and the population data. The PC 70 generates the personal advice by changing a reference ratio of the personal data to the population data according to an amount of storage of the personal data. The system can support a subject to take action for attaining his/her goal by providing the subject with advice corresponding to a period from when the subject starts to take the action until the subject attains the goal.

Description

  The present invention relates to an action support system that generates and provides advice for supporting a subject in health guidance and exercise guidance based on biological data.

In health guidance and exercise guidance, a system is known that provides advice to a subject who is the subject of guidance in order to achieve a set goal.
For example, in a prior art document, a health management system using a data mining technique is proposed. Specifically, time series data on lifestyle and health conditions was accumulated for each subject for a certain period of time, and when a correlation was found between the two, advice that considered the correlation was provided to the subject. As an example, in the process of accumulating time series data of calorie intake and body weight, if the total daily calorie intake exceeds about 1600 kcal, a correlation is found that the body weight increases. The subject was provided with advice to reduce calorie intake.

Yuuki Tsuji (Graduate School, Takasaki University of Health and Welfare) “Individual health management by cloud computing” DEIM Forum 2012 D11-2

  However, in the above prior art documents, there is a problem that advice is not generated until a correlation tendency appears between the lifestyle habit behavior and the health state of the subject. Specifically, it is necessary to extract the lifestyle behavior and health status of the subject as data, and if a correlation tendency appears between them, it is necessary to extract it as a correlation rule, so generally a period of about 3 months is required, During that time, it was difficult to give advice to the subjects. That is, there is a problem that it is difficult to provide effective advice to the subject during the period until the association rule is derived.

  Further, in the above prior art documents, there is a fear that even if the action for achieving the target causes a risk to the subject's body, it cannot be transmitted to the subject. Furthermore, there is a risk of missing an opportunity to convey a dangerous state because no advice is generated while the subject's personal data is accumulated. As described above, there is a problem that it is difficult to advise the subject even if the action for achieving the goal causes a risk to the subject's body.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following forms or application examples.

  [Application Example 1] An action support system according to this application example includes a terminal device that transmits personal data including a plurality of data items including biological data and exercise data of a subject, and is stored in advance corresponding to the data item. A server for storing population data consisting of data groups, receiving and storing the personal data, and a support program for generating personal advice based on the population data and / or the personal data are installed. A computer, wherein the computer generates the personal advice by changing a reference ratio between the population data and the personal data according to a storage amount of the personal data.

According to this application example, population data including a data group stored in advance is stored in a server included in the action support system. Further, the computer generates personal advice by changing the reference ratio between the population data and the personal data in accordance with the accumulated amount of the personal data.
With this configuration, unlike the conventional system in which it is difficult to provide effective advice during the period until the association rule is derived, according to the action support system of this application example, Even in this case, personal advice can be provided using the data group. For example, by referring to data corresponding to the sex, age, and occupation of the subject from the attribute data included in the data group, it is possible to provide personal advice that is inferred.
Therefore, effective advice can be provided even during the period until the association rule is derived.

Also, unlike the conventional system where it was difficult to convey behavioral risks to the subject, the behavior support system of this application example provides personal advice to call attention using biometric data Can do. For example, when the pulse rate of the biological data is used and the pulse rate is abnormally high, advice for calling attention can be provided.
By comprising in this way, the advice for calling attention to the risk in action can be provided.
Therefore, it is possible to provide an action support system that can provide useful advice from the start of use.

  Application Example 2 In the action support system according to the application example described above, when the computer accumulates the personal data for less than a predetermined period, the computer derives a criterion derived using at least attribute data in the data group. The personal advice is generated with reference to the advice table, and the personal advice is generated using the population data and the personal data when the personal data is accumulated for the predetermined period or longer. Is preferred.

  According to this application example, the personal data storage period is used as an index of the personal data storage amount. For example, even if the predetermined period is 3 months and personal data is stored for less than 3 months, the personal data is provided by referring to the data corresponding to the sex, age, and occupation of the subject from the attribute data. be able to.

  Application Example 3 In the action support system according to the application example described above, the computer is derived using at least the attribute data in the data group when the accumulated amount of the personal data is less than a predetermined number of data. The personal advice is generated with reference to the reference advice table, and when the personal data is accumulated more than the predetermined number of data, the personal advice is obtained using the population data and the personal data. It is preferable to produce.

According to this application example, the number of personal data is used as an index of the amount of personal data stored.
With this configuration, even when the accumulated personal data is less than the predetermined number of data, it is possible to provide personal advice that is analogized by referring to the attribute data.

  Application Example 4 In the action support system according to the application example described above, the computer is derived using at least the attribute data in the data group until a predetermined rule is extracted by storing the personal data. When the personal advice referring to a standard advice table is generated and the predetermined rule is extracted, the personal advice is preferably generated based on the predetermined rule.

According to this application example, personal advice is generated by referring to the standard advice table until a predetermined rule is extracted by storing personal data.
With this configuration, it is possible to provide effective advice even in an initial stage until a predetermined rule is extracted.

  Application Example 5 In the action support system according to the application example described above, the attribute data includes at least gender and date of birth, and the personal advice is generated with reference to the reference advice table. It is preferable to refer to the attribute data.

  Application Example 6 In the action support system according to the application example described above, the biological data includes, in addition to the pulse rate, body weight, body fat percentage, abdominal circumference, BMI, total cholesterol value, cholesterol value, and HbA1c value data. It is preferable that at least one of the group is included.

  Application Example 7 The terminal device of the action support system according to this application example is a terminal device that displays personal advice directed to a subject, and is an individual composed of a plurality of data items including the subject's biological data and exercise data. Collecting the data, sending it to a database storing the personal data, analyzing the database and / or the personal data, receiving the personal advice from a data processing unit that generates the personal advice, The data processing unit is generated by changing a reference ratio between the database and the personal data according to the amount of the personal data stored, and displays the personal advice received from the data processing unit. It is characterized by.

  Application Example 8 The server of the behavior support system according to this application example is a server that generates personal advice for a subject, and stores personal data including a plurality of data items including biological data and exercise data of the subject. A storage unit, a database including data corresponding to the plurality of data items of the personal data, a data processing unit that analyzes the database and / or the personal data and generates the personal advice for the subject, The personal advice is generated by changing a reference ratio between the database and the personal data in accordance with a storage amount of the personal data.

Explanatory drawing showing the outline | summary of the lifestyle improvement system which concerns on Embodiment 1. FIG. The block diagram which shows schematic structure of a lifestyle improvement system. (A) The figure which shows the input screen of attribute data, (b) The figure which shows the screen during exercise, (c) The figure which shows the advice screen. The figure which shows the structure of personal data. The figure showing the contents of the database in a server. The figure which shows a part of reference | standard advice table. (A) The figure which shows a biometric data table, (b) The figure which shows a biometric data table, (c) The figure which shows a biometric data table. (A) The figure which shows an exercise | movement data table, (b) The figure which shows an exercise | movement data table, (c) The figure which shows an exercise | movement data table. The figure which shows an attribute data table. The flowchart figure showing the flow of personal advice generation. The figure which shows an example of personal advice. The image figure which shows the ratio of the reference data until it produces | generates a personal advice. The flowchart figure showing the flow of the personal advice production | generation in Embodiment 2. FIG. The flowchart figure showing the flow of the personal advice production | generation in Embodiment 3. FIG. The figure showing the outline | summary of the action assistance system in the modification 4.

  Hereinafter, embodiments of the present invention will be described. The embodiments described below do not unduly limit the contents of the present invention described in the claims. In addition, all the configurations described in the present embodiment are not necessarily essential configuration requirements of the present invention.

(Embodiment 1)
FIG. 1 is an explanatory diagram illustrating an outline of a lifestyle habit improvement system as an action support system according to the first embodiment. FIG. 11 is a diagram illustrating an example of personal advice. First, an outline of the lifestyle improvement system according to the present embodiment will be described.

(Outline of lifestyle improvement system)
The lifestyle improvement system 100 shown in FIG. 1 is a system that supports actions for improving the health of a subject by providing appropriate advice to the subject. In particular, this system is suitable for subjects suffering from lifestyle-related diseases, subjects who want to prevent lifestyle-related diseases, and subjects who want to acquire healthy lifestyle habits.
The terminal device 20 is attached to the wrist 5 or the like of the human body and contacts the wrist 5 with a pulse wave sensor 23 that senses the pulse, so that the pulse rate can be accurately determined even during various operations such as exercise and sleep. It is a wristwatch type biological data measuring device capable of measuring In addition to the pulse rate, the terminal device 20 can collect exercise data such as calorie consumption calculated from the pulse rate, and attribute data such as a date of birth, sex, and occupation input by the subject. You may comprise as follows. As a preferred example, the terminal device 20 is a portable device that can be used while a subject is wearing the wrist 5 and exercising outdoors or in a fitness club. The terminal device 20 includes a display panel 21 that displays measured biometric data and the like. The display panel 21 has a substantially rectangular shape, and when it is attached to the wrist 5, the arm length direction is the long side direction of the display panel 21.

Biometric data, personal data including exercise data, and attribute data stored in the terminal device 20 are stored in the server 50 via the network 4. A PC (Personal Computer) 70 as a computer analyzes the accumulated data group and generates an effective message for the subject.
An example of the generated message is shown in FIG. The advice 90 is an advice generated at the start of the action, and the advice 91 is an example of an advice generated three months after the start of the action. For example, in the advice 90, a goal is given as an advice on how to eat, "if you make an effort to keep the calorie intake within 1980-2100 kcal". The advice 91 provides the advice “There may be stricter dietary restrictions. Be careful not to limit your diet too much.” In this way, it is possible to collect subject information and switch to a subject-specific message according to the amount of accumulated data.
The configuration of the lifestyle improvement system 100 for realizing such effects will be described in detail below.

(Configuration of lifestyle improvement system)
FIG. 2 is a block diagram showing a schematic configuration of the lifestyle improvement system.
The lifestyle improvement system 100 includes a terminal device 20, a server 50, a PC 70, and the like.
The terminal device 20, the server 50, and the PC 70 are connected via the network 4. In a preferred example, the network 4 is connected to the Internet using a public wireless LAN to which the terminal device 20 is connected. The server 50 and the PC 70 are connected via an intranet. A firewall 3 is interposed between the Internet 1 and the intranet 2 to prevent unauthorized access such as theft of personal information.
The server 50 and the PC 70 are not limited to the intranet connection, and may be configured to play both roles of the server 50 and the PC 70 in one PC.

(Configuration of terminal device)
The terminal device 20 includes a sensor unit 24, an operation unit 30, a time measuring unit 31, a first storage unit 32, a second storage unit 36, a communication unit 46, a control unit 47, a display drive unit 48, and the like. These components are connected via a bus B1.
The sensor unit 24 has a function of detecting biological data, exercise data, external environment data, and the like of the subject and outputting the information as personal data. The biosensor unit 25, the motion sensor unit 26, and the environmental sensor. The unit 27 and the like.

  The biological sensor unit 25 is a pulse wave sensor 23 as a preferred example. The pulse wave sensor 23 collects light irradiated from a light emitting element such as an LED (Light Emitting Diode) toward the wrist 5 of the human body and reflected by the blood vessel of the wrist 5 and receives it by a light receiving element such as a photodiode. At this time, the pulse wave sensor 23 detects the pulse wave of the subject using a phenomenon in which the reflectance of light is different between when the blood vessel is dilated and when it is contracted. The detected pulse wave data is sent to a data generation program 34 (described later) executed by a control unit 47 (described later). In addition, it is not limited to incorporating the sensor which detects biometric data in the terminal device 20, and other measuring devices can communicate with other measuring devices such as a weight measuring device, a height measuring device, and a blood pressure measuring device. The structure which acquires the collected biometric data may be sufficient. As a function in this case, a common communication protocol for communicating with other biological devices and a short-range wireless communication interface provided in the communication unit 46 (described later) are further required. The biometric data acquired by the biometric sensor unit 25 is sent to the data generation program 34 executed by the control unit 47.

The motion sensor unit 26 is a sensor unit including a three-axis acceleration sensor, a three-axis gyro sensor, and a GPS (Global Positioning System) sensor (all not shown) as a preferred example. The sensor is not limited, and any sensor that can collect position information of the terminal device 20 and exercise state information may be used.
The triaxial acceleration sensor is a three-axis direction of X, Y, and Z axes in which the long side direction of the display panel 21 of the terminal device 20 is the X axis, the short side direction is the Y axis, and the thickness direction of the terminal device is the Z axis. It is an acceleration sensor attached to and measures the acceleration change of each axis per unit time.
The triaxial gyro sensor measures the angular velocity for each X, Y, and Z axis in the acceleration sensor per unit time.
The GPS sensor includes a GPS reception function and a position information calculation circuit, and measures position information (latitude, longitude, altitude) data for each unit time.

As a preferred example, the environment sensor unit 27 is a sensor unit including a temperature sensor and a pressure sensor (both not shown). However, the environment sensor unit 27 is not limited to these configurations and collects external environment data. Any sensor is possible.
The temperature sensor measures the air temperature every unit time. The pressure sensor measures the atmospheric pressure every unit time.

  The operation unit 30 includes a plurality of operation buttons including an operation button 22 (see FIG. 1) that is a push button type switch. As an operation example, the cursor displayed on the display panel 21 is moved by pressing the operation button 22 to change the selection state. When the operation button 22 is pressed again, the selected function is determined. These are stored in the first storage unit 32 as the button event processing program 35 and executed by the control unit 47. The configuration of the operation unit 30 is not limited to this, and any configuration that allows a plurality of operation inputs may be used, and the display panel 21 may have a touch panel function.

  The time measuring unit 31 is a real time clock, and has time measuring functions such as a calendar function, a clock function, and a stop watch function, for example. Time data such as the date and time that the time measuring unit 31 is measuring is read by the control unit 47.

The first storage unit 32 is a non-volatile memory, and stores (stores) programs such as an attribute input program 33, a data generation program 34, and a button event processing program 35 of the operation unit 30.
The attribute input program 33 is a program for inputting personal attribute data such as a subject's date of birth, sex, and occupation, and is started when the subject uses the terminal device 20 for the first time, for example. Prompt for input. In addition, since it can be started after the start of use, the attribute data can be changed as appropriate. When the attribute data is input in advance by the subject using an electronic device other than the terminal device 20 such as a general PC, the subject's attribute data is stored in the server 50. In such a case, the attribute input program 33 can also acquire the subject's attribute data from the server 50 via the network 4 and the communication unit 46.
The data generation program 34 is a program for sorting personal data including biometric data, exercise data, attribute data, and the like. The personal data is stored in the second storage unit 36 in a text data format divided into data types. Details of the personal data will be described later. The data generation program 34 is updated and added as needed from the server 50 or the PC 70 via the network 4. That is, it is possible to switch and add in the course of the action for improving the health of the subject, such as a program that applies a new calculation method specific to the subject, a program that collects new data items, and the like.

  The second storage unit 36 is a non-volatile memory that stores (stores) data such as personal data 43, personal advice 38, and common variables 37 that are used in programs stored in the first storage unit 32. Yes. Details of the personal data 43 and the personal advice 38 will be described later.

The communication unit 46 is a wireless LAN adapter as a preferred example, and has an IP (Internet Protocol) and a communication protocol common to external biological devices. Note that the present invention is not limited to this configuration, and any communication adapter capable of wireless communication may be used. For example, a Bluetooth (Bluetooth (registered trademark)) adapter may be used. The communication unit 46 transfers data and commands to and from the server 50 and the PC 70 using IP, and acquires biological data from other biological devices using a common communication protocol.
The communication unit 46 includes a physical communication terminal, is connected to another device (such as a general PC) via a cable, and communicates data and commands with the server 50, the PC 70, and other biological devices via another device. It is also possible to adopt a configuration for transferring

The control unit 47 is a CPU (Central Processing Unit), and includes a sensor unit 24, an operation unit 30, a time measuring unit 31, a first storage unit 32, a second storage unit 36, a communication unit 46, and a display drive that constitute the terminal device 20. Each unit such as the unit 48 is controlled.
The display drive unit 48 is a display panel drive circuit (driver), and drives the display panel 21 according to the set screen transition.

(Display screen of terminal device)
FIGS. 3A to 3C are diagrams showing an aspect of the display screen of the terminal device, where FIG. 3A is a diagram showing an attribute data input screen, and FIG. 3B is a diagram showing a screen during exercise; (C) is a figure which shows an advice screen.
Then, the display screen of the terminal device 20 is demonstrated using FIG. 3 (a)-(c). FIG. 3A is an example of a screen for inputting personal attribute data. In this example, the subject is born on July 4, 1960 (19 is omitted) as the date of birth through the operation unit 30 and is displayed as “'60: 07: 04”.

  FIG. 3B is a screen displaying a pulse during exercise. The upper center of the screen shows “120” as the current pulse rate. A heart-shaped icon displayed on the left side of the pulse rate indicates that the terminal device 20 can normally detect the pulse. The smile icon displayed on the right side of the pulse rate indicates that the current pulse rate is an appropriate exercise load (aerobic exercise and low risk). The number “0: 35′24” ”in the middle of the screen is the elapsed time from the start of measurement. This example shows that it is 35 minutes and 24 seconds. “0385 kcal” at the bottom indicates that the cumulative value of calories consumed (not including basal metabolism) in the exercise from the start of measurement is 385 kcal.

  FIG. 3C is a screen displaying personal advice received from the PC 70. The upper part of the screen shows the date “October 5, 2012 (Friday)” (20 is omitted) when the advice is generated. The personal advice 38 is character string data of advice directed to the subject, and is stored in the second storage unit 36 (FIG. 2). The advice display window 39 is an area for displaying advice on the display panel 21, and the subject can visually recognize the full text of the personal advice 38 on the advice display window 39. The personal advice 38 for the subject is not only displayed on the terminal device 20 but also viewed on the general PC, mobile phone, smartphone, etc. owned by the subject via a browser, e-mail, etc. It is also possible to receive and confirm with.

(Server configuration)
Returning to FIG.
The server 50 is a data server and includes a control unit 51, a communication unit 52, a storage unit 53, a database unit 60, and the like. These components are connected via a bus B2.
The control unit 51 is a CPU and controls each unit such as the communication unit 52, the storage unit 53, and the database unit 60 that constitute the server 50.
The communication unit 52 is a network adapter, and receives personal data 43 from the Internet 1 via the firewall 3 between the server 50 and the terminal device 20. Further, a dedicated program command and various data including personal data 43 are transmitted and received between the server 50 and the PC 70 via the intranet 2.

The storage unit 53 is a hard disk drive (HDD), and stores a program for managing the database unit 60 executed by the control unit 51, a program for creating a server setting environment (none of which is shown), and the like. Note that the configuration of the storage unit 53 is not limited to this, and any storage device having a storage capacity for storing the above-described program with a margin may be used. For example, a solid state drive (SSD) may be used. good.
The database unit 60 is an HDD managed by a relational database, and stores population data 61 and a standard advice table 62 (described later).
In the population data 61, personal data of more than 10,000 subjects is accumulated for at least 3 years per day. Details of the database unit 60 will be described later.

(PC configuration)
The PC 70 is a personal computer, and includes a control unit 71, a communication unit 72, an operation unit 73, a display unit 74, a storage unit 80, and the like. These components are connected via a bus B3.
The control unit 71 is a CPU, and controls each unit such as the communication unit 72, the operation unit 73, the display unit 74, and the storage unit 80 that constitute the PC 70.
The communication unit 72 is a network adapter, and transmits personal advice 38 between the PC 70 and the terminal device 20 via the firewall 3. Further, various data including dedicated program commands and personal data are transmitted and received between the PC 70 and the server 50.

The operation unit 73 is an input device such as a keyboard and a mouse.
The display unit 74 employs a liquid crystal panel as a preferred example. Note that a touch panel may be provided on the display surface.
The storage unit 80 is a hard disk drive (HDD) and is managed by a file system. The configuration of the storage unit 80 is not limited to this, and a solid state drive (SSD) may be used. The storage unit 80 stores a support program 81.
The support program 81 is a program for generating personal advice, and is based on the latest personal data using the personal data of the subject received from the server 50, the personal data of a person other than the subject, the reference advice table 62, and the like as input information. Generate personal advice in string format. Details of the support program 81 will be described later.

(Data structure)
Next, the structure of the data group generated in the lifestyle improvement system 100 will be described.
FIG. 4 is a diagram showing a configuration of personal data. The personal data 43 is composed of biological data 40, exercise data 41, and attribute data 42. The personal data 43 is data in text format sorted into each item generated by the terminal device 20.
The biometric data 40 includes data relating to the biometric information of the subject, and is stored for each measurement time interval, body measurement data such as measurement date, weight, height, and body fat, average pulse rate during exercise measured during exercise, and measurement time interval. It consists of measurement data such as the obtained pulse rate. In addition, although not shown in the figure, it includes measurement values obtained by physical measurements such as at the time of medical examination, and blood test results such as total cholesterol, HDL / LDL cholesterol, and HbA1c.
The exercise data 41 includes data measured when the subject exercises. The exercise data 41 is measured or input by the terminal device 20, the measurement date, the number of steps, the calorie consumption, and the pitch measured during exercise (one minute). Measurement data such as the number of steps taken), calories burned, and travel distance.
The attribute data 42 is data related to the personal attributes of the subject, and includes collection information such as sex, date of birth, and job type input by the terminal device 20.
The personal data 43 is not limited to data in text format, and may be data including binary format data.

FIG. 5 is a diagram showing the contents of the database in the server. In the database unit 60 of the server 50 (FIG. 2), population data 61, which is a collection of data used in the lifestyle improvement system 100, is stored. The population data 61 includes a database DB1 and a database DB2.
The database DB1 stores a basic data group 63 as a data group and a reference advice table 62. The basic data group 63 includes biological data, exercise data, and attribute data collected from past clinical trials, and is stored and managed in a database table so that data necessary for analysis can be collected and searched. is there. In the basic data group 63, for example, personal data of more than 10,000 subjects is accumulated for at least three years in units of one day.

FIG. 6 is a diagram showing a part of the reference advice table. The standard advice table 62 is a table in which advice is defined for each data item constituting personal data, and the content of advice is derived based on information stored in the basic data group 63. Specifically, the horizontal axis of the standard advice table 62 includes index items, reference periods, comparison targets, determinations, texts, and the like.
The “index item” is a data item to be referred to. Here, examples of “index items” include body weight, body fat percentage, fat burning zone total time (to be described later), and BMI (Body Mass Index). All data items, or a combination of some of these items are set.
The “reference period” is a period for referring to the data of the subject corresponding to the index item. In the example of body weight in the first row, in order to average the variation due to daily weight change and use the latest value, the weight value of the subject is the average value for the most recent week measured within 4 weeks. Yes.
The “comparison target” is a target value set for the data item of the index item, and is compared with the subject data. A target value corresponding to each data item shown in the index item is derived and set based on the achievement target set before starting the lifestyle improvement action.
“Decision” indicates the relationship between the subject data and the comparison target data by an equation or the like.
“Text” is a character string that is a source of generation advice set for each determination.
The “fat burning zone total time” shown in the “index item” in FIG. 6 is an index for effectively performing aerobic exercise of intensity suitable for fat burning efficiently with less risk. This is the total time spent exercising in the range. Such a pulse rate range is set before the start of exercise by measurement by a leader or a preliminary exercise. For setting the pulse rate range by preliminary exercise, for example, a method disclosed in Japanese Patent Application Laid-Open No. 2011-172903 may be used.
In addition, although not shown, the reason why the job type, work mode, hobby, and the like are set as the “index item” is that energy consumption (calorie consumption) other than during exercise can be grasped. Specifically, when the job type is office work on the desk or outside sales, the office work on the desk consumes less calories as the amount of exercise during work, so the generated advice is more exercise. It is a sentence that promotes and increases calorie consumption. Even before the subject's biological data or exercise data is acquired, it is possible to grasp the behavior and lifestyle conditions that affect the lifestyle improvement of the subject, so appropriate advice based on the subject's specific situation can be given. Can be provided.

Returning to FIG.
The database DB2 stores personal data 64 of the subject. The personal data 64 is a database table constituting a relational database, and each item of the personal data 43 (FIG. 4) is assigned to each item of the database table as a value. Specifically, the biometric data 40 is allocated to the biometric data table, the exercise data 41 is allocated to the exercise data table, and the attribute data 42 is allocated to the attribute data table and stored. The biometric data table, exercise data table, and attribute data table will be described later with reference to FIG.

  7A and 7B are views showing a biometric data table stored in the server 50. FIG. FIG. 7A is a table in which biological data 40 representing the effect of lifestyle improvement is stored, and measured values based on body measurements such as body weight, body fat percentage, waist circumference, BMI, total cholesterol, HDL / LDL cholesterol, It includes test results from blood tests such as HbA1c. These data are measured before the start of lifestyle habit improvement behavior, at the time of measurement once a day, at the time of goal achievement, and at physical examinations at the time of medical examination, and are recorded for each measurement date and time. FIG. 7B is a table in which biological data 40 representing the results of each workout (exercise) is stored, and totals such as workout duration, fat burning zone total time, average pulse rate, maximum pulse rate, etc. Contains the results. FIG. 7C will be described later.

  FIG. 8A shows an exercise data table. The exercise data table is a table in which exercise data 41 representing the results of consumed energy and intake energy measured or inputted by the terminal device 20 is stored, and energy is converted into calorie as consumed calories and intake calories (unit: kcal). Contains the modified value. After the workout, the calories burned after a work with a special load and the calories ingested during a meal or snack are recorded. FIGS. 8B and 8C will be described later.

FIG. 9 is a diagram showing an attribute data table. The attribute data table is a table in which attribute data 42 including personal attributes of the subject is stored, and includes collection information such as gender, date of birth, occupation type, work style, and start date of lifestyle improvement behavior. The attribute data 42 is based on information input to the terminal device 20 by the subject before the start of lifestyle improvement behavior.
The database unit 60 (FIG. 5) uses a relational database engine as a preferred example. The database engine is not limited to this, and may be managed using another database engine such as an object-oriented database or a program programmed in a high-level language.

(PC operation)
FIG. 10 is a flowchart showing the flow of personal advice generation. FIG. 11 is a diagram illustrating an example of personal advice. The advice 90 is advice at the start of action, and the advices 91 and 92 are advices after 3 months and 6 months, respectively.
Hereinafter, description will be made with reference to FIG. The following flow is executed by the control unit 71 controlling each unit including the communication unit 72 based on the support program 81 in the storage unit 80 in the PC 70 shown in FIG.

  In step S1, preparations are made for collecting personal data of the subject. Specifically, first, the personal data 43 (FIG. 4) of the subject is transmitted from the terminal device 20 to the server 50. The personal data 43 is stored as personal data 64 in the database DB2 (FIG. 5). The personal data 64 stored first is information in the attribute data table (FIG. 9) and information in the biometric data table ((FIG. 7A). In the PC 70, the RAM area included in the storage unit 80 is stored. The variable is cleared or the initial value is set.

  In step S2, the PC 70 acquires the personal data 64 of the subject from the server 50 (FIG. 2). Specifically, the PC 70 transmits a test subject's personal data transmission request to the server 50, and the server 50 receiving the request transmits the test subject's personal data 64 to the PC 70. The personal data transmission request from the PC 70 is transmitted periodically (for example, once / day). Further, the PC 70 may receive the personal data 64 from the server 50 when the server 50 receives the new personal data 43 of the subject from the terminal device 20 as a trigger. With this configuration, the personal data 64 stored in the server 50 is notified to the PC 70 in a timely manner.

  In step S3, it is determined whether or not three months have passed since the collection of the personal data 64 of the subject has been started and the lifestyle improvement status can be grasped. Specifically, the start date information in the attribute data table (FIG. 9) is compared with the current date and time, and if it is less than 3 months (Yes), the process proceeds to step S4, and if it is 3 months or more (No), the process proceeds to step S7. .

In step S4, since the data accumulation amount is less than three months, advice is extracted with reference to the standard advice table 62 (FIG. 6). More specifically, data such as body weight, height, and BMI are extracted from the subject's attribute data, such as date of birth, sex, and job type, and biometric data (physical measurement). For each data item of the extracted data, the server 50 is requested for advice corresponding to the index item of the reference advice table 62. The server 50 extracts the corresponding advice from the reference advice table 62, generates character string data including the calculated numerical value necessary for the advice, and transmits it to the PC 70.
The advice example generated here is the advice 90 of FIG.

“<April 10, 2012>
(A-1) Your current physical condition is height 171.3cm, weight 69.8kg, BMI 23.7.
(B-1) The standard weight is 64.8kg, so a 5kg weight loss is required.
(C-1) Predicting from your gender, age, weight, and height, if you exercise 2 hours a day so that your pulse falls within the fat burning zone, you will reach your standard weight in 6 months.
(D-1) If you make an effort to keep the calorie intake per day between 1980 and 2100 kcal, you can achieve standard weight in 3 months. "

The sentence (a-1) of the advice 90 is generated based on the height, weight, and BMI values of the subject.
Next, since the sentence (b-1) is derived from the character string of “text” in the first line of the standard advice table 62 (FIG. 6), “the standard weight is 64.8 kg, so a 5 kg reduction is necessary. " This is the target value for weight loss.
Sentence (c-1) is the total amount of fat burning zone per day required to become standard weight in 6 months based on the subject's sex, date of birth, weight, height, standard weight, etc. Time is calculated and generated as character string data. This is the target value of the total fat burning zone time per day.
The sentence (d-1) is generated as character string data by calculating calorie intake per day, which is a guideline for future meals, based on the subject's sex, date of birth, weight, height, standard weight, etc. ing.
Note that the standard weight value is present as standard weight data derived from age, sex, weight, height, and BMI in a part of the reference advice table 62.

  In step S5, personal advice is generated. Specifically, a single sentence character string data extracted from the server 50 is synthesized, a sentence is created, and a message for the subject is generated. Sentences are organized in the basic order of recognition of the current situation, support for encouraging workouts, and attention and support for meals. In this way, advices 90 to 92 shown in FIG.

  In step S6, personal advice is transmitted. Specifically, the generated personal advice is transmitted to the terminal device 20 of the subject via the network 4. By receiving personal advice such as the advice 90 as an example, the subject can reconfirm the current situation recognition and confirm the contents supporting the action, and can work on future workouts and meals.

Next, steps S7 to S10 as processing when it becomes three months or more in step S3 will be described. In these processes, analysis based on personal data is performed and an advice character string is generated.
In step S7, since the data storage amount has reached a certain level, prediction based on personal data is performed. For example, the average value of the fat burning zone total time per day and the average value of calorie intake per day are calculated from the personal data of the subjects accumulated in time series for at least three months. Next, an average calorie balance per day is calculated from these values, a weight change rate is derived based on the average calorie balance, and a weight after an arbitrary period or a weight change amount is predicted from the current weight. The ideal weight change rate indicating the weight change rate necessary to achieve the target is calculated so that the predicted weight predicted in this way gradually approaches the target weight. This calculation process may be performed by repeated calculation. Based on the calculated ideal weight change rate, the average daily fat burning zone time calculated from personal data, and the average daily calorie intake, the total fat burning zone realizable for the subject The target average value per day of time and the target average value per day of intake calories are calculated. In this way, prediction of weight change and advice information for achieving the goal are determined using the personal data stored in the server 50.

In step S8, the predicted value is compared with the actual measurement value obtained from the subject's biological data (physical measurement). As a result of the comparison, if there is no difference equal to or greater than the predetermined threshold value (Yes), the process proceeds to step S10, and if there is a difference (No), the process proceeds to step S9.
In step S9, difference data analysis is performed. Specifically, the cause of the difference is estimated. The cause is estimated based on the weight and fat burning zone total time included in the personal data, and the meal information input by the subject. First, from the personal data accumulated for three months, the amount of weight change, the average value of the total time of fat burning zone, the average daily calorie intake, etc. are calculated. Next, comparison with each target value in the advice 90 is performed. As a result of the comparison, for example, the body weight has decreased, but the average value of the total fat burning zone time per day is smaller than the target value. On the other hand, when the average daily calorie intake is smaller than the target value, it is judged that the weight loss in the last three months is largely due to dietary restrictions. “There may be stricter dietary restrictions. "Take care not to limit your diet too much."
As another example, although the weight increased as a result of comparison, the average value per day of the total fat burning zone time is larger than the target value. On the other hand, when the average daily calorie intake is higher than the target value, it is determined that the increase in body weight over the past three months is due to excessive dietary intake, and “There is a possibility of eating too much. "Take care not to overdo it." As yet another example, although the weight was decreased as a result of comparison, the average value of the total time of fat burning zone per day is larger than the target value. On the other hand, when the average daily calorie intake is smaller than the target value, it is determined that the increase in weight over the past three months is due to moderate exercise and meals. “I am very good. . ”Is provided.
In step S10, an advice character string is generated. Specifically, if the process proceeds from step S8 to Yes, it is determined that an ideal exercise and meal are being performed, and advice that the action is continued is generated. If the process has proceeded via step S9, advice including guidance on future exercise based on the cause of the difference and the total time of the fat burning zone is generated.
For example, the advice 91 in FIG. 11 is an advice example generated through steps S7 to S9.

“<July 10, 2012>
(A-2) Looking at your activity for 3 months, the total duration of fat burning zone lasted 26 minutes a day for 3 months on average.
(B-2) Weight loss was 1.3 kg. The average daily calorie intake was 1730 kcal.
(C-2) There may be strict dietary restrictions. Be careful not to limit your diet too much.
(D-2) If you continue your exercise for 2 hours, 15 minutes, and 3 months at the same pace as before, you will reach the standard weight. "

The sentence (a-2) of the advice 91 calculates the average time per day using the fat burning zone total time data extracted from the personal data of the subject for three months.
Next, in sentence (b-2), the weight loss and the average daily calorie intake are calculated from the personal data of the subject for three months. From the calculated weight loss, it is reported that the body weight was reduced by 1.3 kg. The calculated average daily calorie intake indicates that the average daily calorie intake for 3 months is 1730 kcal.
In sentence (c-2), weight and fat burning zone total time as the cause of the difference, and dietary information entered by the subject are comprehensively referred to, and the cause of the difference (less calorie intake than standard) was estimated, It refers to the subject's lifestyle (meal).
In sentence (d-2), taking into account the personal data accumulated in addition to the subject's sex, date of birth, weight, height, standard weight, etc., the calorie intake per day that will serve as a guideline for future meals Calculated and generated as character string data.

In step S7, the advice based on the analysis of the personal data is basically generated. However, the advice sent to the past subject or the advice referring to the reference advice table 62 may be generated. .
Also, the advice 92 in FIG. 11 is an advice example generated through steps S8 to S9. The advice 92 is generated after 6 months have passed.

“<October 10, 2012>
The total duration of the fat burning zone lasted for 6 months, averaging 23 minutes per day. Weight increased by 0.9kg. The average daily calorie intake was 2300 kcal. The first half was very good, but the second half gained weight. The weight gain in the second half is probably due to more calories per day than the target.
In the future, you will be able to reach the standard weight by keeping one exercise time for 2 hours, 25 minutes, 3 months and reducing the calorie intake by another 200 kcal per day. "

Thus, even after 6 months have passed, based on the information accumulated in the server 50, based on the weight and total fat burning zone time on the PC 70, and the meal information input by the subject, as well as after 3 months have passed. As a result of comprehensive analysis, the cause of the difference between the predicted value and the actual measurement value (the daily calorie intake is large) was estimated, so the lifestyle habit (meal) of the test subject was “suppressed by another 200 kcal per day” "I advise.
Note that three months as the determination period in step S3 is an example, and a period in which the predicted outcome in step S7 can be derived may be obtained by statistically analyzing the population data.

(Data reference)
Here, a method (ratio) of referring to the database when generating personal advice will be described. The population data 61 placed in the database unit 60 shown in FIG. 5 includes a database DB1 and a database DB2. The data storage amount of the database DB1 is fixed, and the data storage amount of the database DB2 is increased every time personal data is transmitted from the terminal device 20. Therefore, in the population data 61, while the personal data is sent from the subject, the accumulation ratio of the personal data increases.

FIG. 12 is an image diagram showing the ratio of reference data until personal advice is generated in the PC 70.
The population data 110 stores personal data 120 at the beginning of the action. The PC 70 generates the personal advice 90 with reference to the standard advice table 62 because the personal data 120 does not have a sufficient accumulation amount. The population data 111 stores personal data 121 after three months. The PC 70 generates the personal advice 91 by referring to the personal data 121 and the standard advice table 62. In the population data 112, personal data 122 after 6 months has been accumulated. The PC 70 generates the personal advice 92 with reference to the personal data 122 having the data amount for 6 months. As the rate of referring to the personal data increases, the generated personal advice becomes the message content specific to the individual.

  As described above, according to the lifestyle improvement system 100 according to the present embodiment, the following effects can be obtained.

According to the lifestyle improvement system 100, population data including a basic data group stored in advance is stored in the server 50. The PC 70 generates personal advice using the basic data group until the personal data is accumulated for 3 months. When the personal data is 3 months or more, the PC 70 generates personal advice referring to the stored personal data. That is, the personal advice is generated by changing the reference ratio between the population data and the personal data according to the amount of personal data accumulated.
Therefore, unlike the conventional system in which it is difficult to provide effective advice at the beginning of the test subject's behavior, the lifestyle improvement system 100 uses the basic data group even in such a case. Advice can be provided.
Therefore, effective advice for achieving the goal can be provided even at the initial stage of the start of the action of the subject.

Also, the period until the correlation rule described in the prior art document is derived is different from the conventional system in which it is difficult to provide effective advice to the subject. In the period, personal advice can be provided in the same manner using the basic data group.
Therefore, effective advice can be provided even during the period until the association rule is derived.

In addition, unlike the conventional system in which it is difficult to convey behavioral risks to the subject, the lifestyle improvement system 100 can provide personal advice for calling attention using biometric data. . For example, when the pulse rate of the biological data is used and the pulse rate is abnormally high, advice for calling attention can be provided.
Therefore, it is possible to provide advice for calling attention to risks in behavior.
Therefore, the lifestyle improvement system 100 that can provide useful advice from the start of use can be provided.

(Embodiment 2)
FIG. 13 is a flowchart showing the flow of personal advice generation in the second embodiment. The second embodiment is different from the first embodiment in a part of the support program 81 in the lifestyle improvement system 100. The following flow is executed by the control unit 71 controlling each unit including the communication unit 72 based on the support program 81 in the storage unit 80 in the PC 70 shown in FIG.

  In the first embodiment, a period of less than three months is used as an index of the accumulated amount, but in the present embodiment, the number of personal data or the number of data is different from the first embodiment. For example, when the subject actively exercises, the personal data is frequently transmitted, which is effective when a large amount of personal data is accumulated in a short period of time.

  Steps S1, S2, and S4 to S10 in the flowchart of FIG. The process of step S11 will be described.

  In step S11, it is determined whether the number of subjects' personal data is less than 60. Specifically, the number of movement data (FIG. 8B) and biological data (FIG. 7B) for each workout, or the number of data is counted, and if less than 60 (Yes), the process proceeds to step S4. If it is 60 or more (No), the process proceeds to step S7. In addition, 60 cases as the number of determination cases is an example, and the number of cases where the predicted outcome in step S7 can be derived may be obtained by statistically analyzing the population data.

As described above, according to the lifestyle improvement system 100 according to the present embodiment, in addition to the effects in the first embodiment, the following effects can be obtained.
According to the lifestyle improvement system 100, the number of personal data is used as an index of the amount of personal data stored. Therefore, even when the number of personal data is within a predetermined number, it is possible to provide personal advice that is analogized with reference to the attribute data. Therefore, when the subject's behavior starts or when the behavior becomes a single behavior, effective advice for achieving the goal can be provided even during the period until the association rule is derived.
In particular, there is also an effect that for subjects who are actively carrying out lifestyle habit improvement behavior, it is expected to produce remarkable results because advice based on prediction is provided at a relatively early stage from the start of the behavior. .

(Embodiment 3)
FIG. 14 is a flowchart showing the flow of personal advice generation in the third embodiment. The third embodiment is different from the first embodiment in a part of the support program 81 in the lifestyle improvement system 100.

  In the first embodiment and the second embodiment, the period or the number of data is used as an index of the personal data accumulation amount, whereas in the present embodiment, the point at which a predetermined rule is extracted is described above. Different from the embodiment. For example, it is effective for a subject who is unsuitable for continuing lifestyle improvement behavior at a relatively early stage, that is, a subject who is easily bored.

First, since a new data table is required in this embodiment, the data table will be described with reference to FIGS. 7C, 8B, and 8C. In addition, the said data table is a table which comprises the relational database in which the personal data 43 (FIG. 4) transmitted from the test subject was allocated and stored for every data item. Specifically, the biometric data 40 is allocated to the biometric data table, and the exercise data 41 is allocated and stored in the exercise data table.
FIG. 7C is a table in which biological data representing the physical state during workout is stored, and includes a measurement value of the pulse rate (beats / minute) measured during the workout. FIG. 8B is a table in which exercise data representing results for each workout is stored, and includes total results such as workout duration, total calories burned, total number of steps, total travel distance, exercise intensity, and the like. It is out. FIG. 8C is a table storing exercise data representing the progress of exercise during workout, and measured values such as travel distance, calorie consumption, travel altitude, pitch (steps / minute) measured during workout. Is included.

Next, a correlation rule as a predetermined rule will be described. For example, if a test subject obtained data indicating that body fat mass decreased as a result of an improvement activity, analyze which index contributed to `` decrease in body fat mass '' in the time series and set the correlation rule. Extract. Specifically, it is analyzed what kind of exercise / meal was done when the body fat amount decreased. For the result that the amount of body fat has decreased, a combination with high reliability (having a law) among candidates for combinations of exercise and meal data becomes an association rule. For example, in a subject, a correlation rule has been extracted that “when the total fat burning zone time is 200 minutes or more and supper is 874 kcal or less, body fat decreases from the previous day”.
Exercise and meal data include calorie intake during meals, calories burned during exercise, fat burning zone total time, number of steps, and the like. The body fat mass is determined from the measured body fat percentage and body weight. The extraction of the association rule is realized when there are a lot of data to be referred to, and the reliability and the support are analyzed by the data mining method and satisfy a predetermined threshold value or more.

Next, the flowchart shown in FIG. 14 will be described. Steps S1, S2, and S4 to S6 are the same as those in the first embodiment, and will not be described. The process of steps S21 to S23 will be described.
In step S21, the personal data of the subject is analyzed and the correlation rule is extracted. Specifically, candidates for correlation rules appearing in data relating to exercise and diet and data relating to body fat mass are extracted from the personal data of subjects accumulated in time series.

  In step S22, it is determined whether the correlation rule has been extracted in step S21. If extracted, the process proceeds to step S23 (Yes), and if not extracted, the process proceeds to step S4 (No).

In step S23, candidates are selected from the association rules extracted in step S21. Specifically, in the association rule extracted in step S21, combinations of actions that are not suitable as conditions and combinations of complex actions are excluded by a list defined in advance as incompatible combinations. The selected correlation rule is generated as a text by text synthesis. To quote the above example, the sentence "You tend to decrease body fat mass the next day if you set the total fat burning zone time to 200 minutes or more and supper to 874 kcal or less" is generated.
In this embodiment, the target of the association rule is data related to exercise / meal and data related to body fat mass. However, the present invention is not limited to this example, and other data may be used as long as the data represents behavior and results. May be.

As described above, according to the lifestyle improvement system 100 according to the present embodiment, the following effects can be obtained in addition to the effects of the first and second embodiments.
According to this embodiment, until the correlation rule is extracted by accumulating personal data, the personal advice is generated by referring to the standard advice table, and when the correlation rule is extracted, it is based on the subject's own correlation rule. Generate personal advice.
Therefore, in the initial stage until a predetermined rule is extracted, it is possible to provide effective advice with reference to the standard advice table. Furthermore, when an association rule is extracted, more accurate advice based on the behavior pattern of the subject can be provided. Therefore, by providing useful advice in accordance with the period from the beginning of the behavior of the subject to the achievement of the goal, the behavior of the subject toward the goal can be supported.
Moreover, there is a possibility that the subject can be provided with advice at an early stage from the start of the action. Moreover, since the advice is content based on behavior information obtained by analyzing the behavior of the subject, the content is easily familiar to the subject and reasonable. Therefore, it becomes easy for the subject who gets tired easily to continue the lifestyle improvement behavior from an early stage, and there is an effect of increasing the success rate of the goal achievement.

  Note that the present invention is not limited to the above-described embodiment, and various modifications and improvements can be added to the above-described embodiment. A modification will be described below.

(Modification 1)
This will be described with reference to FIG.
In the present embodiment, the case where the generated personal advice is displayed on the display panel 21 of the terminal device 20 shown in FIG. 1 has been described. However, a command for controlling the terminal device 20 is generated based on the content of the personal advice. Also good.
Specifically, the PC 70 generates a command list controlled by the terminal device 20 in the process of generating personal advice as a character string. The generated command list is stored in the first storage unit 32 of the terminal device 20 and is executed by the control unit 47. When executed, an instruction is issued each time during a workout to the subject, and the behavior of the subject is induced.
For example, in the example shown in the third embodiment, the correlation rule is “when the total fat burning zone time is 200 minutes or more and supper is set to 874 kcal or less, the body fat amount on the next day is reduced”. When this correlation rule is executed as the program, the terminal device 20 executes the following operation.

"Operation example of terminal device 20"
When the subject tries to finish the workout in 190 minutes, he gives an instruction to continue the workout as “Let's continue for another 10 minutes”. Also, after a 200-minute workout, caution is given to “Let's make dinner less than 874 kcal”.
In this way, personal advice not only presents a message of a character string as static information, but also makes it possible to give an appropriate instruction at an appropriate timing to a subject wearing the terminal device 20 and acting.
Therefore, it is possible to provide timely advice that encourages the subject to improve, and the success rate for achieving the goal can be increased.

(Modification 2)
This will be described with reference to FIG.
In the present embodiment, the terminal device 20 has been described as a wristwatch-type biological data measuring device, but is not limited to this single device, and may be any portable terminal device having a wireless function.
For example, the terminal device 20 may be a smartphone, a mobile phone, a PDA (Personal Digital Assistant), a PC, or the like. Further, although the sensor unit 24 has been described as being included in the terminal device 20, the sensor unit 24 may be configured as an electronic device separate from the terminal device 20. In that case, what is necessary is just to comprise the sensor part 24 so that it may be connected with the terminal device 20 electronically by wire or radio | wireless. Furthermore, the biological data is not limited to the pulse rate, and may be other biological data, for example, data relating to so-called biological information such as body temperature, blood pressure, blood oxygen concentration, and the like. Furthermore, a combination of a plurality of devices may be used. For example, body information measuring devices such as body weight measuring devices, body composition measuring devices, blood pressure measuring devices, devices that measure exercise information such as pedometers and calorie consumption measuring devices, GPS receiving terminals, temperature / humidity measuring devices, etc. It may be a combination of dedicated equipment such as environmental information collection equipment. In the case of a combination of a plurality of devices, any configuration may be used as long as information on the subject can be transmitted to the server 50. By using such a combination of devices, it is possible to collect a variety of information related to the subject and to generate more detailed personal advice.

(Modification 3)
This will be described with reference to FIG.
In the present embodiment, the “comparison target” in the reference advice table 62 shown in FIG. 6 is an achievement target set before the start of lifestyle improvement behavior, and the content thereof includes standard values such as standard weight and reference value. Although it was set, it may be set not only before starting improvement action but at arbitrary time. The content of the achievement target is not limited to the standard value, and may be a target value set by the subject. Specifically, the target value can be set and changed by the subject before and after the start of behavior improvement activities. When the content of “comparison target” is changed, the PC 70 generates personal advice based on the value newly set as the comparison target. In addition, the change of the target value by the subject is not limited to the input by the terminal device 20, but can be input via a browser on a smartphone or a PC by a web page provided from the PC 70 or the like. As described above, since the achievement target can be set and changed at any time according to the subject's will, the subject's willingness to achieve the target is stimulated, and the success rate of the goal achievement can be increased.

(Modification 4)
FIG. 15 is a diagram illustrating an outline of an action support system according to the fourth modification.
In the present embodiment and the modified example, the configuration of the terminal device 20, the server 50, and the PC 70 has been described. In addition, a smartphone, tablet PC having a display size larger than the display panel 21 of the terminal device 20 and having network transmission / reception The information terminal 98 may be included.
Specifically, the information terminal 98 includes software that transmits / receives personal data and personal advice data to / from the terminal device 20 by communication means such as short-range wireless communication. Further, the software has a function of displaying personal data and personal advice data on the information terminal 98, a function of transmitting / receiving data to / from the server 50 and the PC 70 via the Internet.
The PC 70 generates personal advice and transmits it to the information terminal 98. Here, the PC 70 generates data including multimedia information such as personal advice with a graph image, a moving image, an image, and sound suitable for display on the information terminal 98 as personal advice. The information terminal 98 displays or reproduces personal advice received from the PC 70. In addition, the information terminal 98 extracts personal advice from the personal advice received from the PC 70, such as content that needs to be confirmed immediately before or during exercise, and transmits the extracted personal advice to the terminal device 20. In this way, by adding the information terminal 98 to the configuration, it is possible to transmit using the device (the terminal device 20 or the information terminal 98) suitable for the condition of the subject according to the content of the personal advice, and the subject can take the personal advice as an action. Because it becomes easier to reflect, the success rate of achieving the goal can be increased.

(Modification 5)
This will be described with reference to FIGS. 10, 13, and 14.
10, 13, and 14 are flowcharts showing the flow of personal advice generation in the above-described embodiment. This flow is a flow executed by the control unit 71 controlling each unit including the communication unit 72 based on the support program 81 of the storage unit 80 in the PC 70 shown in FIG.
The personal advice generation in step S5 is a process common to FIG. 10, FIG. 13, and FIG. In the above-described embodiment, the text is generated by synthesizing the single sentence character string data extracted from the server 50. In addition, the text is generated by synthesizing the character string data generated by the PC 70. You may create it. Specifically, the character string generated by the PC 70 includes a message for inquiring about the situation for a specific subject who is behind the transmission of personal data, a message including information on introduction of products that the subject is likely to be interested in, a new message Contains messages that contain information about the introduction of the service that started. By adding such a message, the subject's interest is attracted, and the continuation of the lifestyle habit improvement behavior of the subject is supported, and the success rate of achieving the goal can be increased.

  DESCRIPTION OF SYMBOLS 1 ... Internet, 2 ... Intranet, 3 ... Firewall, 4 ... Network, 5 ... Wrist, 20 ... Terminal device, 21 ... Display panel, 22 ... Operation button, 23 ... Pulse wave sensor, 24 ... Sensor part, 25 ... Biosensor 26: Motion sensor unit, 27 ... Environmental sensor unit, 30 ... Operation unit, 31 ... Timekeeping unit, 32 ... First storage unit, 33 ... Attribute input program, 34 ... Data generation program, 35 ... Button event processing program, 36 ... second storage unit 37 ... common variable 38 ... personal advice 39 ... advice display window 40 ... biological data 41 ... exercise data 42 ... attribute data 43 ... personal data (terminal device) 46 ... communication , 47 ... control unit, 48 ... display drive unit, 50 ... server, 51 ... control unit, 52 ... communication unit, 53 ... storage unit, 60 ... database 61, population data, 62 ... reference advice table, 63 ... basic data group, 64 ... personal data (server), 70 ... PC, 71 ... control unit, 72 ... communication unit, 73 ... operation unit, 74 ... display Part, 80 ... storage part, 81 ... support program, 90, 91, 92 ... advice, 98 ... information terminal, 100 ... lifestyle improvement system, 110, 111, 112 ... population data, 120, 121, 122 ... personal data , DB1, DB2 ... database.

Claims (8)

A terminal device that transmits personal data including a plurality of data items including biological data of the subject and exercise data;
A server that stores population data composed of pre-stored data groups corresponding to the data items, and receives and accumulates the personal data;
A computer installed with a support program for generating personal advice based on the population data and / or the personal data;
The computer generates the personal advice by changing a reference ratio between the population data and the personal data in accordance with an accumulation amount of the personal data. The computer generates the personal advice by referring to a standard advice table derived using at least attribute data in the data group when the accumulation period of the personal data is less than a predetermined period,
The behavior support system according to claim 1, wherein the personal advice is generated using the population data and the personal data when the personal data is accumulated for the predetermined period or longer. The computer generates the personal advice by referring to a standard advice table derived using at least the attribute data in the data group when the accumulated amount of the personal data is less than a predetermined number of data,
2. The behavior support system according to claim 1, wherein when the personal data is accumulated more than the predetermined number of data, the personal advice is generated using the population data and the personal data. . The computer generates the personal advice referring to a standard advice table derived using at least the attribute data in the data group until a predetermined rule is extracted by the accumulation of the personal data,
The behavior support system according to claim 1, wherein, when the predetermined rule is extracted, the personal advice is generated based on the predetermined rule. The attribute data includes at least gender and date of birth,
The action support system according to any one of claims 1 to 4, wherein the attribute data is referred to when the personal advice is generated with reference to the reference advice table. In addition to the pulse rate, the biological data includes
The behavior according to any one of claims 1 to 5, comprising at least one of the group consisting of body weight, body fat percentage, waist circumference, BMI, total cholesterol level, cholesterol level, and HbA1c level data. Support system. A terminal device for displaying personal advice for a subject,
Collecting personal data consisting of a plurality of data items including the subject's biological data and exercise data, and sending the personal data to a database for storing the data,
Analyzing the database and / or the personal data and receiving the personal advice from a data processing unit that generates the personal advice;
The personal advice is generated in the data processing unit by changing the reference ratio between the database and the personal data according to the amount of the personal data accumulated,
A terminal device of an action support system, wherein the personal advice received from the data processing unit is displayed. A server that generates personal advice for a subject,
A storage unit for storing personal data including a plurality of data items including biological data of the subject and exercise data;
A database comprising data corresponding to the plurality of data items of the personal data;
A data processing unit that analyzes the database and / or the personal data and generates the personal advice for the subject;
A server for an action support system, wherein the personal advice is generated by changing a reference ratio between the database and the personal data in accordance with a storage amount of the personal data.

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