JP7828079B2 - Method, device, program, and system for measuring subject's health - Google Patents

Description

The present invention relates to a method for measuring the health of a subject.

The stage before health progresses to the onset of disease is called pre-illness. In a society with an extremely low birthrate and aging population, where soaring medical costs threaten to lead to economic collapse, there is a need for the development of technology that can visualize and quantify the transition from health to the pre-illness state in order to prevent the onset of disease.

For example, Patent Document 1 discloses a technology for evaluating the health status of subjects by obtaining data on multiple test items for multiple subjects and creating a function that uses multiple test data items as variables.

Patent No. 6069598

However, the above-mentioned conventional technologies perform risk assessments by taking measurements along specific axes, such as lifestyle-related diseases like diabetes and arteriosclerosis, cancer, dementia, sarcopenia, liver disease, and kidney disease, along a pathway from healthy to diseased, leading to disease. This poses a problem in that it is not possible to assess a person's overall health status. To address this issue, the inventors invented a means (health positioning map, health function, etc.) that can assess various health risks (International Application No. PCT/JP2019/43062).

The applicant has developed a means of easily assessing health risks at home, etc., starting from this invention. This is because the prior art described above requires invasive tests such as blood tests, which cannot be easily performed at home, etc.

One aspect of the present invention aims to provide a means for assessing a person's overall health through a simple test.

The present invention provides, for example, the following items.
(Item 1)
1. A method for measuring the health of a subject, comprising:
Step (1): Acquiring a data set for a parameter set, the parameter set including autonomic nervous parameters, body composition parameters, and questionnaire parameters, and step (1)
I. Obtaining data on the autonomic nervous parameters using an autonomic nervous system measuring device for measuring the autonomic nervous parameters;
II. Obtaining data for said body composition parameters using a body composition analyzer;
III. Obtaining data for said questionnaire parameters using a questionnaire processing device for measurement of said questionnaire parameters;
and
Step (2): transmitting the data set to a server device;
Step (3): receiving health level information derived based on the data set from the server device;
Step (4): Expressing the health level as a position on a health level positioning map of the health level information.
(Item 2)
2. The method of claim 1, wherein the parameter set further includes a blood pressure parameter.
(Item 3)
The step (1)
IV. Obtaining data for said blood pressure parameter using a sphygmomanometer;
Item 3. The method according to item 2, comprising:
(Item 4)
4. The method according to any one of items 1 to 3, wherein step (1) includes acquiring the data set using at most four measurement devices.
(Item 5)
5. The method of claim 4, wherein each of the at most four measuring devices is portable or handheld.
(Item 6)
4. The method according to any one of items 1 to 3, wherein step (1) includes acquiring the data set using one measuring device.
(Item 7)
7. The method according to any one of items 1 to 6, wherein the health level information includes an X-axis value and a Y-axis value in the health level positioning map, and step (4) includes displaying the health level information on the health level positioning map based on the X-axis value and the Y-axis value.
(Item 8)
8. The method according to claim 7, wherein the step (3) includes the measuring device used in the step (1) receiving health information from the server device, the health information including an X-axis value and a Y-axis value in the health positioning map.
(Item 9)
9. The method according to claim 7, wherein the step (4) includes the measuring device used in the step (1) displaying the health level information on the health level positioning map displayed on a display unit of the measuring device based on the X-axis value and the Y-axis value.
(Item 10)
Step (5): repeating steps (1) to (4) at least twice to determine a first position and a second position on the health positioning map;
Step (6): Evaluating the health of the subject based on a trajectory including the first position and the second position.
(Item 11)
Item 11. The method according to item 10, wherein step (5) comprises repeating steps (1) to (4) at least twice a day.
(Item 12)
12. The method according to any one of items 1 to 11, wherein the step (1) includes automatically acquiring at least a portion of the dataset without the subject being aware of it.
(Item 13)
13. The method according to any one of items 1 to 12, wherein the step (1) includes automatically acquiring data on the autonomic nerve parameters and data on the body composition parameters without the subject being aware of the data, in response to acquiring data on the questionnaire parameters.
(Item 14)
14. The method of any one of items 1 to 13, wherein the server device is configured to derive the health information using a health function that correlates the dataset with the subject's position on the health positioning map.
(Item 15)
15. The method of any one of items 1 to 14, wherein the time at which the data for the autonomic parameters, the time at which the data for the body composition parameters, and the time at which the data for the questionnaire parameters were acquired are each within about 5 minutes.
(Item 16)
16. The method according to any one of items 1 to 15, wherein steps (2) to (4) are performed within a predetermined time.
(Item 17)
17. The method of claim 16, wherein the predetermined time is about 1 minute.
(Item 18)
the health positioning map is created using a parameter set for creating a health positioning map;
18. The method according to any one of items 1 to 17, wherein the parameter set is part of a parameter set for generating the health positioning map.
(Item 19)
A method for controlling a device according to a subject's health status, comprising:
generating an instruction to control the device based on a position on a health level positioning map of the health level information in the method according to item 1;
and controlling the device in accordance with the instructions.
(Item 20)
After controlling the device,
repeating steps (1) to (4) to determine a second position on the health positioning map; and
generating second instructions for controlling the device based on the location and the second location.
(Item 21)
An apparatus for measuring the health of a subject, comprising:
An acquisition means for acquiring a data set for a parameter set, the parameter set including autonomic nerve parameters, body composition parameters, and questionnaire parameters, the acquisition means comprising:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Acquiring data for the questionnaire parameters from a questionnaire processing device for measurement of the questionnaire parameters;
a transmitting means for transmitting the data set to a server device;
a receiving means for receiving health level information derived based on the data set from the server device;
and a representation means for representing the health level as a position on a health level positioning map of the health level information.
(Item 22)
A program for measuring the health of a subject, the program being executed on a computer device, the program comprising: step (1): acquiring a data set for a parameter set, the parameter set including autonomic nervous parameters, body composition parameters, and questionnaire parameters, and step (1) comprising:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Obtaining data for said questionnaire parameters from a questionnaire processing device for measurement of said questionnaire parameters;
and
Step (2): transmitting the data set to a server device;
Step (3): receiving health level information derived based on the data set from the server device;
Step (4): Expressing the health level as a position on a health level positioning map of the health level information.
(Item 23)
A system for measuring a health level of a subject, the system comprising: a terminal device; and a server device;
The terminal device
Step (1): Obtaining a dataset for a parameter set;
Step (2): transmitting the data set to the server device;
The server device
Step (A): deriving health information using a health function that correlates the data set with the subject's position on a health positioning map;
Step (B): transmitting the health level information to the terminal device;
The terminal device
Step (3): receiving the health level information from the server device;
Step (4): Expressing the health level as a position on a health level positioning map of the health level information.

One aspect of the present invention provides a means for assessing a person's overall health through a simple test. This provides technology that can measure health status at any time, whether at home or in an office environment, and track changes in that status, as well as technology that can use this to develop personalized solutions for improving daily habits.

FIG. 10 is a diagram illustrating an example of the flow of a new service for visualizing a user's health condition. FIG. 10 is a diagram showing an example of a screen 1000 showing the health condition of a user. FIG. 2 illustrates an example of the configuration of a server device 100. FIG. 2 is a diagram illustrating an example of the configuration of a processor unit 120 according to an embodiment. FIG. 10 is a diagram illustrating an example of the configuration of a processor unit 130 according to another embodiment. FIG. 10 is a diagram illustrating an example of the configuration of a processor unit 140 according to yet another embodiment. FIG. 2 is a diagram illustrating an example of a data flow according to an embodiment of the present invention. FIG. 2 is a diagram illustrating an example of the configuration of a terminal device 300 according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of the data configuration of a first data set stored in the database unit 200. 10 is a flowchart illustrating an example of processing in the server device 100. 10 is a flowchart illustrating another example of the processing in the server device 100. 10 is a flowchart illustrating another example of the processing in the server device 100. 10 is a flowchart illustrating another example of the processing in the server device 100. 7B is a flowchart illustrating an example of a process following the process 700 shown in FIG. 7A. FIG. 1 is a diagram showing an example of data flow in a method implemented in the computer system 10 of the present invention. FIG. 3 is a diagram showing an example of a health assessment map created by the health assessment device. FIG. 10 is a diagram showing the results of clustering plot distribution patterns in this example. FIG. 10 is a diagram showing a health level positioning map created in one embodiment. FIG. 10 is a diagram showing the results of comparing the mapping positions on the health positioning map before and after taking reduced CoQ10 for three months. FIG. 1 shows the results of comparing test parameters before and after taking reduced CoQ10 for three months. FIG. 10 shows the correlation coefficient between the actual measured values and predicted values of the health function using the second parameter set of 19 items from four measurements, and the correlation coefficient between the actual measured values and predicted values of the health function using the second parameter set of 18 items from three measurements. FIG. 1 shows the results of Example 6.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that in this specification, the word "approximately" means ±10% of the following numerical value.

The inventors of the present invention have developed a new service for visualizing a user's health status . This service acquires various data related to the user's health and, based on the acquired data, provides the user with information indicating in which region on a health positioning map the user's health is positioned. Here, a health positioning map refers to a map having multiple regions characterized by health-related information.

Each region of the health positioning map can be characterized as a different health state. For example, one region of the health positioning map can be characterized as a young-adult/mental health disease risk group, another region of the health positioning map can be characterized as a middle-aged-elderly/lifestyle-related disease risk group, and yet another region of the health positioning map can be characterized as an elderly/diabetes risk group. For example, one region of the health positioning map can be characterized as a pre-disease group for a certain health condition, and another region can be characterized as a high-risk group for that health condition. Note that these characterizations are merely examples, and various characterizations are possible. For example, as shown in Figure 1B (described below), health levels A to C can be characterized based on physical health and mental health. Such characterizations are simple, and health states can be intuitively understood. The characterization of each region of the health positioning map can be determined, for example, by analyzing the trends of multiple subjects who belong to each region.

With this new service, users can obtain information on which area on a health positioning map their health level is located based on the results of simple measurements they can take at home. With this new service, users can also obtain visual, real-time information on which area on a health positioning map their health level is located.

Figure 1A shows an example flow of a new service for visualizing a user's health status.

In step S1, user U performs several measurements at home H. The measurements include, for example, measurements using a measuring device 20 and measurements using a computer device 30. The measuring device 20 is any measuring device that can be used at home H, including, but not limited to, a body composition scale 21 and a blood pressure monitor 22. The computer device 30 is any computer device that can be used at home H, including, but not limited to, a smartphone 31 and a personal computer 32. The measurements using the measuring device 20 include measurements using a body composition scale 21, thereby obtaining data on body composition. The measurements using the measuring device 20 may further include measurements using a blood pressure monitor 22, thereby obtaining data on blood pressure. The measurements using the computer device 30 include measurements of autonomic nervous function using a camera, thereby obtaining data on autonomic nervous function. The measurements using the computer device 30 include measurements using a questionnaire administered to user U, thereby obtaining data including living conditions and/or subjective assessments.

For example, the blood pressure monitor may be a type that is worn on the body (e.g., an upper arm blood pressure monitor, a wristwatch blood pressure monitor, or a ring blood pressure monitor), or a non-contact blood pressure monitor (e.g., a blood pressure monitor that measures blood pressure from an image of the face or finger taken with a camera).For example, the body composition monitor may be a type that is placed on during measurement, or a type that can be worn on the body (e.g., a wristwatch body composition monitor).

1 illustrates the use of three devices (body composition monitor, blood pressure monitor, and computer device 30) to perform four measurements, but the present invention is not limited to this. For example, four devices may be used to perform four measurements (e.g., body composition measurement using body composition monitor 21, blood pressure measurement using blood pressure monitor 22, autonomic nervous function measurement using an autonomic nervous function monitor capable of measuring autonomic nervous function, and questionnaire measurement using a questionnaire processing device capable of acquiring data from a questionnaire), two devices may be used to perform four measurements (e.g., body composition measurement and blood pressure measurement using a device capable of measuring body composition and blood pressure, and autonomic nervous function measurement and questionnaire measurement using computer device 30), or one device may be used to perform four measurements (e.g., measurements using a device capable of measuring body composition, blood pressure, autonomic nervous function, and questionnaire data). Alternatively, for example, three measurements may be performed using three devices (e.g., body composition measurement using the body composition analyzer 21, autonomic nervous function measurement using an autonomic nervous function measuring device capable of measuring autonomic nervous function, and questionnaire measurement using a questionnaire processing device capable of acquiring data from a questionnaire), two devices may be used to perform three measurements (e.g., body composition measurement using an device capable of measuring body composition, autonomic nervous function measurement using the computer device 30, and questionnaire measurement), or one device may be used to perform three measurements (e.g., measurement using an device capable of measuring body composition, autonomic nervous function, and questionnaire data). In this way, the devices used for the measurements may be multiple separate devices or a single device. In the case of measurements using a single device, the single device may be the computer device 30 (e.g., a smartphone 31, a personal computer 32, a tablet, etc.). Alternatively, the single device may be a device separate from the computer device 30. In this case, the computer device 30 is used for communicating and displaying information.

Measurements using such measuring devices are non-invasive. Such measurements can be performed in a short time through simple actions, such as simply wearing the device, standing on it, appearing in front of the camera, or answering questions. Furthermore, simple and non-invasive measurements using such minimal measuring devices allow multiple data sets to be acquired nearly simultaneously, making the time difference between each data set small or negligible. For example, by performing multiple measurements nearly simultaneously, the difference between the completion time of the measurement from which the earliest data was acquired and the completion time of the measurement from which the latest data was acquired (i.e., the time difference between the data sets) can be within 1 second, 2 seconds, 5 seconds, 10 seconds, 15 seconds, 20 seconds, 30 seconds, 1 minute, 2 minutes, 3 minutes, 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, or 6 hours. For example, it is preferable to set the difference between the completion time of the measurement from which the earliest data was acquired and the completion time of the measurement from which the latest data was acquired to within 5 minutes. This is because the influence of the time difference between each data item can be ignored if the time difference between each data item is within 5 minutes. In particular, data measured by an autonomic nervous system monitor or a blood pressure monitor can fluctuate from moment to moment, so it is preferable to be able to ignore the influence of the time difference between each data item. This makes it possible to display the position on the health positioning map based on the accurate health level at a given point in time.

Measurements may be performed consciously or unconsciously by the user U. For example, measurements can be performed automatically when a predetermined condition is met, allowing the user U to perform the measurements without being aware of the measurement. Measurements can be performed, for example, using a measuring device worn on the user U's body, without the user U being aware of the measurement. For example, by using a measuring device worn on the user U's body, measurements can be performed regardless of whether the user U is indoors or outdoors (for example, whether the user U is walking or standing still). Alternatively, or in addition, measurements can be performed, for example, using a measuring device installed within the user U's range of activity, without the user U being aware of the measurement. For example, by using a measuring device installed within the user U's range of activity, measurements can be performed regardless of what the user U is doing within the range of activity (for example, whether the user U is walking or standing still). The measuring devices installed within the user U's range of movement include, but are not limited to, sensors mounted on furniture used by the user U (e.g., chairs, beds, etc.), sensors mounted on electronic devices used by the user U (e.g., personal computers, refrigerators, air conditioners, smart speakers, etc.), and sensors installed in a room in the user U's home, etc. For example, blood pressure can be automatically measured by a blood pressure monitor worn on the body when a predetermined condition is met. For example, body composition can be automatically measured by a body composition analyzer worn on the body when a predetermined condition is met. For example, body composition can be automatically measured by a body composition analyzer mounted on the seat of the user's chair when a predetermined condition is met. For example, autonomic nervous function can be automatically measured by a camera of the computer device 30 when a predetermined condition is met. For example, autonomic nervous function can be automatically measured by a camera mounted on an electronic device used by the user U when a predetermined condition is met. For example, autonomic nervous function can be automatically measured by a camera installed in a room in the user U's home, etc. when a predetermined condition is met. For example, data including the living situation and/or subjective evaluation can be automatically measured from communication with an electronic device (e.g., a smart speaker) used by the user U. The predetermined conditions include, for example, a predetermined time (temporal condition), entering/leaving a predetermined location (geographical condition), performing a predetermined behavior (behavioral condition), etc. The predetermined time may be, for example, a predetermined time of day (e.g., between 6:00 AM and 8:00 AM, between 11:00 AM and 1:00 PM, between 4:00 PM and 6:00 PM, between 9:00 PM and midnight, or 6:00 AM, midnight, 6:00 PM, midnight, etc.), or may be a predetermined interval (e.g., every hour, every three hours, every six hours, every 12 hours, etc.). The predetermined location may include, but is not limited to, a company, home, living room, bedroom, etc. The specified actions include, but are not limited to, for example, sitting in a chair, lying down on a bed, or being within the field of view of a camera mounted on an electronic device used by user U or a camera installed in a room in user U's home or the like for a certain period of time (e.g., 10 seconds, 30 seconds, 1 minute) or more.

In one embodiment, the predetermined action may be, for example, acquiring at least one piece of data. For example, in response to acquiring data through at least one of the four measurements, other of the four measurements may be automatically performed, thereby enabling the other of the four measurements to be performed without the user U being aware of it. For example, in response to the user U consciously answering a questionnaire, body composition, autonomic nervous function, and/or blood pressure may be automatically measured. This allows the user U to perform multiple measurements with the simple action of simply answering a questionnaire.

In step S2, the data measured in step S1 is imported into the computer device 30. For example, data on body composition measured using the body composition monitor 21 is input into the computer device 30. This may be done, for example, by establishing communication between the body composition monitor 21 and the computer device 30, or by the user U inputting the measurements taken by the body composition monitor 21 into the computer device 30. For example, data on blood pressure measured using the sphygmomanometer 22 is input into the computer device 30. This may be done, for example, by establishing communication between the sphygmomanometer 22 and the computer device 30, or by the user U inputting the measurements taken by the sphygmomanometer 22 into the computer device 30. In step S1, the data on autonomic nervous function and the data on the questionnaire were measured using the computer device 30, and therefore these data have already been imported into the computer device 30.

In step S3, the data captured in step S2 is transmitted from the computer device 30 to the service provider's server device S via the network 40. The server device S can derive health information for user U based on the measurement results for user U received from the computer device 30. The health information for user U includes information indicating in which area on the health positioning map user U is positioned.

In step S4, the health information derived by the server device S is transmitted from the server device S to the computer device 30 via the network 40.

In step S5, the computer device 30 displays to the user U the region on the health positioning map in which the user U's health level is located based on the health level information. The user U can recognize their own health status by referring to the information associated with the region on the health positioning map in which their health level is located. For example, if the user U's health level falls within the region characterized as health level B on the health positioning map as shown in FIG. 1B, the user U can recognize that their health level is neither bad nor good. This can, for example, encourage the user U to make an effort to improve their health. For example, if the user U's health level falls within the region characterized as the young person/mental health disorder risk group on the health positioning map, the user U can recognize their health status as being at risk for mental health disorders. This can, for example, encourage the user U to take measures to prepare for mental health disorder risks.

Steps S1 to S5, from taking measurements to displaying the health positioning map, may be performed by the user's conscious operation, or may be performed automatically without the user's awareness. In other words, by taking various measurements, user U can easily recognize his or her position on the health positioning map. Alternatively, all of steps S1 to S5 can be performed automatically, even by performing measurements automatically. This allows user U to recognize his or her position on the health positioning map without the burden of taking measurements.

Because the data obtained by the measurement in step S1 does not require time to analyze, the time from when the data is imported into the computer device 30 in step S2 to when the health positioning map is displayed in step S5 can be shortened. This allows the user to immediately know their own health status. In particular, by continuously performing the measurement in step S1 using a wearable measuring device, for example, the user can visually recognize changes in their own health status in real time. Here, the time from when the data is imported into the computer device 30 in step S2 to when the health positioning map is displayed in step S5 can be, for example, 0.01 seconds, 0.02 seconds, 0.05 seconds, 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second or less, 3 seconds or less, 5 seconds or less, 10 seconds or less, 20 seconds or less, 30 seconds or less, 45 seconds or less, 1 minute or less, 5 minutes or less, 10 minutes or less, 1 hour or less, 2 hours or less, 12 hours or less, 24 hours or less, etc. Such good responsiveness reduces the waiting time of the user and reduces the psychological burden on the user regarding measurement, thereby preventing a decrease in the user's motivation to take measurements.

Furthermore, by repeating steps S1 to S5 described above after a predetermined period of time has passed, user U can recognize changes in his or her health condition over time. For example, user U can recognize the direction his or her health condition is heading by referring to the time-series changes in his or her position on the health positioning map. For example, if user U's health level was in an area characterized as health level B on the health positioning map as shown in FIG. 1B, and after a predetermined period of time, user U's health level is still in the area characterized as health level B on the health positioning map but is approaching the area characterized as health level C, user U can recognize that his or her health condition is heading toward health level C. This can, for example, encourage user U to make an effort to prevent his or her health level from worsening. For example, if a user's health level falls within the region characterized as the young-age/mental health disease risk group on the health positioning map, and after a predetermined period of time, the user's health level falls within the region characterized as the young-age/mental health disease risk group on the health positioning map but approaches the region characterized as the middle-aged/elderly/lifestyle-related disease risk group, the user U may recognize that his or her health condition is moving toward the middle-aged/elderly/lifestyle-related disease risk group. This may, for example, prompt the user U to take measures to prepare for the risk of lifestyle-related diseases.

For example, by performing steps S1 to S5 described above before an event, user U can recognize his or her current health level. Then, by performing steps S1 to S5 described above after the event, user U can recognize his or her current health level. By comparing the pre-event health level obtained in this way with the post-event health level, user U can recognize the impact of the event on his or her health level. Events include, but are not limited to, infectious disease outbreaks (such as COVID-19), natural disasters (such as damage caused by storms, heavy rain, heavy snowfall, floods, storm surges, earthquakes, tsunamis, volcanic eruptions, and other abnormal natural phenomena), overtime work, experiences (direct experiences or simulated experiences (such as experiences via VR (virtual reality) or AR (augmented reality)), communication with other people or animals, etc.).

For example, by performing steps S1 to S5 described above at the beginning of the day (e.g., between 6:00 AM and midnight), user U can recognize his or her current health level. Then, by performing steps S1 to S5 described above at the end of the day (e.g., between 6:00 PM and midnight), user U can recognize his or her current health level. By comparing the health level obtained at the beginning of the day with the health level at the end of the day, user U can recognize the daily fluctuations in health level. If such health level is primarily expressed as fatigue level, for example, user U can objectively recognize the degree of fatigue accumulated as a result of the day's activities.

For example, by performing steps S1 to S5 described above at the end of one day (e.g., between 6:00 PM and midnight), user U can recognize his or her current health level. Then, by performing steps S1 to S5 described above at the beginning of the next day (e.g., between 6:00 AM and midnight), user U can recognize his or her current health level. By comparing the health level obtained at the end of one day with the health level at the beginning of the next day, user U can recognize overnight fluctuations in health level. Nighttime fluctuations in health level may correspond to the body's resilience or elasticity, and user U can objectively recognize the extent to which he or she has recovered through a night's sleep.

The above-described steps S1 to S5 can be performed at predetermined times throughout the day. The predetermined times may be, for example, a predetermined time during the day (e.g., between 6:00 AM and 8:00 AM, between 11:00 AM and 1:00 PM, between 4:00 PM and 6:00 PM, between 9:00 PM and midnight, or 6:00 AM, 12:00 PM, 6:00 PM, or midnight, etc.), a predetermined interval (e.g., every hour, every three hours, every six hours, etc.), or a time freely set by the user. For example, by performing steps S1 to S5 in the morning (e.g., between 6:00 AM and 8:00 AM, or 6:00 AM, etc.), midday (e.g., between 11:00 AM and 1:00 PM, or midnight, etc.), evening (e.g., between 4:00 PM and 6:00 PM, or 6:00 PM, etc.), or night (e.g., between 9:00 PM and midnight, or midnight, etc.), fluctuations in health status due to daily activities can be closely tracked.

By assessing and accumulating such short-term health status, it is possible to capture minute fluctuations in health status and evaluate health from a new perspective. Health status can be assessed through simple tests at home, making such short-term health assessments easy to perform. This can also be used to develop individualized solutions for improving daily habits, etc.

While the above examples illustrate four simple non-invasive tests that can be performed at home, measurements are not limited to non-invasive tests. For example, measurements may include invasive tests. In this specification, "invasive tests" refer to tests that injure the subject's body (e.g., by drawing blood via injection or excising tissue), while "non-invasive tests" refer to tests that do not injure the subject's body in any way. Typical invasive tests are tests that detect the amount of components contained in blood or plasma, while typical non-invasive tests include tests that detect components in the subject's excretions (urine, breath, saliva), autonomic nervous function tests, cognitive function tests, questionnaires, and VAS (Visual Analogue Scale). In this specification, "invasive parameters" refer to parameters obtained by invasive tests, and "non-invasive parameters" refer to parameters obtained by non-invasive tests.

In the above example, the measurement is performed at home H, but the location of the measurement is not limited to home H. For example, the measurement may be performed at a company, pharmacy, community center, cafe, etc. other than a testing facility. Because simple measurements are sufficient, measurements can be performed in locations other than testing facilities. Of course, measurements may also be performed at a hospital or specialized testing facility.

Figure 1B shows an example of a screen 1000 showing the user's health status. Screen 1000 may be provided to the user, for example, by being displayed on the display screen of user U's computer device 30 (e.g., a personal computer, smartphone, tablet, etc.).

Screen 1000 includes a health positioning map display section 1100 and a radar chart display section 1200.

The health positioning map display unit 1100 displays a health positioning map. In this health positioning map, the horizontal axis is associated with physical health, with larger values on the horizontal axis indicating poorer physical health, while the vertical axis is associated with mental health, with larger values on the vertical axis indicating poorer mental health.

The health positioning map displayed on the health positioning map display unit 1100 includes 10 regions, of which one region is characterized as health level A, three regions are characterized as post-health level B, and six regions are characterized as health level C. Here, health level A indicates good health, health level B indicates average health, and health level C indicates poor health and requires attention.

Users can assess their own health status based on the area on the health positioning map where their health level is located. In the example shown in Figure 1B, the user's health level is plotted with a star, indicating that the user's health level falls within the area characterized as health level B. For example, by plotting the health levels of multiple users belonging to a certain group on a single health positioning map, it is possible to determine the level of health of a particular user relative to that group. This is useful, for example, in corporate labor management. For example, a corporate labor manager can view a health positioning map on which the health levels of the company's employees are plotted and consider whether organizational intervention is necessary or possible. For example, if the health level of a specific employee is plotted in a worse position than that of other employees, the manager can consider whether to intervene for that employee. Or, for example, if the health levels of employees in a specific department are plotted in a worse position than those of employees in other departments, the manager can consider whether to intervene for that department.

The radar chart display unit 1200 displays a radar chart. This radar chart indicates health status on a scale of 0 to 5 based on six perspectives (musculoskeletal musculoskeletal system, metabolism/metabolic syndrome, autonomic nervous system, sleep-wake rhythm, mental health, and fatigue). The musculoskeletal musculoskeletal system perspective indicates muscle strength and other conditions related to exercise. The metabolism/metabolic syndrome perspective indicates conditions such as internal energy metabolism and obesity. The autonomic nervous system perspective indicates the state of nervous regulation related to concentration and relaxation. The sleep-wake rhythm perspective indicates conditions such as sleep and drowsiness. The mental health perspective indicates conditions such as depression. The fatigue perspective indicates mental and physical fatigue. Users can instantly recognize which perspective is contributing to their health status. The radar chart displayed on the radar chart display unit 1200 features health-related information on each axis, and the user's score is mapped to each axis. Therefore, in this specification, such radar charts can also be considered a type of health positioning map.

The screen 1000 may include a health ranking display section (not shown). The health ranking display section displays the user's health ranking within a specific group. The specific group may include, for example, a group of people with the same last name as the user, a group of people of the same age as the user, or a group of people of the same age and last name as the user. Such a ranking display makes it easier to intuitively understand one's own health level. Furthermore, by displaying the direction of what should be done to improve the ranking, it is possible to encourage users to change their behavior using the principle of competition.

The above-mentioned services can be realized, for example, by computer system 10, described below.

2. Computer System Configuration FIG. 2 shows an example of the configuration of the computer system 10.

The computer system 10 comprises a server device 100 and at least one terminal device 300.

The server device 100 is connected to the database unit 200. The server device 100 is also connected to at least one terminal device 300 via a network 400. The server device 100 is, for example, a computer installed at a service provider that provides a new service for visualizing a user's health status.

Network 400 may be any type of network. Network 400 may be, for example, the Internet or a LAN. Network 400 may be a wired network or a wireless network.

Although three terminal devices 300 are depicted in Figure 2, the number of terminal devices 300 is not limited to this. The number of terminal devices 300 can be any number equal to or greater than one. An example of a terminal device 300 is a computer held by a user, but is not limited to this. For example, it may be a computer installed in a hospital, a computer installed in a room in an office where examinations can be performed, etc. Here, the computer may be any type of computer. For example, the terminal device 300 may be any type of terminal device, such as a smartphone, tablet, personal computer, smart glasses, etc.

The server device 100 comprises an interface unit 110, a processor unit 120, and a memory unit 150.

The interface unit 110 exchanges information with the outside of the server device 100. The processor unit 120 of the server device 100 can receive information from the outside of the server device 100 and can send information to the outside of the server device 100 via the interface unit 110. The interface unit 110 can exchange information in any format.

The interface unit 110, for example, includes an input unit that enables information to be input to the server device 100. It does not matter in what manner the input unit enables information to be input to the server device 100. The interface unit 110, for example, includes an output unit that enables information to be output from the server device 100. It does not matter in what manner the output unit enables information to be output from the server device 100.

The processor unit 120 executes the processing of the server device 100 and controls the operation of the entire server device 100. The processor unit 120 reads and executes programs stored in the memory unit 150. This allows the server device 100 to function as a system that executes desired steps. The processor unit 120 may be implemented by a single processor or by multiple processors.

The memory unit 150 stores programs required to execute the processes of the server device 100, data required to execute those programs, and the like. The memory unit 150 may store programs for causing the processor unit 120 to perform processing to create a health level positioning map (e.g., a program that implements the processing shown in Figures 5A and 5B, described below), programs for causing the processor unit 130 to perform processing to create a health function (e.g., a program that implements the processing shown in Figure 6, described below), and programs for causing the processor unit 140 to perform processing to estimate the user's health level (e.g., a program that implements the processing shown in Figures 7A and 7B, described below). Here, the manner in which the programs are stored in the memory unit 150 is not important. For example, the programs may be pre-installed in the memory unit 150. Alternatively, the programs may be installed in the memory unit 150 by being downloaded via a network. In this case, the type of network is not important. The memory unit 150 may be implemented by any storage means.

Database unit 200 may store, for example, data obtained from multiple subjects. Furthermore, database unit 200 may store, for example, health level positioning map data generated by server device 100. Furthermore, database unit 200 may store, for example, health functions generated by server device 100. Furthermore, database unit 200 may store health level information indicating the user's health level derived by server device 100. When the user's consent is obtained, the health level information indicating the user's health level is stored in database unit 200, and can be made available for research use by other users.

Figure 3A shows an example of the configuration of the processor unit 120 in one embodiment. The processor unit 120 may have a configuration for processing to create a health positioning map.

The processor unit 120 comprises an acquisition means 121, a processing means 122, a mapping means 123, a clustering means 124, and a characterization means 125.

The acquisition means 121 is configured to acquire a first dataset for a first parameter set, described below, for each of a plurality of subjects. For example, the acquisition means 121 acquires a dataset of multiple items (e.g., 232 items in one embodiment) for each subject. The first parameter set may be, for example, an extracted parameter set obtained by acquiring a dataset of an initial parameter set, calculating the correlation between each piece of data in the dataset of the initial parameter set, and extracting parameters whose correlation coefficient is equal to or greater than a predetermined threshold. In this case, the extracted parameter set may be extracted to include the four basic parameters, described below. The first parameter set may be, for example, an extracted parameter set obtained by acquiring a dataset of an initial parameter set and extracting, from the dataset of the initial parameter set, parameter sets that have a high influence on the health positioning map using machine learning.

The acquisition means 121 can, for example, receive data about multiple subjects stored in the database unit 200 via the interface unit 110 and acquire the received data. The acquisition means 121 can, for example, receive data about multiple subjects stored in the database unit 200 from a computer system of an examination facility (e.g., a hospital, research institute, etc.) via the interface unit 110 and acquire the received data. The acquired first data set is passed to the processing means 122 for subsequent processing.

The database unit 200 may store a first data set for a first parameter set.

Figure 4 shows an example of the data structure of the first dataset stored in the database unit 200.

The database unit 200 stores a first data set for a first parameter set for each of a plurality of subjects. Each of the plurality of subjects is assigned an ID. For example, the database unit 200 stores a set of values (first data set) for each parameter of the first parameter set, such as age, muscle mass, BMI, fat percentage, ultrasonic conduction velocity, osteoporosis index, etc.

3A again, in one embodiment, the acquisition means 121 may be further configured to acquire, as sub-first data, first data included in a portion of the multiple regions included in the created health positioning map. Alternatively, the acquisition means 121 may be configured to acquire, as sub-first data, first data included in a portion of the multiple regions identified by the clustering means 124 described below. The acquisition means 121 may, for example, acquire first data within a region selected by the health positioning map creator. The health positioning map creator can input the region selection into the server device 100 via the interface unit 110. For example, in order to create a health positioning map focusing on a specific group of multiple subjects, such as a male subject group, a female subject group, a young group (a group under 40 years old), a middle-aged group (a group aged 40 to 60 years old), or an elderly group (a group aged 60 years old or older), the health positioning map creator can select a region to which the group may belong. The obtained sub-first data may be passed to the clustering means 124 for further processing.

The processing means 122 is configured to process the first data set acquired by the acquisition means 121. The processing means 122 is able to output first data by processing the first data set.

The processing by the processing means 122 may include any processing as long as it is possible to map the output first data. When creating a positioning map based on mixed gender data, it is preferable to correct for gender differences.

The processing by the processing means 122 may include, for example, dimensionality reduction. Dimensionality reduction is a process of converting m-dimensional data into n-dimensional data, where m>n. Dimensionality reduction may be performed using, for example, multidimensional scaling (MDS), principal component analysis, multiple regression analysis, principal component analysis, or machine learning, but the means for dimensionality reduction are not limited to these. The dimensionality reduction preferably reduces the first data set to two-dimensional or three-dimensional data. This is because mapping the two-dimensional or three-dimensional data using the mapping means 123 described below results in a map of two-dimensional or three-dimensional space, making it possible to obtain a map that is visually easy to understand. Dimensionality reduction may be performed using multidimensional scaling. This is because mapping the first data obtained using multidimensional scaling using the mapping means 123 described below makes it possible to obtain a map that is visually easy to understand.

The processing by the processing means 122 may include, for example, standardization processing. Standardization processing is processing to align the scale of data for each parameter in the first dataset. Standardization processing may be, for example, processing to calculate a Z-score (processing to correct data so that the mean is 0 and the standard deviation is 1), processing to calculate a T-score (processing to correct data so that the mean is 50 and the standard deviation is 10), etc. The processing means 122 may perform standardization processing of the data in the first dataset for all parameters in the first parameter set, or may perform standardization processing of the data in the first dataset for specific parameters.

The processing means 122 may perform standardization processing on the first data set for the entire plurality of subjects, or on the first data set for a specific population of the plurality of subjects. Specific populations of the plurality of subjects include, but are not limited to, a male subject population, a female subject population, a young population (a population under 40 years old), a middle-aged population (a population between 40 and 60 years old), an elderly population (a population over 60 years old), etc. The processing means 122 can form any population from the plurality of subjects and perform standardization processing on the first data set for that population.

For example, the processing means 122 can classify first data sets from multiple subjects into data sets for male subjects and data sets for female subjects, and standardize the data sets for male subjects to perform standardization processing for the male subject population, or can standardize the data sets for female subjects to perform standardization processing for the female subject population, or can perform both. In this way, performing standardization processing for the male subject population and/or the female subject population is preferable for parameters in the first parameter set that differ between men and women (e.g., blood triglyceride concentration, etc.), and is more preferable for parameters in the first parameter set that differ significantly between men and women (e.g., blood red blood cell count, etc.). This eliminates gender differences, making it possible to create a health positioning map that is not biased by gender differences.

The processing by the processing means 122 may include, for example, weighting processing. Weighting processing is processing in which at least some of the data in the first data set is weighted. For example, at least some of the data in the first data set may be weighted by adding a predetermined number to the data, or at least some of the data in the first data set may be weighted by multiplying the data by a predetermined number. The predetermined number to be added or multiplied may be constant for each piece of data to be weighted, or may be different. For example, the predetermined number may be varied so that a larger or smaller weight is assigned to data that has a greater impact on the health function derived by the derivation means 133, which will be described later. Alternatively, the predetermined number may be varied so that a larger or smaller weight is assigned to data that has a smaller impact on the health function derived by the derivation means 133, which will be described later.

The processing means 122 may perform weighting processing on the first data set for the entire group of subjects, or on the first data set for a specific population of the subjects. The processing means 122 can form an arbitrary population from the multiple subjects and perform weighting processing on the first data set of that population. The population on which weighting processing is performed may be the same as or different from the population on which the above-mentioned standardization processing is performed.

The mapping means 123 is configured to map the first data output by the processing means 122 for each of the multiple subjects. Mapping by the mapping means 123 is a process of associating n-dimensional first data with positions in n-dimensional space. By mapping the first data, the mapping means 123 can output a map in which the first data for each of the multiple subjects is mapped. For example, if the first data is two-dimensional, the mapping means 123 can output a two-dimensional map by mapping the first data so that the first data is associated with positions in two-dimensional space, i.e., on a plane. Figure 9A is a diagram showing an example of the mapping result by the mapping means 123. As shown in Figure 9A, the mapping means 123 outputs a map by plotting points defined by the first data obtained by the processing means 122 for each of the multiple subjects in two-dimensional space (multidimensional space). For example, the mapping means 123 may output a map (radar chart) in which the first data for each of the multiple subjects is mapped by mapping the n-dimensional first data on a radar chart with n axes.

The clustering means 124 is configured to cluster the first data mapped by the mapping means 123. Clustering by the clustering means 124 is a process of dividing the mapped first data into multiple clusters and identifying the regions to which each of the multiple clusters belongs. As used herein, "region" refers to a range in n-dimensional space and has n-dimensional extent. The clustering means 124 can divide the mapped first data into any number of clusters. For example, it is preferable that the clustering means 124 divide the mapped first data into at least three clusters. This is because using three clusters (e.g., clusters for good health, average health, and poor health, as shown in Figure 1B) enables the creation of a health positioning map that is intuitively understandable to the user. The number of clusters into which the mapped first data is divided may depend on the number N of multiple subjects. In the example shown in Figure 9A, the clustering means 124 classifies the first data mapped by the mapping means 123 into four clusters. The clustering means 124 can cluster the data using any known method. For example, the clustering means 124 can divide the data into multiple clusters using a non-hierarchical clustering method (e.g., k-means, k-means++, PAM, etc.). The clustering means 124 can preferably divide the data into multiple clusters using k-means. This is because the results of clustering using k-means better reflect the trends of the subject population and contain richer information than the results of clustering using other methods. The clustering means 124 can, for example, identify multiple regions by defining boundaries that separate each of the multiple clusters. Defining the boundaries can be performed using any known process. For example, in the radar chart example described above, the clustering means 124 can simply separate the values of each axis from the values of the other axes and identify the multiple axes as multiple regions.

In one embodiment, the clustering means 124 may further be configured to cluster the sub-first data acquired by the acquisition means 121. The clustering means 124 may divide the sub-first data into a plurality of clusters and identify regions to which the plurality of clusters belong. The clustering means 124 may divide the sub-first data into any number of clusters.

The characterization means 125 is configured to characterize at least a portion of the multiple regions identified by the clustering means 124. The characterization means 125 may characterize at least a portion of the multiple regions based on information input to the server device 100 by the health positioning map creator via the interface unit 110. For example, the health positioning map creator may analyze the characteristics of the subjects corresponding to the first data included in each of the multiple regions and input information to characterize the region based on the analysis results. Alternatively, the characterization means 125 may characterize at least a portion of the multiple regions without relying on input by the health positioning map creator. For example, the characterization means 125 may characterize at least a portion of the multiple regions based on their relative positions in the health positioning map, or may characterize at least a portion of the multiple regions based on machine learning.

In this way, a health positioning map is created in which at least some of the multiple regions are characterized. In one embodiment, the health positioning map in which some of the multiple regions identified by clustering the sub-first data are characterized becomes a health positioning map for some of the multiple subjects.

The health level positioning map created by the processor unit 120 is output to the outside of the server device 100, for example, via the interface unit 110. The health level positioning map may be transmitted to the database unit 200 via the interface unit 110 and stored in the database unit 200. Alternatively, it may be transmitted to the processor unit 130, described below, for creating a health function. As described below, the processor unit 130 may be a component of the same server device 100 as the processor unit 120, or may be a component of a different computer system.

Figure 3B shows an example of the configuration of the processor unit 130 in another embodiment. The processor unit 130 may have a configuration for processing to create a health function for mapping the subject's health level on a health level positioning map. The processor unit 130 may be a processor unit provided in the server device 100 as an alternative to the above-mentioned processor unit 120, or may be a processor unit provided in the server device 100 in addition to the processor unit 120. When the processor unit 130 is a processor unit provided in the server device 100 in addition to the processor unit 120, the processor unit 120 and the processor unit 130 may be implemented by the same processor or by different processors.

The processor unit 130 comprises a first acquisition means 131, a second acquisition means 132, and a derivation means 133.

The first acquisition means 131 is configured to acquire a health level positioning map. The acquired health level positioning map may be a health level positioning map created by the processor unit 120 described above, or a health level positioning map created in a different manner, as long as it is created using the first parameter set. The acquired health level positioning map is passed to the derivation means 133 for subsequent processing.

The second acquisition means 132 is configured to acquire a second data set for a second parameter set, described below, for at least a portion of the plurality of subjects. In this specification, the second parameter set is also simply referred to as a "parameter set." The second parameter set is part of the first parameter set. The second parameter set may include parameters not included in the first parameter set. The second acquisition means 132 can, for example, acquire data for a portion of the plurality of subjects stored in the database unit 200 via the interface unit 110. The acquired second data set is passed to the derivation means 133 for subsequent processing.

The derivation means 133 is configured to derive a health function that correlates the second data set acquired by the second acquisition means 132 with a position on the health positioning map acquired by the first acquisition means 131. The derivation means 133 can derive the health function using, for example, machine learning, decision tree analysis, random forest regression, multiple regression analysis, principal component analysis, etc. The health function can be derived, for example, for each axis of the n-dimensional health positioning map. For example, if the health positioning map is two-dimensional, it can derive a health function X that correlates the second data set with the X coordinate on the health positioning map, and a health function Y that correlates the second data set with the Y coordinate on the health positioning map. The derivation means 133 may, for example, arbitrarily increase or decrease the number of variables in the health function to create multiple health functions with identification accuracy, i.e., a group of health functions (hereinafter also referred to as a group of multiple-pattern health functions). For example, the derivation means 133 may create (1) a health function whose variables are data on blood test items and data on other items, and (2) a health function whose variables are only data on blood test items, both of which have similar accuracy. Furthermore, the derivation means 133 may create a group of health functions whose variables are data selected from a data group on items other than data on blood test items, as a group of multiple pattern health functions.

The health function may be, for example, a regression model. The regression model may be a linear regression model or a nonlinear regression model. The derivation means 133 can derive each coefficient of the regression model by performing machine learning for at least some of the multiple subjects, using the second dataset as an independent variable and the subject's coordinates on the health positioning map as dependent variables. When the second dataset obtained from the subject is input as the independent variable of such a machine-learned regression model, the subject's coordinates on the health positioning map are output. The output coordinates can be used to map the subject's health on the health positioning map.

The health function may be, for example, a neural network model. The neural network model has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network model corresponds to the number of dimensions of the input data. In other words, the number of nodes in the input corresponds to the number of parameters in the second parameter set. The hidden layer of the neural network model may include any number of nodes. The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of the output data. In other words, if the neural network model outputs an X coordinate on a health positioning map, the number of nodes in the output layer is 1. For example, if the neural network model outputs n coordinates on an n-dimensional health positioning map, the number of nodes in the output layer is n. The derivation means 133 can derive the weight coefficients of each node for at least some of the subjects by machine learning using the second dataset as input training data and the subject's position on the health positioning map as output training data.

For example, a set of (input training data, output training data) for machine learning could be (second dataset for second parameter set for first subject, coordinates on the first subject's health positioning map), (second dataset for second parameter set for second subject, coordinates on the second subject's health positioning map), ... (second dataset for second parameter set for i-th subject, coordinates on the i-th subject's health positioning map), ... etc. When a second dataset obtained from a subject is input to the input layer of such a machine-learned neural network model, the coordinates on that subject's health positioning map are output to the output layer. The output coordinates can be used to map the subject's health onto the health positioning map.

The health function created by the processor unit 130 is output to the outside of the server device 100, for example, via the interface unit 110. The health function may be transmitted to the database unit 200 via the interface unit 110 and stored in the database unit 200. Alternatively, it may be transmitted to the processor unit 140, described below, for processing to estimate the user's health level. As described below, the processor unit 140 may be a component of the same server device 100 as the processor unit 130, or may be a component of a different computer system.

Figure 3C shows an example of the configuration of processor unit 140 in yet another embodiment. Processor unit 140 may have a configuration for processing to estimate a user's health level. In processing by processor unit 140, the user's health level can be estimated by estimating which area on a health level positioning map the user's health level is located in. Processor unit 140 may be a processor unit included in server device 100 as an alternative to processor unit 120 and processor unit 130 described above, or may be a processor unit included in server device 100 in addition to processor unit 120 and/or processor unit 130 described above. When processor unit 140 is a processor unit included in server device 100 in addition to processor unit 120 and/or processor unit 130, processor unit 120, processor unit 130, and processor unit 140 may all be implemented by the same processor, all may be implemented by different processors, or two of processor unit 120, processor unit 130, and processor unit 140 may be implemented by the same processor.

The processor unit 140 includes a third acquisition means 141, a fourth acquisition means 142, an output generation means 143, and an output mapping means 144.

The third acquisition means 141 is configured to acquire a health function. The health function is a function that correlates a data set for the above-mentioned second parameter set with a position on the health positioning map. The acquired health function may be a health function created by the above-mentioned processor unit 130 or a health function created in a different way, as long as it can correlate the user data set with a position on the health positioning map. The health positioning map may be a health positioning map created by the above-mentioned processor unit 120 or a health positioning map created in a different way, as long as it is created using the first parameter set. The acquired health function is passed to the output generation means 143 for subsequent processing.

The fourth acquisition means 142 is configured to acquire a user data set for the user's second parameter set. The fourth acquisition means 142 can, for example, acquire the user data set stored in the database unit 200 via the interface unit 110. Alternatively, the fourth acquisition means 142 can, for example, acquire the user data set from the terminal device 300 via the interface unit 110. The acquired user data set is passed to the output generation means 143 for subsequent processing.

The output generation means 143 is configured to generate an output from the health function. The output generation means 143 generates an output from the health function by inputting the user data set acquired by the fourth acquisition means 142 into the health function acquired by the third acquisition means 141. This output is referred to in this specification as "health level information." The health level information includes coordinates on the health level positioning map (e.g., X-axis value, Y-axis value).

For example, if the health function is a regression model as described above, by inputting the user data set into the independent variables of the regression model, coordinates on the health positioning map are output.

For example, if the health function is a neural network model as described above, inputting the user data set into the input layer of the neural network model outputs coordinates on a health positioning map.

The output mapping means 144 is configured to map the output generated by the output generation means 143 onto the health positioning map. Because the output generated by the output generation means 143 is a coordinate, the output mapping means 144 can map the coordinate within the n-dimensional space of the health positioning map.

The output mapped on the health positioning map by the processor unit 140 is output to the outside of the server device 100, for example, via the interface unit 110. The output can be transmitted to the terminal device 300, for example, via the interface unit 110.

In the above example, it has been explained that the processor unit 140 is equipped with output mapping means 144, but the processor unit 140 does not have to be equipped with output mapping means 144. Instead of the processor unit 140 being equipped with output mapping means 144, the terminal device 300 may have the function of expressing health level information as a position on a health level positioning map. In this case, the output by the output generation means 143, i.e., the health level information, is transmitted to the terminal device 300 via the interface unit 110.

Note that each component of the server device 100 described above may be composed of a single hardware component, or may be composed of multiple hardware components. If composed of multiple hardware components, the manner in which the hardware components are connected does not matter. The hardware components may be connected wirelessly or by wire. The server device 100 of the present invention is not limited to a specific hardware configuration. It is also within the scope of the present invention for the processor units 120, 130, and 140 to be configured using analog circuits rather than digital circuits. The configuration of the server device 100 of the present invention is not limited to that described above, as long as it is possible to realize its functions.

Figure 3D is a diagram showing an example of data flow 1 by server device 100 in one embodiment. As shown in Figure 3D, data flow 1 includes data acquisition step 10, data processing step 20, health assessment map creation step 30, clustering map creation step 40, health function value calculation step 50, positioning map creation step 60, and output step 70. For example, data acquisition step 10, data processing step 20, health assessment map creation step 30, and clustering map creation step 40 have the function of a health positioning map creation device and can be implemented by server device 100 equipped with, for example, the processor unit 120 described above. Furthermore, data acquisition step 10 and health function value calculation step 50 have the function of a health function value calculation device and can be implemented by server device 100 equipped with, for example, the processor unit 140 described above. In the output step 70, the data generated in the data processing step 20, the health assessment map creation step 30, the clustering map creation step 40, the health function value calculation step 50, or the positioning map creation step 60 is output, for example, by a display device (e.g., a liquid crystal display).

The data set acquired in the data acquisition step 10 is sent to the data processing step 20.

The data processing step 20 performs, for example, correction 21 and dimensionality reduction 22, as shown in FIG. 3D. The data processing step 20 can be implemented, for example, by the processing means 122 of the processor unit 120 described above.

Correction 21 corrects the data acquired in data acquisition step 10. Specifically, correction 21 corrects each piece of data acquired in data acquisition step 10 so that the values of the data acquired in data acquisition step 10 fall within a predetermined range (for example, the mean value is 0 and the standard deviation is 1). The corrected data is sent to dimensionality reduction 22.

The dimension reduction step 22 reduces the dimensions of the multiple data passed from the data acquisition step 10 or the correction step 21. Specifically, the dimension reduction step 22 reduces the dimensions of the multiple data (multidimensional data) passed from the data acquisition step 10 or the correction step 21 to an arbitrary dimension (two dimensions in this embodiment) using multiple regression analysis, multidimensional scaling, principal component analysis, or machine learning. The dimension-reduced data may be in the form of a function, for example. For example, this function is a function for calculating health-related indicators. For example, the function is a function that uses some or all of the data included in the first data as variables and is created by assigning greater weight to data that has a particularly large impact among various disease factors. In this embodiment, the function is created using a linear or nonlinear model for some or all of the data included in the first data. In one embodiment, a two-dimensional function (in this embodiment, composed of a horizontal axis function X (hereinafter referred to as the first function) and a vertical axis function Y (hereinafter referred to as the second function)) is created. The created function is passed to the health assessment map creation step 30 and the output step 70 .

In this embodiment, as described above, dimensionality reduction 22 creates a two-dimensional function by reducing multidimensional data to two dimensions. The first and second functions are functions whose variables are all or part of the multiple data passed from data acquisition step 10 or correction step 21, and are functions calculated by multiple regression analysis, multidimensional scaling, principal component analysis, or machine learning. In this invention, "machine learning" means either machine learning including deep learning or machine learning not including deep learning. The variables constituting the first and second functions may be completely identical or completely different, and some of the variables may overlap.

The health assessment map creation step 30 may be implemented, for example, by the mapping means 123 of the processor unit 120 described above. In the health assessment map creation step 30, a map for assessing health (hereinafter referred to as a health assessment map) is created using, for example, the data processed in the data processing step 20, more specifically, a function created by dimensionality reduction. In this embodiment, since the function is two-dimensional, the health assessment map is a two-dimensional map. Figure 9A is a diagram showing an example of a health assessment map. As shown in Figure 9A, in the health assessment map creation step 30, a health assessment map is created by plotting points determined by the first function and the second function for multiple subjects in a two-dimensional space (multidimensional space).

The clustering map creation step 40 may be implemented, for example, by the clustering means 124 and characterization means 125 of the processor unit 120 described above. In the clustering map creation step 40, for example, the points plotted on the health assessment map created in the health assessment map creation step 30 are clustered into several clusters, and a map characterizing the multiple regions (hereinafter referred to as a health positioning map) is created. In this embodiment, the clustering map creation step 40 uses a non-hierarchical clustering method (in this embodiment, k-means) to cluster the plotted points into any number of clusters. In the example shown in Figure 9A, the points are classified into four clusters. A health positioning map is created by characterizing at least one of these four regions with health-related information.

The created health level positioning map may be output in the output step 70, passed to the health function value calculation step 50, or passed to the health prediction positioning map creation step 60.

The health function value calculation step 50 and the health prediction positioning map creation step 60 can be implemented, for example, by the processor unit 130 and processor unit 140 described above. In the health function value calculation step 50, a health function is first created based on the health level positioning map. Note that the number of variables in the health function may be increased or decreased arbitrarily to create multiple health functions with identification accuracy, i.e., a group of health functions. Next, the value of the health function for a subject other than the subject from whom the first data set was acquired to create the health function is calculated. Specifically, the created health function is applied to the data (newly acquired data) of the target subject acquired in the data acquisition step 10 to calculate the value of the health function for that subject. Note that since the health function is a multidimensional function, the health function value is naturally also a multidimensional value.

The health prediction positioning map creation step 60 plots the subject's health function value calculated in the health function value calculation step 50 on the health assessment map created in the health assessment map creation step 30 or the health positioning map created in the clustering map creation step 40 to create a health prediction positioning map. This makes it possible to predict the health state of a subject for whom new data has been measured.

In addition, the health prediction positioning map creation step 60 may involve plotting the health function values calculated in the health function value calculation step 50 using data acquired at time intervals for a single subject on the health assessment map or the health degree positioning map to create a health prediction positioning map. This allows the degree of change in the subject's health condition to be evaluated.

In addition, data obtained from new subjects can also be used as data for updating the health function. In the health function value calculation step 50, the health function can be updated by further using data obtained from new subjects or data output from the correction step 21 using that data. This makes it possible to evaluate a wider variety of health risks and improve the accuracy of health risk assessment.

Each step of the data flow 1 (particularly, the data processing step 20, the health assessment map creation step 30, the clustering map creation step 40, the health function value calculation step 50, and the health predictive positioning map creation step 60) may be implemented by a logic circuit (hardware) formed on an integrated circuit (IC chip) or the like, or by software. In the latter case, the health assessment device 1 includes a computer that executes instructions of a software program that realizes each function. This computer includes, for example, one or more processors and a computer-readable recording medium storing the program. The object of the present invention is achieved when the processor in the computer reads and executes the program from the recording medium. The processor may be, for example, a CPU (Central Processing Unit). The recording medium may be a "non-transitory tangible medium," such as a ROM (Read Only Memory), a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like. The device may also include a RAM (Random Access Memory) for expanding the program. The program may be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast waves). Note that one aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

The above data flow allows for the creation of a health positioning map and/or health function using various data. In other words, it is possible to create a health positioning map and/or health function that can comprehensively calculate a subject's health, rather than existing health indicators related to individual diseases. Therefore, it is possible to create a health positioning map and/or health function that can evaluate various health risks (i.e., that can evaluate the subject's overall health).

In addition, health positioning maps and/or health functions can be flexibly created by combining various data measured in data acquisition step 10. In other words, there is a great deal of flexibility in selecting the items to be measured in data acquisition step 10.

In one embodiment, a health function is created using machine learning with the second dataset as input data. This allows for the creation of a health function that can more accurately produce an index of the subject's health status.

In one embodiment, before creating a health positioning map using the first data set, data is corrected between genders. This allows for the creation of a health positioning map that can be used for both genders.

In one aspect of the present invention, the first data may include only data acquired by non-invasive measurement. According to the above configuration, data can be acquired without injuring the subject, such as through a blood test.

In one embodiment, a map for assessing health can be created using the function created by the data processing step 20. Specifically, when the function is a multidimensional vector, the map for assessing health is created by plotting the points defined by the function for multiple subjects in multidimensional space. This allows the health status of the subjects to be visually confirmed.

In one embodiment, the points plotted in multidimensional space are clustered. Then, by referencing the data of subjects belonging to each cluster, it is possible to identify the type of subject group that each cluster represents and characterize those clusters. As a result, by plotting the data of newly measured subjects on a health positioning map, it becomes possible to predict the health status of the measured subjects.

Figure 3E shows an example configuration of terminal device 300.

The terminal device 300 comprises an interface unit 310, a camera 320, a display unit 330, a memory unit 340, and a processor unit 350.

The interface unit 330 exchanges information with the outside of the terminal device 300. The processor unit 320 of the terminal device 300 is capable of receiving information from the outside of the terminal device 300 and transmitting information to the outside of the terminal device 300 via the interface unit 310. The interface unit 310 can exchange information in any format.

The interface unit 310 includes, for example, an input unit that enables information to be input to the terminal device 300. It does not matter how the input unit enables information to be input to the terminal device 300. For example, if the input unit is a touch panel, the user may input information by touching the touch panel. Alternatively, if the input unit is a mouse, the user may input information by operating the mouse. Alternatively, if the input unit is a keyboard, the user may input information by pressing keys on the keyboard. Alternatively, if the input unit is a microphone, the user may input information by speaking into the microphone. Alternatively, if the input unit is a camera, the user may input information captured by the camera. Alternatively, if the input unit is a data reading device, the information may be input by reading information from a storage medium connected to the terminal device 300. Alternatively, if the input unit is a receiver, the receiver may input information from outside the terminal device 300 via the network 400.

The interface unit 310 includes, for example, an output unit that enables information to be output from the terminal device 300. It does not matter in what manner the output unit enables information to be output from the terminal device 300. For example, if the output unit is a speaker, the information may be output by sound from the speaker. Alternatively, if the output unit is a data writing device, the information may be output by writing the information to a storage medium connected to the terminal device 300. Alternatively, if the output unit is a transmitter, the transmitter may output the information by transmitting it to an external device outside the terminal device 300 via the network 400. In this case, the type of network does not matter. For example, the transmitter may transmit information via the Internet or via a LAN.

The terminal device 300 can receive measurement data from any measuring device by communicating with the measuring device via the interface unit 310.

The terminal device 300 can receive data on body composition parameters from the body composition scale, for example, by communicating with the body composition scale via the interface unit 310.

A body composition scale is any device capable of measuring the body composition of a subject. The body composition scale may be, for example, a type of body composition scale that can be stood on when measuring, or a type of body composition scale that can be worn on the body (e.g., a wristwatch-type body composition scale). The body composition scale may be implemented by the terminal device 300, for example, by installing an application for measuring body composition.

The terminal device 300 can receive data on blood pressure parameters from the blood pressure monitor, for example, by communicating with the blood pressure monitor via the interface unit 310.

A blood pressure monitor is any device capable of measuring the blood pressure of a subject. For example, the blood pressure monitor may be a type worn on the body (e.g., an upper arm blood pressure monitor, a wristwatch blood pressure monitor, or a ring blood pressure monitor), or a non-contact blood pressure monitor (e.g., a blood pressure monitor that measures blood pressure from an image of the face or finger taken with a camera). The blood pressure monitor may be implemented by the terminal device 300, for example, by installing an application for measuring blood pressure. The blood pressure monitor may also measure the subject's body temperature, for example. In this case, the body temperature may be measured in a non-contact manner (e.g., from an image taken with a camera) or in a contact manner.

The terminal device 300 can receive data on autonomic nervous parameters from the autonomic nervous measuring device, for example, by communicating with the autonomic nervous measuring device via the interface unit 310.

An autonomic nerve measuring device is any device capable of measuring the autonomic nerve function of a subject. The autonomic nerve measuring device may be, for example, a device capable of detecting heart rate variability. The autonomic nerve measuring device may be implemented by the terminal device 300, for example, by installing an application for measuring autonomic nerve function. The autonomic nerve measuring device can, for example, measure data related to pulse waves as data for autonomic nerve parameters. The autonomic nerve measuring device can, for example, obtain data related to the subject's pulse waves from an image of the subject taken by a camera, and derive data for autonomic nerve parameters from the data related to pulse waves.

The terminal device 300 can receive data for questionnaire parameters, for example, by communicating with a questionnaire processing device via the interface unit 310.

The questionnaire processing device may be any device capable of obtaining data from the questionnaire responses by having the subject complete a questionnaire and processing the results of the questionnaire responses by the subject. The questionnaire processing device may be, for example, an electronic device (e.g., a game console) capable of processing the results of the questionnaire responses by the subject. The questionnaire processing device may be implemented by the terminal device 300, for example, by installing an application for having the subject complete a questionnaire. The questionnaire processing device may be, for example, a device capable of optically reading the questionnaire response form. In one embodiment, the questionnaire processing device may measure, for example, data on questionnaire parameters, such as age, data on subjective assessment parameters, and/or data on lifestyle parameters. In another embodiment, the questionnaire processing device may measure, for example, data on questionnaire parameters, such as age, subjective assessment of quality of life (QOL), subjective assessment of fatigue, subjective assessment of psychology, lifestyle conditions, etc.

The questionnaire processing device may be, for example, a device capable of deriving questionnaire parameters from data acquired from a subject. Such a questionnaire processing device may acquire data indicating living conditions from data acquired from an activity tracker (e.g., sleep time, exercise time, etc.). Alternatively, such a questionnaire processing device may have the functionality of an activity tracker and automatically acquire questionnaire parameters from data acquired from the subject. Alternatively, such a questionnaire processing device may have an imaging function and automatically acquire questionnaire parameters from an image of the subject (e.g., the subject's facial expression).

The questionnaire processing device may be, for example, a group of questionnaire processing devices consisting of multiple measurement devices capable of deriving questionnaire parameters. For example, at least one questionnaire parameter may be obtained from one measurement device and at least one questionnaire parameter may be obtained from another measurement device.

The terminal device 300 can receive data related to temperature, for example, by communicating with a thermometer via the interface unit 310. Data related to temperature includes, for example, air temperature and body temperature.

The thermometer may measure temperature, for example, non-contact (e.g., from an image of the face or fingers captured by a camera, or by bringing the temperature measuring unit close to the object of temperature measurement) or contact (e.g., by bringing the temperature measuring unit into contact with the object of temperature measurement). A non-contact thermometer may be implemented using an electronic device (e.g., terminal device 300) by connecting the temperature measuring unit to the earphone jack of the electronic device. A contact thermometer can measure body temperature, for example, in the armpit, mouth, or ear. A thermometer that measures body temperature in the ear may be, for example, an earphone type (wired earphone, wireless earphone, etc.). Such a contact thermometer may also be implemented using an electronic device (e.g., terminal device 300) by connecting the temperature measuring unit to the earphone jack of the electronic device or by wirelessly connecting to the electronic device.

The measuring devices described above may be portable or handheld. As used herein, a "portable" measuring device refers to a device that can be carried manually. As used herein, a "portable" measuring device refers to a device intended to be carried around in daily life. Portable or handheld measuring devices are not limited to specific locations, and their use facilitates easy measurement at any time and place. An advantage of the present invention is that it allows for a simple assessment of a user's overall health by using parameters that can be measured using a small number of portable or handheld devices (e.g., 10 or fewer, 5 or fewer, 4 or fewer, preferably 3 or fewer).

The above-described measuring device preferably does not include a function for measuring bone density parameters, particularly ultrasonic conduction velocity (SOS = Speed of Sound). In a typical embodiment, the above-described measuring device does not require a function for measuring bone density parameters, particularly ultrasonic conduction velocity (SOS = Speed of Sound). Measuring bone density parameters requires a dedicated equipment configuration and a skilled measurer. By not including or requiring a function for measuring bone density parameters, the measuring device can be made portable or mobile, as described above, or functionality similar to that of the above-described measuring device can be implemented on the terminal device 300. This facilitates easy measurement at any location and at any time.

The measuring device described above may be server-linked. A server-linked measuring device is a device that has the ability to communicate with a server and can send measured data directly to the server. In this case, the measuring device described above does not need to communicate with the terminal device 300.

The above-mentioned measuring devices may be implemented by separate devices, or by a single device with multiple functions. For example, at least one of the above-mentioned measuring devices may be implemented by the terminal device 300. This reduces the number of measuring devices and the burden on the user for measurements. For example, by implementing all of the above-mentioned measuring devices in the terminal device 300, the user will be able to perform measurements using only the terminal device 300.

Data acquisition using the above-mentioned measuring devices may be performed by the subject consciously or unconsciously. Data acquisition using the above-mentioned measuring devices can be performed without the subject's awareness, for example, by automatically performing measurements when a predetermined condition is met. Measurements can be performed, for example, using a measuring device worn on the subject's body, without the subject's awareness. For example, by using a measuring device worn on the subject's body, measurements can be performed regardless of whether the subject is indoors or outdoors (e.g., whether the subject is walking or stationary). Alternatively, or in addition, measurements can be performed without the subject's awareness, for example, using a measuring device installed within the subject's range of movement. For example, by using a measuring device installed within the subject's range of movement, measurements can be performed regardless of what the subject is doing within the range of movement (e.g., whether the subject is walking or stationary). Examples of measuring devices installed within the subject's range of movement include, but are not limited to, sensors mounted on furniture used by the subject (e.g., chairs, beds, etc.), sensors mounted on electronic devices used by the subject (e.g., personal computers, refrigerators, air conditioners, smart speakers, etc.), and sensors installed in rooms in the subject's home, etc. For example, data on blood pressure parameters can be automatically measured by a blood pressure monitor worn on the body when a predetermined condition is met. For example, data on body composition parameters can be automatically measured by a body composition analyzer worn on the body when a predetermined condition is met. For example, data on body composition parameters can be automatically measured by a body composition analyzer mounted on the seat of the user's chair when a predetermined condition is met. For example, data on autonomic nervous system parameters can be automatically measured by a camera equipped in an autonomic nervous system measuring device when a predetermined condition is met. For example, data on autonomic nervous system parameters can be automatically derived from an image of the subject automatically captured by a camera equipped in an electronic device used by the subject or in the terminal device 300 when a predetermined condition is met. For example, data on autonomic nervous parameters can be automatically derived from images of the subject automatically captured by a camera installed in a room, such as the subject's home, when a predetermined condition is met. For example, data on questionnaire parameters can be automatically acquired through communication with an electronic device (e.g., a smart speaker) used by the subject. The predetermined conditions include, for example, a predetermined time (temporal condition), entering/leaving a predetermined location (geographical condition), or performing a predetermined behavior (behavioral condition). The predetermined time may be, for example, a predetermined time of day (e.g., between 6:00 AM and 8:00 AM, between 11:00 AM and 1:00 PM, between 4:00 PM and 6:00 PM, between 9:00 PM and midnight, or 6:00 AM, midnight, 6:00 PM, midnight, etc.), or may be at predetermined intervals (e.g., every hour, every three hours, every six hours, every 12 hours, etc.). Examples of predetermined locations include, but are not limited to, a company, home, living room, bedroom, etc. The predetermined actions include, but are not limited to, for example, sitting in a chair, lying down on a bed, being within the camera's field of view for a certain period of time (for example, 10 seconds, 30 seconds, 1 minute), etc.

In one embodiment, the predetermined action may be, for example, acquiring at least one data item. For example, in response to acquiring at least one data item among data on blood pressure parameters, data on body composition parameters, data on autonomic nervous system parameters, and data on questionnaire parameters, other data items among data on blood pressure parameters, data on body composition parameters, data on autonomic nervous system parameters, and data on questionnaire parameters may be automatically acquired. For example, in response to acquiring data on questionnaire parameters from the subject's consciously completed questionnaire responses, the questionnaire processing device may automatically measure data on blood pressure parameters, data on body composition parameters, and data on autonomic nervous system parameters. This allows multiple data items to be automatically acquired simply by having the subject consciously complete the simple action of completing a questionnaire.

In one embodiment, the terminal device 300 may perform specific preprocessing on data received from a small number of measuring devices (e.g., 10 or less, 5 or less, 4 or less, preferably 3 or less) before transmitting the data to the server device 100 for subsequent processing. The specific preprocessing may, for example, improve the efficiency of subsequent processing of data received from a small number of measuring devices.

For example, the terminal device 300 may perform specific preprocessing on a portion of the data received from a small number of measuring devices, or may perform specific preprocessing on all of the data received from a small number of measuring devices. For example, the terminal device 300 may perform first preprocessing on a first subset of the data received from a small number of measuring devices, and perform second preprocessing, different from the first preprocessing, on a second subset of the data received from the small number of measuring devices. For example, the terminal device 300 may perform first preprocessing on data acquired by measurements consciously performed by the subject, and second preprocessing on data acquired by measurements unconsciously performed by the subject.

In one embodiment, the terminal device 300 may organize the data received from a small number of measuring devices into a specific data structure before transmitting it to the server device 100. By structuring the data in a specific format, for example, subsequent processing of the data received from a small number of measuring devices may be made more efficient.

For example, the terminal device 300 may organize some of the data received from a small number of measurement devices into a data structure of a particular type, or may organize all of the data received from a small number of measurement devices into a data structure of a particular type. For example, the terminal device 300 may organize a first subset of the data received from a small number of measurement devices into a data structure of a first type, and organize a second subset of the data received from the small number of measurement devices into a data structure of a second type that is different from the first type. For example, the terminal device 300 may organize data acquired by measurements consciously performed by the subject into a data structure of a first type, and organize data acquired by measurements unconsciously performed by the subject into a data structure of a second type.

The terminal device 300 may communicate with, for example, a device connectable to a network, such as an electrical appliance connectable to a network (a so-called "IoT device"), via the interface unit 310. This allows the terminal device 300 to control the device, for example, according to the health level (for example, the position on a health level positioning map). In this case, the device may be controlled by combining, for example, data on the surrounding environment (for example, temperature, climate, weather, brightness, etc.) with the health level (for example, the position on a health level positioning map).

The device is, for example, an air conditioner. The terminal device 300 can control the air conditioner to maintain an appropriate indoor temperature and/or humidity according to the health level. The device is, for example, a light. The terminal device 300 can control the light to maintain an appropriate brightness according to the health level. The device is, for example, a music player. The terminal device 300 can control the music player to play appropriate music (for example, music that relaxes the mind and body, music that increases motivation, audio that guides yoga, stretching, aerobic exercise, etc.) according to the health level. The device is, for example, a video player. The terminal device 300 can control the video player to play appropriate videos (for example, videos that relax the mind and body, videos that increase motivation, videos of yoga, stretching, aerobic exercise, etc.) according to the health level.

The device is, for example, an incense emitter. The terminal device 300 can control the incense emitter to emit an appropriate scent (for example, a scent that relaxes the mind and body, a scent that increases motivation, etc.) according to the level of health. The device is, for example, a smart speaker. The terminal device 300 can control the smart speaker to enable the user to converse or chat with an appropriate person (for example, a comfortable person (for example, family, friends, etc.) in order to relax) or an appropriate avatar) according to the level of health. The device is, for example, an exercise device. The terminal device 300 can control the exercise device to provide an appropriate exercise menu according to the level of health. The device is, for example, an information terminal. The terminal device 300 can control the information terminal to present any information (for example, a menu of meals, drinks, or snacks, an exercise menu, etc.) according to the health level, and the terminal device 300 can control the information terminal to provide any service (for example, a health monitoring service provided by family or an affiliated institution (for example, a company, etc.), a health point service, etc.) according to the health level. The information terminal may be the terminal device 300 itself, or may be a device separate from the terminal device 300.

Camera 320 is any camera capable of capturing still or video images. It may be a camera built into terminal device 300, or an external camera attached to terminal device 300. For example, if an autonomic nervous system measuring device is implemented in terminal device 300, images captured by camera 320 can be used to derive data for autonomic nervous system parameters.

Camera 320 may be a camera capable of acquiring, for example, at least one of data on body composition parameters, blood pressure parameters, autonomic nervous system parameters, and questionnaire parameters, or all of these data. This makes it possible to replace at least one of a body composition monitor, blood pressure monitor, autonomic nervous system monitor, and questionnaire processing device, or all of these devices, with camera 320.

The display unit 330 is any display that displays a screen.

The memory unit 340 stores programs required for the terminal device 300 to execute its processes, data required for the execution of those programs, and the like. The memory unit 340 stores a program that causes the processor unit 350 to execute processes for measuring the subject's health (for example, a program that realizes the processes shown in Figure 8, which will be described later). Here, how the program is stored in the memory unit 340 is not important. For example, the program may be pre-installed in the memory unit 340. Alternatively, the program may be installed in the memory unit 340 by being downloaded via the network 350. In this case, the type of network is not important. The memory unit 340 may be implemented by any storage means.

The processor unit 350 controls the overall operation of the terminal device 300. The processor unit 350 reads and executes programs stored in the memory unit 340. This allows the terminal device 300 to function as a device that executes desired steps.

In an embodiment in which the blood pressure monitor is implemented by the terminal device 300, the processor unit 350 may be configured to acquire data regarding blood pressure, for example, by analyzing images acquired by the camera 320. The processor unit 350 may acquire data regarding blood pressure, for example, by using a known application to analyze video of a face acquired by the camera 320. The processor unit 350 may acquire data regarding blood pressure, for example, by using a known application to analyze video of a fingertip acquired by the camera.

In an embodiment in which the autonomic nervous system measuring device is implemented by the terminal device 300, the processor unit 350 may be configured to obtain data regarding autonomic nervous system function, for example, by analyzing images acquired by the camera 320. The processor unit 350 may obtain data regarding autonomic nervous system function from the images using techniques known in the art.

In another embodiment in which the autonomic nervous system measuring device is implemented by the terminal device 300, the processor unit 350 may, for example, acquire images by the camera 320 from which data for autonomic nervous system parameters can be derived, and transmit the acquired images via the interface unit 310 to the server device 100 for analysis. In this embodiment, the processor unit 350 does not have the function of deriving data for autonomic nervous system parameters; instead, the server device 100 has that function. This reduces the processing load on the terminal device 300.

In an embodiment in which the questionnaire processing device is implemented by a terminal device 300, the processor unit 350 may be configured to obtain data for the questionnaire parameters, for example, by presenting a questionnaire to a user via the display unit 330 and processing responses to the questionnaire entered via the interface unit 310. The processor unit 350 may obtain data for the questionnaire parameters from the responses to the questionnaire using techniques known in the art.

In another embodiment in which the questionnaire processing device is implemented by the terminal device 300, the processor unit 350 may, for example, receive responses to the questionnaire and transmit the received responses to the server device 100 via the interface unit 310 for analysis. In this embodiment, the processor unit 350 does not have the function of deriving data for questionnaire parameters; instead, the server device 100 has that function. This reduces the processing load on the terminal device 300.

In the example shown in FIG. 3E, each component of the terminal device 300 is provided within the terminal device 300, but the present invention is not limited to this. Any of the components of the terminal device 300 may also be provided external to the terminal device 300. For example, the camera 320, display unit 330, memory unit 340, and processor unit 350 may each be configured as separate hardware components, and each hardware component may be connected via any network. In this case, the type of network does not matter. Each hardware component may be connected, for example, via the Internet, via a LAN, wirelessly, or via a wired connection.

2, the database unit 200 is provided external to the server device 100, but the present invention is not limited to this. It is also possible to provide at least a portion of the database unit 200 inside the server device 100. In this case, at least a portion of the database unit 200 may be implemented by the same storage means as the storage means that implements the memory unit 150, or by a storage means different from the storage means that implements the memory unit 150. In either case, at least a portion of the database unit 200 is configured as a storage unit for the server device 100. The configuration of the database unit 200 is not limited to a specific hardware configuration. For example, the database unit 200 may be configured as a single hardware component, or may be configured as multiple hardware components. For example, the database unit 200 may be configured as an external hard disk drive for the server device 100, or as cloud storage connected via the network 400.

3. First Parameter Set The inventors focused on physiological or biochemical mechanisms common to health frailty and chronic fatigue in order to evaluate the overall health of a subject. Without intending to be bound by theory, based on previous fatigue medical research and health frailty follow-up research, there may be a common mechanism between fatigue, chronic fatigue, health frailty, aging, and disease onset. This is
(1) "Biological oxidation (rusting)" indicates the progression of biological oxidation and the decline of antioxidant capacity.
(2) "Decrease in repair energy," which indicates a delay in the release of the above-mentioned biological oxidation.
(3) "Inflammation," which indicates the detection of oxidized (rusty) cells, and (4) "Autonomic nervous function," which indicates the function of sensing and regulating (1) to (3) above.
By comprehensively evaluating the parameters (1) to (4), it becomes possible to determine a state of health frailty that has not yet progressed to disease (i.e., "pre-disease").

Based on the above theory, in a representative embodiment, the first parameter set for obtaining the first data set in the present invention may include a "biological oxidation parameter," a "repair energy reduction parameter," an "inflammation parameter," and an "autonomic nervous function parameter." Hereinafter, the four parameters (1) "biological oxidation parameter," (2) "repair energy reduction parameter," (3) "inflammation parameter," and (4) "autonomic nervous function parameter" may be collectively referred to as the four basic parameters.

The first parameter set is a set of parameters used to create a health positioning map. In this specification and claims, the first parameter set is also referred to as the "parameter set for creating a health positioning map."

(biooxidation parameters)
Reactive oxygen species (ROS) oxidize and denature many biopolymers that make up cells, such as DNA, lipids, proteins, and enzymes, in vivo, impairing cellular functions. Oxidative denature caused by reactive oxygen species is thought to lead to various diseases and aging.

On the other hand, the body contains antioxidant enzymes such as superoxide dismutase (SOD) and catalase, as well as antioxidants such as coenzyme Q10, vitamin C, and vitamin E, to prevent cellular dysfunction caused by reactive oxygen species.

Therefore, the measurement of "biological oxidation parameters" in the present invention can include the measurement of oxidative damage caused by reactive oxygen species, the measurement of antioxidant capacity, or the measurement of the balance between oxidative damage and antioxidant capacity. The measurement of oxidative damage caused by reactive oxygen species can be carried out by directly measuring the amount of reactive oxygen species, or by measuring oxidative damage to proteins, lipids, or nucleic acids.

Methods for measuring oxidative damage are well known in the art, and those skilled in the art can select and measure an appropriate measurement target. Specific markers used in the present invention as indicators of oxidative damage caused by reactive oxygen species include, but are not limited to, d-ROMs (Derivatives of Reactive Oxygen Metabolites), which directly measure the amount of reactive oxygen species in the blood; protein carbonyl content (PCC; Protein Carbonyl Content), which is an indicator of oxidative damage to proteins; 4-hydroxynonenal and isoprostane, which are indicators of oxidative damage to lipids; and 8-OHdG (8-hydroxydeoxyguanosine), which is an indicator of oxidative damage to nucleic acids.

Methods for measuring antioxidant capacity are well known in the art, and those skilled in the art can select and measure an appropriate subject. In the present invention, specific markers used as indicators of antioxidant capacity include, but are not limited to, BAP (Biological Antioxidant Potential), which quantifies iron-reducing power, serum thiol status, glutathione measurement, vitamin C measurement, total coenzyme Q10, and reduced coenzyme Q10 ratio. Total coenzyme Q10 and reduced coenzyme Q10 ratio can be measured, for example, using LC-MS/MS (specifically, they can be calculated from the concentrations of reduced and oxidized coenzyme Q10 detected, for example, by multiple reaction monitoring).

In the present invention, the marker that indicates the balance between oxidative damage and antioxidant capacity is, but is not limited to, OSI (Oxidation Stress Index). Note that the OSI in the present invention is d-ROMs/BAP.

Preferred biooxidation parameters in the present invention include BAP, total coenzyme Q10, reduced coenzyme Q10 ratio, and OSI.

Typically, biooxidation parameters are parameters that can be measured by non-invasive tests.

(Repair energy reduction parameter)
Even if biological tissues are damaged by oxidation, the body has a mechanism to repair the damaged tissue. ATP is required for tissue damage in the body. However, a decrease in ATP production delays the repair of damaged tissue. A decrease in ATP production also leads to a delay in recovery from fatigue. The "repair energy decrease parameter" in the present invention is any parameter that indicates a decrease in ATP production.

ATP is produced through glycolysis, the TCA cycle, and the electron transport chain, with the greatest amount of ATP produced in the final electron transport chain. Coenzyme Q10 is an important coenzyme responsible for the electron transport chain. Therefore, "repair energy reduction parameters" in the present invention include, but are not limited to, total coenzyme Q10, reduced coenzyme Q10 ratio, and glycolysis and TCA cycle metabolites (e.g., pyruvate, lactate, citric acid, isocitrate, succinate, fumarate, malate, etc.). It should be noted that total coenzyme Q10 and reduced coenzyme Q10 ratio are "biooxidation parameters" due to their antioxidant activity, and are also "repair energy reduction parameters" due to their contribution to ATP production. Preferred "repair energy reduction parameters" in the present invention may include total coenzyme Q10 and reduced coenzyme Q10 ratio.

The methods for measuring total coenzyme Q10 and the ratio of reduced coenzyme Q10 are as described above. Metabolites of the glycolytic pathway and citric acid cycle can be measured by extracting compounds that reflect the glycolytic pathway and citric acid cycle from metabolome analysis.

Typically, the repair energy reduction parameter is a parameter that can be measured by non-invasive testing.

(Inflammatory parameters)
When a large amount of tissue is damaged by oxidation in a living body, a large amount of local inflammation occurs due to an immune response. Types and measurement methods of inflammation parameters are well known in the art, and those skilled in the art can appropriately select and measure inflammation parameters.

Inflammatory parameters in the present invention include, but are not limited to, CRP (C-Reactive Protein), WBC (white blood cell count), albumin, red blood cell count, interleukin-1β, interleukin-6, etc.

Typically, the inflammation parameter is one that can be measured by a non-invasive test.

(Autonomic nervous function parameters)
When health frailty is examined over time, it is first observed that autonomic nervous function (especially parasympathetic nervous function) declines, followed by a decline in sleep quality, followed by the accumulation of fatigue, followed by loss of motivation, depression, immune system disorders such as allergies, endocrine system disorders such as menstrual irregularities, and digestive system abnormalities. Therefore, abnormalities in autonomic nervous function are an important parameter for understanding the early stages of health frailty.

In the present invention, autonomic nervous function is evaluated from heart rate parameters. Specifically, the heart rate parameters used in the present invention include, but are not limited to, the following:
・Average HR
It refers to the average of all heart rates over a 5-minute period.
Activity of the entire autonomic nervous system: TP (Total Power = ms ² )
The calculated total power of the power spectrum in the frequency range of 0 to 0.4 Hz (VLF, LF, HF) measured for 5 minutes. This value reflects the overall activity of the autonomic nervous system, which is primarily dominated by sympathetic nervous activity, and is said to be a value related to fatigue.
- Global activity of sympathetic nervous system function; VLF (very low frequency ^ms2 )
This is the power spectrum of the frequency band of approximately 0.0033 to 0.04 Hz. Generally, this parameter is said to indicate the overall activity of the very slow mechanism of sympathetic nervous function.
Sympathetic nervous activity: LF (low frequency ^ms2 )
This is the power spectrum of the frequency band of approximately 0.004 to 0.15 Hz, and this value mainly reflects the activity of the (vasomotor) sympathetic nerves.
Parasympathetic nervous activity: HF (high frequency ms ² )
This is the power spectrum of the frequency band of approximately 0.15 to 0.4 Hz, and this value reflects the activity of the parasympathetic nerve (vagus nerve).
・Sympathetic and parasympathetic balance; LF/HF ratio This is the ratio of LF (low frequency) to HF (high frequency) power, and this value represents the overall balance between the sympathetic and parasympathetic nervous systems. Generally, a high value indicates sympathetic dominance, and a low value indicates parasympathetic dominance.

The above heart rate parameters that indicate autonomic nervous function can be measured by methods known in the art, but can also be measured using a precise methodology that simultaneously measures electrocardiograms and fingertip pulse waves and performs heart rate variability analysis (see Japanese Patent Nos. 5455071 and 5491749, which are incorporated herein by reference). For example, the autonomic nervous function parameters of the present invention may be measured using the simplified autonomic nervous system measuring device FMCC-VSM301 (Fatigue Science Institute Co., Ltd., Osaka, Japan), which can simultaneously measure electrocardiograms and pulse waves.

Preferred "autonomic nervous function parameters" in the present invention may be, but are not limited to, mean HR, TP, LF, HF, LF/HF, etc. Note that because the variance of HF and LF values is large and they do not follow a normal distribution, it is preferable to evaluate the above parameters, TP, LF, HF, and LF/HF, which are related to HF and LF, by logarithmic transformation. Therefore, more preferred "autonomic nervous function parameters" in the present invention include mean HR, ln(TP), ln(LF), ln(HF), and ln(LF/HF).

Typically, autonomic function parameters are parameters that can be measured by non-invasive tests.

(Further first parameter set)
The first parameter set of the present invention may include one or more of the following parameters to create a positioning map that more appropriately assesses the overall health of a subject:

Basic parameters include known parameters that represent the physical condition and health state of a subject. The basic parameters of the present invention include, but are not limited to, age, height, weight, waist circumference, body composition, bone mineral density, blood pressure, muscle strength, body temperature, and WH ratio.

Body composition includes, but is not limited to, muscle mass, BMI (body mass index = weight/height), and fat percentage. Body composition can be a parameter measured using, for example, bioelectrical impedance analysis (BIA). BIA is a technology that quantitatively measures the components that make up the human body from the impedance generated when an electric current is passed through the body. For example, the body composition analyzer manufactured by InBody Japan Co., Ltd. can measure body composition using BIA.

There are several known methods for measuring bone density, including the MD method, which measures bone density from photographs of hand bones taken with X-rays; the ultrasound method, which measures the heel bone using ultrasound; the QCT method, which uses CT scans; and the DEXA (Dual Energy X-ray Absorptiometry) method, which uses X-rays and a computer. Parameters obtained by any of these methods can be used in the present invention.

Examples of bone mineral density parameters in the present invention include, but are not limited to, ultrasonic wave velocity (SOS = Speed of Sound), osteoporosis assessment (Osteoporosis Index; e.g., OSIRIS (Osteoporosis Index of Risk)), young adult comparison % (YAM = Young Adult Mean; young age mean BMD (Bone Mineral Density); bone mineral density = bone mass / area (unit: g/ ^cm2 )) value (reference value) is set to 100%, and the subject's BMD value is compared to % of the T-score (value defining an index with the young age mean BMD value (reference value) set to 0 and the standard deviation as 1 SD). Methods for measuring these basic parameters are well known in the art.

Blood pressure is routinely measured in the art, and systolic blood pressure may be used as the basic parameter for the present invention.

Muscle strength is conventionally measured in the field, and muscle mass and average left and right grip strength can be used as basic parameters in the present invention.

The WH ratio (waist-hip ratio) is the waist circumference divided by the hip circumference and is used as an indicator of obesity. The WH ratio can be used to determine whether an obese person has "pear-shaped obesity" or "apple-shaped obesity," which describes their body type.

The basic parameters of the present invention may preferably include age, muscle mass, BMI, fat percentage, SOS, OI, systolic blood pressure, and average left and right grip strength.

Basic parameters are parameters that can be measured by non-invasive tests.

Blood Parameters In addition to the above-mentioned biooxidation parameters, it is preferable that the first parameter set further includes blood parameters that are commonly used to evaluate the subject's renal excretory, hepatobiliary, and pancreatic detoxification function systems.

These blood parameters include, but are not limited to, HbA1c (hemoglobin A1c; a glycated protein formed by binding glucose to hemoglobin), ALP (alkaline phosphatase), ALT (alanine aminotransferase), AST (aspartate aminotransferase), BS (blood glucose level), BUN (blood urea nitrogen), CK (creatine kinase; a plasma muscle cell enzyme that can be used to evaluate the motor, skeletal, and muscular function systems), G-GT (gamma glutamyl transpeptidase), HDL-C (HDL-cholesterol), HGB (hemoglobin), LD (lactate dehydrogenase), LDL-C (LDL cholesterol), TG (triglycerides; neutral fats), T-P (total protein), UA (uric acid), amylase, albumin, potassium, creatinine, chloride, cortisol, sodium, eGFR, vitamins (e.g., vitamin B1), and mineral (iron, copper, calcium, etc.) levels.

Typically, the blood parameters are parameters that can be measured by invasive tests.

Cognitive function parameter According to research by the present inventors, cognitive function declines as fatigue accumulates. Therefore, the first parameter of the present invention may further include a cognitive function parameter.

The cognitive function parameters of the present invention are measured using a simple cognitive function test called Trail Making Test (TMT), in which subjects trace in order with a pencil indicators such as numbers from 1 to 25 written on a piece of paper.
The cognitive function parameters can be obtained by the Advanced Trail Making Test (ATMT), which performs TMT on a touch panel, and the modified ATMT (K. Mizuno et al. Brain & Development 33 (2011) 412-420) developed by the present inventors. Although the TMT, ATMT, and modified ATMT differ in methodology, they all measure the same subject, and are all cognitive function parameters of the present invention.

The inventors have prepared, for example, five tasks for assessing various elements of cognitive function as part of the modified ATMT, which can be used alone or in combination. Furthermore, each of these cognitive tasks can be assessed based on total reaction time or total number of correct answers.

Cognitive function parameters are parameters that can be measured by non-invasive tests.

Blood vessel and skin parameters According to research by the present inventors, accumulation of fatigue can affect blood vessels and skin. Therefore, preferably, the first parameter set of the present invention may also include blood vessel and skin parameters.

Examples of vascular parameters include, but are not limited to, vascular age, average capillary length, vascular opacity, and number of blood vessels. The average capillary length, vascular opacity, and number of blood vessels can be easily measured by image processing of the capillary course in the fingernail bed. Image processing of the capillary course and measurement of these parameters can be performed using, for example, a capillary scope manufactured by Atto Co., Ltd. (Osaka, Japan).
Skin parameters include, but are not limited to, for example, the moisture content, moisture loss, gloss, etc. Methods for measuring the moisture content, moisture loss, and gloss of arm skin are well known in the art.

Vascular and skin parameters are parameters that can be measured by non-invasive tests.

Subjective Evaluation Parameters The first parameter set of the present invention may include subjective evaluation parameters of the subject in addition to the objective parameters obtained by the measurements described above. By adding subjective evaluations to the first parameter set in addition to the objective parameters based on various measured values, the health positioning map can reflect the subject's physical, fatigue, and mental state, etc., which cannot be fully understood from the measurements of various components.

In one embodiment, the subjective evaluation parameters of the present invention may include subjective evaluations of fatigue, sleep, mental state, etc. The subjective evaluation parameters of the present invention may also include subjective evaluations related to personality and temperament.

Subjective assessment of fatigue may include one or more of the following: subjective assessment of fatigue duration, fatigue disability questions, fatigue visual analogue scale (VAS), Chalder Fatigue Scale (CFQ), fatigue symptom score (CFQ11) calculated using 11 items of the CFQ (Tanaka M et al., Psychol Rep_2010, 106, 2, 567-575), presenteeism questionnaire or VAS, and fatigue questionnaire. "Fatigue disability questions" refer to questions that confirm the subject's subjective assessment of whether or not fatigue is causally related to any disability. "Fatigue disability questions" may, for example, include whether the subject feels that fatigue interferes with work, housework, or schoolwork, or questions about illnesses that may be causing fatigue. The "fatigue questionnaire" asks whether a person is aware of any symptoms of fatigue, such as whether they feel fatigued, whether they feel tired even after a night's sleep, etc. As a presenteeism questionnaire, for example, the WHO Health and Work Performance Questionnaire (HPQ) or the Work Limitations Questionnaire (WLQ) may be used.

Subjective assessment of sleep may include one or more of the following: time of sleep (asleep onset and wake-up), average sleep time, VAS for sleepiness, and questionnaire for sleep quality.

The subjective assessment of mental state may include one or more of a depression VAS and questionnaire, and a motivation VAS and questionnaire. A "depression questionnaire" refers to questions about any symptoms of depression, and may include whether the subject feels depressed, whether they feel reluctant to interact with others, etc., and one example is the K6 total (a commonly used index of mental problems developed by Kessler et al.).

In the above, the "questionnaire" may be evaluated based on answers to specific questions, or by scoring answers to multiple questions.

Subjective parameters are parameters that can be measured by non-invasive tests.

Lifestyle parameters The first parameter set of the present invention may include lifestyle parameters in addition to the objective parameters and the subjective evaluation parameters. The lifestyle parameters of the present invention are facts about the subject's lifestyle, and may include, for example, years of education, marital duration, whether or not the subject is living with their spouse, smoking status (yes/no, frequency and/or amount), drinking status (yes/no, frequency and/or amount), working hours, exercise (yes/no, frequency and/or amount), dietary status (whether or not the subject feels they eat too quickly, how often they snack after dinner, how often they skip breakfast, etc.), medical history, medication status, and supplement intake status.

Life status parameters are parameters that can be measured by non-invasive tests.

Additional Parameters The first parameter set of the present invention may include parameters based on data relating to evaluation of brain function and neuropsychiatric system, evaluation of cardiovascular and respiratory function system, and evaluation of renal excretion, hepatobiliary and pancreatic and detoxification function system.

For example, data related to brain function and neuropsychiatric assessment may include, in addition to those mentioned above, communication function, activity levels (daytime and during sleep), brain morphometry using MRI (Magnetic Resonance Imaging) (which can measure functional decline according to areas of brain tissue that have contracted), resting fMRI, and nerve fiber bundle anisotropy (size and robustness of nerve fiber bundles).

In addition to the above, data related to assessment of the cardiovascular and respiratory function systems may also include blood flow (which can be measured, for example, by a Doppler flowmeter) and analysis of exhaled gas components (NO (asthma), acetone (diabetes), etc.). Data related to analysis of exhaled gas components can be measured by analysis using mass spectrometry or ion mobility spectrometry.

Data related to the evaluation of the renal excretory, hepatic, biliary, and pancreatic detoxification systems may include data from skin gas component analysis, in addition to the above. Data related to skin gas component analysis can be measured using mass spectrometry or a highly sensitive variable laser detection device.

(Preferred First Parameter Set)
In one embodiment, the first parameter set of the present invention may include a biooxidation parameter, a repair energy reduction parameter, an inflammation parameter, and an autonomic nervous function parameter (basic four parameters). By creating a health positioning map based on data on the first parameter set including these basic four parameters, the health positioning map makes it possible to determine a health fragility state that has not yet developed into a disease (i.e., "pre-disease").

In another embodiment, the first parameter set of the present invention may include four basic parameters, a fundamental parameter, a cognitive function parameter, and a subjective parameter. Without intending to be bound by theory, it is believed that adding fundamental parameters, cognitive function parameters, and subjective parameters to the four basic parameters allows for a broad assessment of fatigue and mental state. Preferably, the subjective parameters include an assessment of one or more of fatigue, sleep, and mental state, and more preferably include at least an assessment of fatigue.

In yet another embodiment, the first parameter set of the present invention may include four basic parameters, a basal parameter, a cognitive function parameter, a subjective parameter, and a blood parameter. Without intending to be bound by theory, by adding a blood parameter that can accurately evaluate the function of the endocrine system to the first parameter set in addition to the four basic parameters, the basal parameters for evaluating fatigue and mental state, the cognitive function parameter, and the subjective parameter, it becomes possible to evaluate a broader range of health levels that includes even different perspectives.

In yet another preferred embodiment, the first parameter set of the present invention may include four basic parameters, fundamental parameters, cognitive function parameters, subjective parameters, blood parameters, vascular and skin parameters, and living situation parameters.

The first parameter set of the present invention typically includes both invasive and non-invasive parameters. This means that even if the second parameter set (described below) is composed solely of non-invasive parameters, the positioning map that serves as the basis for evaluation still reflects information about the invasive parameters. As a result, by examining only non-invasive parameters, evaluation can be performed that takes into account the influence of parameters that can only be obtained through invasive testing. This is one of the significant advantages of the present invention.

The first parameter set may be, for example, an extracted parameter set obtained by obtaining a data set of an initial parameter set, calculating the correlation between each piece of data in the data set of the initial parameter set, and extracting parameters whose correlation coefficient is greater than or equal to a predetermined threshold, or it may be an extracted parameter set obtained by using machine learning to extract a parameter set from the data set of the initial parameter set that has a high influence on the health positioning map.

4. Second Parameter Set The second parameter set is a part of the first parameter set and is a set of parameters used to derive a health function. By using the second parameter set, the subject's condition can be appropriately represented on the health positioning map using a smaller number of parameters than the first parameter set. In this specification and claims, the second parameter set is also simply referred to as a "parameter set." The second parameter set may include parameters not included in the first parameter set.

In a preferred embodiment of the present invention, the second parameter set may consist solely of non-invasive parameters. However, the positioning map used to evaluate this second parameter set may also include invasive parameters. Therefore, by evaluating a subject's overall health using a second parameter set consisting solely of non-invasive parameters, an evaluation that inherently includes the influence of invasive parameters can be performed. This is one of the significant advantages of the present invention. Furthermore, estimating a user's health status from such a parameter set that does not include invasive test results allows users to easily know their own health status regardless of the location of the test. For example, users can learn their own health status through simple tests not only at hospitals but also at work, pharmacies, community centers, cafes, homes, etc.

In one embodiment, the second parameter set consisting only of non-invasive parameters may include the following parameters:
(1) Body composition parameters (2) Autonomic nervous system parameters (3) Questionnaire parameters

Here, (2) the autonomic nervous parameters may include data related to pulse waves. (3) The questionnaire parameters may include, for example, age, living situation parameters, and/or subjective assessment parameters. The subjective assessment parameters included in the questionnaire parameters may include subjective assessments related to QOL (Quality of Life), subjective assessments related to fatigue, and subjective assessments related to psychology.

(1) The body composition parameters may be at least a part of the basic parameters of the first parameter set described above. Preferably, the body composition parameters may include at least one of BMI and WH ratio, and more preferably both BMI and WH ratio. This makes it possible to associate a visually understandable indicator of body shape with the subject's condition on the health positioning map. In this case, the BMI and WH ratio may or may not be included in the first parameter set. In one example, the BMI may be included in the first parameter set, and the WH ratio may not be included in the first parameter set. (2) The autonomic nervous parameters may be at least a part of the autonomic nervous function parameters of the first parameter set described above. (3) The questionnaire parameters may be at least a part of the basic parameters, lifestyle situation parameters, and/or subjective assessment parameters of the first parameter set described above.

(1) Data on body composition parameters can be measured using a body composition analyzer, (2) data on autonomic nervous parameters can be measured using an autonomic nervous system measuring device, and (3) data on questionnaire parameters can be measured using a questionnaire processing device. In this way, parameters (1) to (3) can be measured using at most three measuring devices. This reduces the burden on the user for measurement and makes it easier to measure at a location other than a testing facility, such as at home. In a preferred embodiment, one or both of the body composition parameters and autonomic nervous system parameters can be automatically measured without the user's awareness, and these parameters can be combined with the latest questionnaire parameters to evaluate the user's overall health on a health positioning map.

The second set of parameters in this embodiment is:
(4) Blood pressure parameter. Including (4) blood pressure improves the accuracy of health prediction compared to the case where only the parameters (1) to (3) are included. In a preferred embodiment, any one, any two, or all three of the body composition parameters, autonomic nerve parameters, and blood pressure parameters are automatically measured without the user's awareness, and these parameters are combined with the most recent questionnaire parameters to provide an assessment of the user's overall health on a health positioning map.

(4) Data for blood pressure parameters can be measured using a sphygmomanometer. Thus, parameters (1) to (4) are parameters that can be measured using at most four measuring devices. This improves the accuracy of predictions while still reducing the burden on the user for measurement, making it easier to measure at any location and time, such as at home.

In these embodiments, the second parameter set does not include non-invasive parameters that are complex to measure or require a long time to measure. For example, the second parameter set does not include cognitive function parameters or bone mineral density parameters. Rather, the second parameter set is made up of parameters that can be easily measured using simple measuring devices; in one embodiment, the second parameter set is made up of parameters that can be measured using at most four measuring devices, in another embodiment, the second parameter set is made up of parameters that can be measured using at most three measuring devices, and in yet another embodiment, the second parameter set is made up of parameters that can be measured using a single measuring device. This reduces the burden on the user for measurement and enables easy measurement at locations other than a testing facility, such as at home.

In a preferred embodiment, the above-mentioned parameters that can be measured automatically can be measured automatically, without the user's awareness, within a time range set by the user, triggered by a specified condition with each parameter measurement device (for example, contact with various devices, or the distance between various devices and the user coming within a certain range, etc.).

5A shows an example of processing in the server device 100. The processing 500 is processing for creating a health level positioning map. The processing 500 is executed by the processor unit 120 of the server device 100.

In step S501, the acquisition means 121 of the processor unit 120 acquires a first data set for a first parameter set for each of a plurality of subjects. The acquisition means 121 can, for example, receive data for a plurality of subjects stored in the database unit 200 via the interface unit 110 and acquire the received data. The acquisition means 121 can, for example, receive data for a plurality of subjects stored in the database unit 200 from a computer system of an examination facility (e.g., a hospital, research institute, etc.) via the interface unit 110 and acquire the received data.

In step S502, the processing means 122 of the processor unit 120 processes the first data set acquired in step S501 to obtain first data. The processing by the processing means 122 may include, for example, at least one of a dimensionality reduction process, a standardization process, and a weighting process on the first data set.

Preferably, the processing by the processing means 122 includes a dimensionality reduction process for the first dataset. This reduces the dimensions of the multidimensional, complex first dataset, enabling the creation of more understandable data and ultimately a health positioning map. In this case, the dimensionality reduction process preferably reduces the first dataset to two-dimensional or three-dimensional data. This is because a health positioning map created from two-dimensional or three-dimensional data is a map of two-dimensional or three-dimensional space, making it visually easier to understand. The dimensionality reduction process (1) makes it possible to determine the contribution of each measurement item to the values of each axis of the health positioning map, and (2) allows the data on the health positioning map to be more clearly understood, excluding data for measurement items that are not significantly related to the values of each axis of the health positioning map.

More preferably, the processing by the processing means 122 includes a standardization process for the first dataset and a dimensionality reduction process for the standardized dataset. This eliminates scale differences between parameters in the first dataset and ensures that the influence of each parameter in the first dataset on the health positioning map is equally taken into account, making it possible to create a highly accurate and easy-to-understand health positioning map. In particular, for example, by performing a standardization process on parameters resulting from gender differences, it becomes possible to evaluate both data obtained from male subjects and data obtained from female subjects on the same health positioning map.

More preferably, the processing by the processing means 122 includes a weighting process for the first data set, a standardization process for the weighted data set, and a dimensionality reduction process for the standardized data set. This emphasizes the magnitude of the influence of each parameter of the first data set on the health positioning map, making it possible to create a health positioning map that is more accurate and easier to understand.

The standardization process by the processing means 122 may be performed, for example, on all parameters in the first parameter set, or on specific parameters. The weighting process by the processing means 122 may be performed, for example, on the entire first data set of multiple subjects, or on the first data set of a specific population of multiple subjects.

In step S503, the mapping means 123 of the processor unit 120 maps the first data obtained in step S502 for each of the multiple subjects. The mapping means 123 maps the n-dimensional first data onto n-dimensional space. By mapping the first data for each of the multiple subjects, the mapping means 123 can output a map on which the first data for each of the multiple subjects is mapped. For example, if the first data is two-dimensional, the mapping means 123 can output a two-dimensional map by mapping the first data to positions in two-dimensional space, i.e., on a plane.

In step S504, the clustering means 124 of the processor unit 120 identifies multiple regions by clustering the first data mapped in step S503. The clustering means 124 can identify any number of regions by dividing the mapped first data into any number of clusters.

In step S505, the characterization means 125 of the processor unit 120 characterizes at least some of the multiple regions identified in step S504. The characterization means 125 may, for example, characterize at least some of the multiple regions based on information input to the server device 100, or may automatically characterize at least some of the multiple regions without relying on input. For example, the characterization means 125 may characterize at least some of the multiple regions based on their relative positions on a health positioning map, or may characterize at least some of the multiple regions based on machine learning.

The above-described process 500 creates a health positioning map that characterizes at least some of the regions. The created health positioning map can be used in processes 510, 600, and 700, which are described below.

Figure 5B shows another example of processing in the server device 100. Process 510 is processing for creating a health level positioning map for data included in a partial area of the health level positioning map created in process 500. Process 510 is executed in the processor unit 120 of the server device 100.

In step S511, the processor unit 120 receives input to select a portion of the multiple areas of the health positioning map created in process 500. The input to select a portion of the multiple areas is input, for example, from outside the server device 100 via the interface unit 110.

In step S512, the acquisition means 121 of the processor unit 120 acquires the first data mapped to the selected area. The acquired first data may be referred to as sub-first data.

In step S513, the clustering means 124 of the processor unit 120 identifies multiple regions by clustering the sub-first data acquired in step S512. The processing in step S513 may be similar to the processing in step S504.

In step S514, the characterization means 125 of the processor unit 120 characterizes at least some of the multiple regions identified in step S513. The processing in step S514 may be similar to the processing in step S505.

The above-described process 510 creates a health positioning map for some of the subjects. The health positioning map for some of the subjects may be, for example, a health positioning map focused on a particular group of the subjects, such as a male subject group, a female subject group, a young group (a group under 40 years old), a middle-aged group (a group aged 40 to 60 years old), or an elderly group (a group aged 60 years old or older). The multiple regions of the health positioning map focused on a specific group may have different characteristics from the multiple regions of the overall health positioning map for the multiple subjects, and may be used to analyze the health status from different perspectives.

Figure 6 shows another example of processing in the server device 100. Process 600 is a process for creating a health function. Process 600 is executed in the processor unit 130 of the server device 100.

In step S601, the processor unit 130 prepares a health degree positioning map. For example, the processor unit 130 can prepare the health degree positioning map by acquiring the health degree positioning map using the first acquisition means of the processor unit 130. The health degree positioning map is created based on the first data set for the above-mentioned first parameter set for multiple subjects. The prepared health degree positioning map may be a health degree positioning map created by process 500 or process 510, or a health degree positioning map created in a different manner, as long as it is created using the first parameter set.

In step S602, the second acquisition means 132 of the processor unit 130 acquires a second data set for a second parameter set for at least some of the multiple subjects. The second parameter set is part of the first parameter set. The second acquisition means 132 can, for example, acquire data for some of the multiple subjects stored in the database unit 200 via the interface unit 110.

In step S603, the derivation means 133 of the processor unit 130 derives a health function that correlates the second data set of at least some of the subjects acquired in step S602 with the positions on the health positioning map of at least some of the subjects. The derivation means 133 can derive the health function, for example, by machine learning. The health function can be derived, for example, for each axis of the n-dimensional health positioning map.

The health function may be, for example, a regression model or a neural network model. If the health function is a regression model, the derivation means 133 can derive each coefficient of the regression model by performing machine learning for at least some of the multiple subjects, using the second dataset as an independent variable and the subject's coordinates on the health positioning map as a dependent variable. If the health function is a neural network model, the derivation means 133 can derive the weighting coefficient of each node by performing machine learning for at least some of the multiple subjects, using the second dataset as input training data and the subject's position on the health positioning map as output training data.

The above-described process 600 creates a health function for mapping the subject's health on a health positioning map. The created health function can be used in process 700, described below. By not including invasive test results in the second parameter set when creating the health function, the resulting health function can identify a location on the health positioning map from data in a parameter set that does not include invasive test results and estimate the user's health. Estimating a user's health from such a parameter set that does not include invasive test results allows the user to know their own health regardless of the location of the test. For example, users can learn their own health through simple tests not only at hospitals but also at work, pharmacies, community centers, cafes, homes, etc.

Figure 7A shows another example of processing in the server device 100. Process 700 is a process for estimating the user's health level. Process 700 is executed in the processor unit 140 of the server device 100.

In step S701, the processor unit 140 prepares a health function. For example, the processor unit 140 can prepare the health function by acquiring the health function using the third acquisition means 141 of the processor unit 140. The health function is a function that correlates a data set for the second parameter set with a position on the health positioning map. The acquired health function may be a health function created by process 600 or a health function created in a different way, as long as it can correlate a user data set with a position on the health positioning map. The health positioning map may be a health positioning map created by process 500 or process 510, as long as it is created using the first parameter set, or a health positioning map created in a different way.

In step S702, the fourth acquisition means 142 of the processor unit 140 acquires a first user data set for the user's second parameter set. The fourth acquisition means 142 can, for example, acquire the first user data set stored in the database unit 200 via the interface unit 110. Alternatively, the fourth acquisition means 142 can, for example, acquire the first user data set from the user's terminal device via the interface unit 110.

In step S703, the output generating means 143 of the processor unit 140 obtains a first output by inputting the first user data set into a health function. For example, if the health function is a regression model, the first user data set is input into an independent variable of the regression model, and coordinates on a health positioning map are output as the first output. For example, if the health function is a neural network model, the first user data set is input into the input layer of the neural network model, and coordinates on a health positioning map are output as the first output.

In step S704, the output mapping means 144 of the processor unit 140 maps the first output onto the health positioning map. Since the output obtained in step S703 is a coordinate on the health positioning map, the output mapping means 144 can map the coordinate within the n-dimensional space of the health positioning map.

By mapping the user's health level on a health positioning map using the above-described process 700, the user's health status can be estimated from the characterization of the area where the user's health level is mapped. For example, by using a health function that does not include invasive test results in the second parameter set, a location on the health positioning map can be identified from data in the parameter set that does not include invasive test results, and the user's health level can be estimated. Estimating a user's health level from such a parameter set that does not include invasive test results allows the user to know their own health level regardless of the location of the test. For example, users can learn their own health level through simple tests not only at hospitals but also at work, pharmacies, community centers, cafes, homes, etc.

For example, the estimated health status can be used to correct other data. The other data can be, for example, data that can vary depending on the health status. For example, the other data can include, but is not limited to, test results, including cognitive ability tests, physical fitness tests, academic achievement tests, etc. For example, the test results can be corrected to add points to a user whose test results are poorer because their health status is worse than normal. For example, in order to minimize the impact of a user's health status, the test results can be corrected to add points to a user who is relatively healthier among multiple users.

Figure 7B shows an example of a process that follows process 700 shown in Figure 7A. The process shown in Figure 7B is a process for estimating the user's health level after a predetermined period of time has passed.

In step S705, the fourth acquisition means 142 of the processor unit 140 acquires a second user data set for the user's second parameter set. Step S705 is performed at least after a predetermined period has elapsed since step S702. The fourth acquisition means 142 can, for example, acquire the second user data set stored in the database unit 200 via the interface unit 110. Alternatively, the fourth acquisition means 142 can, for example, acquire the second user data set from the user's terminal device via the interface unit 110.

In step S706, the output generation means 143 of the processor unit 140 obtains a second output by inputting the second user data set into the health function. For example, if the health function is a regression model, the second user data set is input into the independent variables of the regression model, and coordinates on the health positioning map are output as the second output. For example, if the health function is a neural network model, the second user data set is input into the input layer of the neural network model, and coordinates on the health positioning map are output as the second output.

In step S707, the output mapping means 144 of the processor unit 140 maps the second output onto the health positioning map. Since the output obtained in step S706 is a coordinate on the health positioning map, the output mapping means 144 can map the coordinate within the n-dimensional space of the health positioning map.

Steps S705 to S707 allow an estimation of the user's health condition after a predetermined period of time. For example, by comparing the health condition estimated in steps S701 to S704 with the health condition estimated in steps S705 to S707, it is possible to identify changes in the health condition over time.

It is also possible to predict future health status based on the direction of time-series changes on the health positioning map. For example, if mapping the first output shows that the health level falls within an area characterized as healthy on the health positioning map, and mapping the second output after a predetermined period of time shows that the health level falls within an area characterized as healthy but is approaching an area characterized as a lifestyle-related disease risk group, it is possible to predict that the health status is moving in the direction of lifestyle-related disease risk.

In one embodiment, process 700 can be used to evaluate items for improving health (e.g., medicines, foods, beverages, health equipment, etc.). For example, by having a user use an item for improving health for a predetermined period of time and comparing a first output before the predetermined period with a second output after the predetermined period has elapsed, time series changes on the health positioning map can be identified. The time series changes on the health positioning map due to the use of an item for improving health reflect the effectiveness of the item for improving health, and by comparing them with time series changes on the health positioning map when the item for improving health is not used, the effectiveness of the item for improving health can be evaluated.

Furthermore, items for improving health status can be recommended to the user based on the direction of time-series changes on the health positioning map due to the use of items for improving health status. For example, items for improving health status are generally effective for users whose time-series changes on the health positioning map are in the opposite direction to the direction of time-series changes on the health positioning map due to the use of items for improving health status. Therefore, items whose time-series changes are in the opposite direction to the time-series changes on the health positioning map of the user identified by the processing of steps S701 to S707 can be recommended to the user.

Furthermore, it is possible to predict future health status based on the trajectory of time-series changes on a health positioning map. For example, a subject's health can be evaluated by pattern matching a trajectory that includes a first position resulting from mapping a first output and a second position resulting from mapping a second output after a predetermined period of time has elapsed. This can be achieved, for example, by learning the trajectory patterns of healthy individuals and those of subjects at risk for a particular disease.

In one embodiment, process 700 can be used to evaluate the impact of a stimulus on a health condition. For example, by providing a stimulus to a user for a predetermined period of time and comparing a first output before the predetermined period of time with a second output after the predetermined period of time has elapsed, a time series change on the health positioning map can be identified. The time series change on the health positioning map due to the stimulus reflects the impact of the stimulus on the health condition, and by comparing it with the time series change on the health positioning map when the stimulus is not provided, the impact of the stimulus on the health condition can be evaluated.

Here, stimuli include, but are not limited to, tactile stimuli, olfactory stimuli, visual stimuli, gustatory stimuli, auditory stimuli, and stimuli from the external environment. Tactile stimuli may include, for example, massage, acupressure, vibration, etc. Olfactory stimuli may include, for example, smelling a particular odor. Gustatory stimuli may include, for example, eating sweet foods, sour foods, salty foods, bitter foods, umami foods, etc. Visual stimuli may include, for example, viewing a particular still image, viewing a particular video, viewing a particular color, viewing an object under lighting of a particular brightness, etc. Auditory stimuli may include, for example, listening to particular music, listening to noise, listening to high-pitched sounds (e.g., sounds above approximately 1000 Hz), listening to low-pitched sounds (e.g., sounds below approximately 100 Hz), etc. Stimuli from the external environment can include, for example, changes in the external environment, the presence of substances in the external environment (e.g., viruses, allergens, NOx, etc.), and the like.

Furthermore, stimuli for improving the user's health status can be recommended to the user based on the direction of time-series changes on the health positioning map due to the application of the stimuli. For example, stimuli for improving the user's health status are generally effective for users whose time-series changes on the health positioning map are in the opposite direction to the direction of time-series changes on the health positioning map due to the application of the stimuli. Therefore, stimuli that have time-series changes in the opposite direction to the time-series changes on the health positioning map of the user identified by the processing of steps S701 to S707 can be recommended to the user.

In one embodiment, process 700 can be used to evaluate the impact of an event on a health condition. For example, by comparing a first output before the event with a second output after the event, a time series change on the health positioning map can be identified. The time series change on the health positioning map due to experiencing the event reflects the impact of the event on the health condition, and the impact of the event on the health condition can be evaluated by comparing it with the time series change on the health positioning map if the event were not experienced. Here, events include, but are not limited to, infectious disease outbreaks (such as COVID-19), natural disasters (such as damage caused by storms, heavy rain, heavy snow, floods, storm surges, earthquakes, tsunamis, volcanic eruptions, and other abnormal natural phenomena), overtime work, experiences (direct experiences or simulated experiences (such as experiences via VR (virtual reality) or AR (augmented reality)), communication with other people or animals, etc.).

Figure 8 shows an example of data flow in a method implemented in the computer system 10 of the present invention. Figure 8 shows the flow of data between a measuring device, a terminal device 300, and a server device 100 in a method for measuring the health of a subject.

In step S801, the measurement device acquires a data set for a parameter set. Here, the parameter set corresponds to the second parameter set described above. The parameter set may include autonomic nervous parameters, body composition parameters, and questionnaire parameters.

In one embodiment, step S801 includes:
I. Obtaining data on autonomic parameters using an autonomic nervous system measuring device;
II. Obtaining data for body composition parameters using a body composition monitor; and III. Obtaining data for questionnaire parameters using a questionnaire processing device.

In this case, the autonomic nervous system measuring device, body composition meter, and questionnaire processing device may each be separate devices, may each be implemented by the same device, or at least two of them may be implemented by the same device. Alternatively, at least two of the autonomic nervous system measuring device, body composition meter, and questionnaire processing device may be implemented by the terminal device 300. In this embodiment, in step S801, a data set is acquired using at most three measuring devices, for example, a data set is acquired using two measuring devices, or for example, a data set is acquired using one measuring device. The fewer measuring devices used, the lighter the burden on the user for measurement.

In another embodiment, step S801 includes:
I. Obtaining data on autonomic parameters using an autonomic nervous system measuring device;
II. Obtaining data for body composition parameters using a body composition analyzer; III. Obtaining data for questionnaire parameters using a questionnaire processing device;
IV. Obtaining data for blood pressure parameters using a sphygmomanometer.

In this case, the autonomic nervous system measuring device, body composition monitor, questionnaire processing device, and blood pressure monitor may each be separate devices, may each be implemented by the same device, or at least two of them may be implemented by the same device. Alternatively, at least two of the autonomic nervous system measuring device, body composition monitor, questionnaire processing device, and blood pressure monitor may be implemented by the terminal device 300. In this embodiment, in step S801, a data set is acquired using at most four measuring devices, for example, a data set is acquired using three measuring devices, for example, a data set is acquired using two measuring devices, or for example, a data set is acquired using one measuring device. The fewer measuring devices used, the lighter the burden on the user for measurement.

Acquiring a dataset for a parameter set may be performed consciously or unconsciously by the subject. Acquiring a dataset for a parameter set can be performed automatically, without the subject's awareness, for example, when a predetermined condition is met. The predetermined condition may include, for example, a temporal condition (e.g., between 6:00 AM and 8:00 AM, between 11:00 AM and 1:00 PM, between 4:00 PM and 6:00 PM, between 9:00 PM and midnight, or at 6:00 AM, at midnight, at 6:00 PM, at midnight, or every hour, every three hours, every six hours, or every twelve hours), a geographical condition (e.g., when leaving work or entering home), or a behavioral condition (e.g., sitting in a chair, lying in bed, or being within the camera's field of view for a certain period of time).

In step S802, the measuring device transmits the data set acquired in step S801 to the terminal device 300, and the terminal device 300 receives the data set. The processor unit 350 of the terminal device 300 acquires the data set via the interface unit 310.

In one embodiment, the processor section 350 includes:
I. Obtaining data for autonomic parameters from an autonomic nervous system measuring device;
II. Obtaining data for body composition parameters from a body composition analyzer;
III. Obtaining data for questionnaire parameters from questionnaire processing equipment. A data set can be obtained by:

In another embodiment, the processor section 350
I. Obtaining data for autonomic parameters from an autonomic nervous system measuring device;
II. Obtaining data for body composition parameters from a body composition analyzer;
III. Obtaining data for questionnaire parameters from a questionnaire processing device;
IV. A data set can be obtained by obtaining data for blood pressure parameters from a sphygmomanometer.

The timing at which step S802 is performed after step S801 does not matter. For example, the processor unit 350 of the terminal device 300 may acquire a data set in step S802 each time a data set for one parameter in the parameter set is acquired in step S801, or the processor unit 350 of the terminal device 300 may acquire a data set in step S802 after data sets for all parameters in the parameter set have been acquired in step S801.

In an embodiment in which the measuring equipment is implemented by a terminal device 300, step S802 may be omitted.

In step S803, the processor unit 350 of the terminal device 300 transmits the data set to the server device 100 via the interface unit 310, and the server device 100 receives the data set. The processor unit 140 of the server device 100 acquires the data set via the interface unit 100.

The timing at which step S803 is performed after step S802 does not matter. For example, the processor unit 350 of the terminal device 300 may transmit the data set in step S803 each time a data set for one parameter in the parameter set is acquired in step S802, or the processor unit 350 of the terminal device 300 may transmit the data set in step S803 after data sets for all parameters in the parameter set have been acquired in step S802.

For example, when transmitting images acquired for analysis to derive autonomic nervous parameters to the server device 100, it is preferable to transmit the images to the server device 100 each time they are acquired, or separately from data sets of other parameters. This is because images have a large data volume, which can lead to communication delays.

In step S804, the processor unit 140 of the server device 100 derives health information using a health function that correlates the dataset with the subject's position on the health positioning map. The health information includes coordinates on the health positioning map (e.g., X-axis value, Y-axis value). The processor unit 140 can derive the health information, for example, by processing steps S701 to S703 of process 700.

In step S805, the processor unit 140 of the server device 100 transmits health level information to the terminal device 300 via the interface unit 110, and the terminal device 300 receives the health level information. The processor unit 350 of the terminal device 300 receives the health level information via the interface unit 310. In an embodiment in which the above-mentioned measuring device is implemented by the terminal device 300, the processor unit 140 of the server device 100 transmits health level information via the interface unit 110 to the measuring device that performed the measurement in step S801, and the measuring device receives the health level information (e.g., coordinates on a health level positioning map (e.g., X-axis value, Y-axis value)).

In step S806, the processor unit 350 of the terminal device 300 represents the subject's health level as a position on a health level positioning map of the health level information. The health level information can be displayed on the health level positioning map based on the X-axis value and Y-axis value included in the health level information. The processor unit 350 can, for example, display the health level positioning map on the display unit 330 and plot the coordinates indicated by the health level information on the displayed health level positioning map. In an embodiment in which the above-mentioned measuring device is implemented by the terminal device 300, the measuring device that performed the measurement in step S801 will display the health level information on the health level positioning map based on the X-axis value and Y-axis value included in the health level information.

Because the data obtained by the measurement in step S801 does not require time for analysis, the process from the time the terminal device 300 receives the data set (or from the time the terminal device 300 transmits the data set to the server device 100 in step S803) to the time the subject's health level is represented as a position on the health level positioning map in the health level information in step S806 can be completed within a predetermined time. The predetermined time can be, for example, 0.01 seconds, 0.02 seconds, 0.05 seconds, 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second, 3 seconds, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 45 seconds, 1 minute, 5 minutes, 10 minutes, 1 hour, 2 hours, 12 hours, 24 hours, etc. This allows the user to immediately know their health status. This excellent responsiveness reduces the psychological burden of measurement on the user and prevents a decline in the user's motivation to measure.

Furthermore, in steps S805 and S806, the health information transmitted from the server device 100 can be configured to include at least coordinates on the health positioning map (e.g., X-axis value, Y-axis value), thereby further shortening the specified time. For example, the processor unit 350 of the terminal device 300 that receives the health information can display the health information on the health positioning map simply by plotting the coordinates on the positioning map, thereby simplifying processing on the terminal device 300 and shortening the time from receipt to display of the health information. For example, the amount of data transferred for the transmitted health information can be reduced, thereby preventing a decrease in communication speed. This allows the health information to be transmitted at high speed, thereby reducing data transfer time and shortening the time required to transmit the health information.

The health level information received in step S805 and/or the position on the health level positioning map represented in step S806 can be stored in the memory unit 340 of the terminal device 300.

In one embodiment, the health information received in step S805 and/or the position on the health positioning map represented in step S806 can be used, for example, to correct other data. The other data can be, for example, data that can vary depending on health status. For example, the other data can include, but is not limited to, test results, including cognitive ability tests, physical fitness tests, academic achievement tests, etc. For example, a correction can be made to add points to the test results of a subject whose test results are poor because their health status is worse than normal. For example, in order to minimize the impact of the subject's health status, a correction can be made to add points to the test results of a subject whose health status is relatively poor among multiple subjects.

In one embodiment, the location on the health positioning map represented in step S806 can be used, for example, to control other devices connectable to the network. Here, the other devices may be electrical appliances connectable to the network (so-called "IoT devices").

For example, the processor unit 350 of the terminal device 300 can generate instructions for controlling a device based on its location on the health positioning map. The processor unit 350 of the terminal device 300 can then control the device in accordance with the instructions by transmitting the instructions to the device via a network.

Alternatively, the processor unit 350 of the terminal device 300 transmits the position on the health positioning map to the device via the network. The device can then generate instructions for controlling the device based on the position on the health positioning map, and control the operation of the device in accordance with the instructions.

Here, the device is, for example, an air conditioner. The air conditioner can be controlled to maintain an appropriate indoor temperature and/or humidity depending on the position on the health positioning map. The device is, for example, a light. The light can be controlled to maintain an appropriate brightness depending on the position on the health positioning map. The device is, for example, a music player. The music player can be controlled to play appropriate music (e.g., music that relaxes the mind and body, music that increases motivation, etc.) depending on the position on the health positioning map. The device is, for example, a video player. The video player can be controlled to play appropriate videos (e.g., videos that relax the mind and body, videos that increase motivation, etc.) depending on the position on the health positioning map.

As described above, the control of such other devices can be made even more useful by ensuring that it is carried out within a predetermined time from when the terminal device 300 receives the data set (or when the terminal device 300 transmits the data set to the server device 100 in step S803) until the subject's health level is expressed as a position on the health level positioning map of health level information in step S806. In other words, by obtaining the position on the health level positioning map used for control within a predetermined time, particularly in approximately real time, it becomes possible to achieve real-time control according to the subject's current health.

In one embodiment, if the user of the terminal device 300 gives consent, the health information derived in step S804 is stored in the database unit 200 and made available for research use by other users.

By repeating steps S801 to S805 at least twice, at least two positions on the health positioning map can be obtained.

In step S806, the subject's health can be evaluated based on a trajectory that includes at least two obtained positions. For example, the subject's health can be evaluated by pattern matching a trajectory that includes a first position obtained in steps S801 to S805 the first time and a second position obtained in steps S801 to S805 the second time after a predetermined period has elapsed. This can be achieved, for example, by learning the trajectory patterns of healthy individuals and the trajectory patterns of subjects at risk for a particular disease.

Here, the specified period can be any period. The specified period can be, for example, half a day, one day, one week, two weeks, one month, three months, six months, one year, etc. For example, if the specified period is half a day, the health level of the subject will be evaluated based on the diurnal fluctuations in the subject's health level. This can provide, for example, an indicator of the degree of fatigue accumulated due to daily activities. For example, if the specified period is the amount of sleep, the health level of the subject will be evaluated based on the night-time fluctuations in the subject's health level. This can provide, for example, an indicator of the degree of recovery achieved through nighttime sleep.

In the above example, in step S802, the measuring device transmits the data set acquired in step S801 to the terminal device 300, and in step S803, the processor unit 350 of the terminal device 300 transmits the data set to the server device 100 via the interface unit 310. However, the present invention is not limited to this. For example, if the measuring device is a server-linked type, the measuring device can transmit the data set directly to the server device 100. Therefore, step S802 can be omitted.

In one embodiment, in step S806, an item for improving health (e.g., medicine, food, beverage, health equipment, etc.) can be evaluated based on a trajectory including at least two obtained positions. For example, by comparing a first position obtained in steps S801 to S805 in the first iteration with a second position obtained in steps S801 to S805 in the second iteration after the user has used the item for improving health for a predetermined period of time, it is possible to identify time series changes on the health positioning map. The time series changes on the health positioning map resulting from the use of the item for improving health reflect the effectiveness of the item for improving health, and the effectiveness of the item for improving health can be evaluated by comparing them with time series changes on the health positioning map in the case where the item for improving health was not used.

Furthermore, items for improving health status can be recommended to the user based on the direction of time-series changes on the health positioning map due to the use of items for improving health status. For example, items for improving health status are generally effective for users whose time-series changes on the health positioning map are in the opposite direction to the direction of time-series changes on the health positioning map due to the use of items for improving health status. Therefore, items whose time-series changes are in the opposite direction to the time-series changes on the health positioning map of the user identified by the processing of step S806 can be recommended to the user.

In one embodiment, step S806 may be used to evaluate the impact of the stimulus on the health status. For example, by comparing a first position obtained in steps S801 to S805 for the first time with a second position obtained in steps S801 to S805 for the second time after providing the stimulus to the user for a predetermined period of time, it is possible to identify time series changes on the health status positioning map. The time series changes on the health status positioning map due to the application of the stimulus reflect the impact of the stimulus on the health status, and by comparing them with the time series changes on the health status positioning map when the stimulus is not provided, the impact of the stimulus on the health status can be evaluated.

Here, stimuli include, but are not limited to, tactile, olfactory, visual, gustatory, and auditory stimuli. Tactile stimuli may include, for example, massage, acupressure, vibration, etc. Olfactory stimuli may include, for example, smelling a particular odor. Gustatory stimuli may include, for example, eating sweet foods, sour foods, salty foods, bitter foods, umami foods, etc. Visual stimuli may include, for example, viewing a particular still image, viewing a particular video, viewing a particular color, viewing an object under lighting of a particular brightness, etc. Auditory stimuli may include, for example, listening to particular music, listening to noise, listening to high-pitched sounds (e.g., sounds above approximately 1000 Hz), listening to low-pitched sounds (e.g., sounds below approximately 100 Hz), etc.

Furthermore, stimuli for improving the user's health status can be recommended to the user based on the direction of time-series changes on the health positioning map due to the application of the stimuli. For example, stimuli for improving the user's health status are generally effective for users whose time-series changes on the health positioning map are in the opposite direction to the direction of time-series changes on the health positioning map due to the application of the stimuli. Therefore, stimuli that have time-series changes in the opposite direction to the time-series changes on the health positioning map of the user identified by the processing of steps S701 to S707 can be recommended to the user.

In one embodiment, step S806 may be used to evaluate the impact of an event on health status. For example, by comparing a first position obtained in steps S801 to S805 before the event with a second position obtained in steps S801 to S805 after the event, time-series changes on the health positioning map can be identified. The time-series changes on the health positioning map due to experiencing the event reflect the impact of the event on health status, and the impact of the event on health status can be evaluated by comparing them with the time-series changes on the health positioning map in the absence of the event. Here, events include, but are not limited to, infectious disease outbreaks (such as COVID-19), natural disasters (such as storms, heavy rain, heavy snow, floods, storm surges, earthquakes, tsunamis, volcanic eruptions, and other damage caused by abnormal natural phenomena), overtime work, experiences (direct or simulated experiences (such as experiences via virtual reality (VR) or augmented reality (AR))), communication with other people or animals, etc.).

In the above-described embodiment of controlling a device, step S806 can be used to evaluate the impact of controlling the device on the health status and further control the device taking that evaluation into account. For example, a second command for controlling the device can be generated based on the first position obtained in steps S801 to S805 the first time and the second position obtained in steps S801 to S805 the second time after the device has been controlled based on the first position. For example, by comparing the first position with the second position, time series changes on the health positioning map can be identified. The time series changes on the health positioning map due to controlling the device reflect the impact of controlling the device on the health status. For example, if controlling the device has a positive impact on the health status, a second command can be generated to continue controlling the device, and the device can be controlled in accordance with the second command. For example, if controlling the device has a negative impact on the health status, a second command can be generated to stop controlling the device or to control in the opposite direction, and the device can be controlled in accordance with the second command.

For example, if the device is an air conditioner, the air conditioner can be controlled to maintain an appropriate indoor temperature according to the first position obtained in steps S801 to S805 for the first time, and then if the second position obtained in steps S801 to S805 for the second time (1) moves to a position with a worse health condition than the first position, the air conditioner can be controlled to increase or decrease the indoor temperature, or (2) moves to a position with a better health condition than the first position, the air conditioner can be controlled to maintain the indoor temperature.

For example, if the device is a light, the light can be controlled to maintain an appropriate brightness depending on the first position obtained in steps S801 to S805 the first time, and then if the second position obtained in steps S801 to S805 the second time is (1) moved to a position with a worse health condition than the first position, the light can be controlled to brighten or dim the brightness, or (2) moved to a position with a better health condition than the first position, the light can be controlled to maintain the brightness.

In the examples described above with reference to Figures 5A, 5B, 6, 7A, 7B, and 8, it was described that processes were performed in a specific order, but the order of each process is not limited to that described and may be performed in any order that is logically possible.

In the examples described above with reference to Figures 5A, 5B, 6, 7A, and 7B, it was explained that the processing of each step shown in Figures 5A, 5B, 6, 7A, and 7B is implemented by processor unit 120, processor unit 130, or processor unit 140 and a program stored in memory unit 150, but the present invention is not limited to this. At least one of the processing of each step shown in Figures 5A, 5B, 6, 7A, and 7B may be implemented by a hardware configuration such as a control circuit. Alternatively, at least one of the steps shown in Figures 5A, 5B, 6, 7A, and 7B may be performed by a person using a computer system or measuring equipment.

In the example described above with reference to Figure 8, it was explained that some of the processing of each step shown in Figure 8 is realized by the processor unit 350 of the terminal device 300 and a program stored in the memory unit 340, but the present invention is not limited to this. At least one of the processing of each step shown in Figure 8 may be realized by a hardware configuration such as a control circuit.

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

(Example 1. Creation of a health positioning map)
In this example, initial data on 232 health-related items were first acquired for 720 subjects. This initial data acquisition was carried out at Kobe Riken IIB Building. These 232 items included both invasive and non-invasive tests. Next, the acquired initial data values were corrected so that the mean value was 0 and the standard deviation was 1. Next, a portion of the corrected data was extracted using a method that extracts only one piece of data if the correlation between the data was higher than a predetermined value (specifically, a correlation coefficient of 0.9). This portion of the data included data on the four basic parameters. Note that in this example, one piece of data was extracted if the correlation coefficient was higher than 0.9, but the present invention is not limited to this, and the correlation coefficient value can be changed as appropriate. Note that by setting the correlation coefficient to 0.9 or higher, a health function can be created that can more comprehensively calculate the subject's health.

As a result of the extraction, 81 items were extracted. These 81 items correspond to the "first parameter set" of the present invention. These 81 items included four basic parameters, fundamental parameters, cognitive function parameters, subjective parameters, blood parameters, vascular and skin parameters, and lifestyle parameters.

Next, multidimensional scaling was used to reduce the multidimensional data (i.e., 81-dimensional data) to two dimensions, and data for 692 of the 720 individuals for whom complete data was available was plotted on two dimensions. The plot distribution patterns of the 692 plots plotted on two dimensions were clustered using k-means, resulting in 10 clusters (Figure 9B). Health-related information was assigned to each cluster, and a health positioning map of the present invention was created. Figure 10 shows the health positioning map created in this example. Note that while Figure 10 also shows the actual plots, the health positioning map of the present invention does not necessarily include the original plots; it merely needs to include the areas identified by the clustering of the plots and the health information associated with those areas.

(Example 2. Creation of another health positioning map)
In this example, initial data on 242 health-related items was first obtained for 1,000 subjects. These 242 items included both invasive and non-invasive tests. Next, machine learning was performed using a trained model that had learned the influence of the 242 data items on the health positioning map to extract items with a relatively high influence on the health positioning map.

As a result of the extraction, 69 items were extracted. These 69 items correspond to the "first parameter set" of the present invention. These 69 items included 30 blood parameters and SOS. These 69 items were items that could be measured through seven measurements. Next, using the extracted 69 items, a health positioning map of the present invention was created using the same method as in Example 1.

(Example 3. Creation of health function)
A function (health function) that allows appropriate placement on the health positioning map created in Example 1 using a smaller number of test items (second parameter set) than the first parameter set was identified by machine learning.

As described above, Figure 10 is a health positioning map for this example. Analysis of the subjects corresponding to each plot revealed that, as shown in Figure 10, the plots on the left side of Figure 10 (i.e., the -X-axis side) were plots of relatively younger subjects, while the plots on the right side (i.e., the +X-axis side) were plots of relatively older subjects. The group of subjects included in group G1 in Figure 10 were young, had high levels of depression and anxiety, poor autonomic nervous system regulation, and made a high number of errors in cognitive tasks. Therefore, if a subject whose data has been newly measured belongs to group G1, it can be seen that the subject is likely to be a subject who is not yet suffering from a mental health disorder.

Furthermore, the subjects included in group G2 were older and had higher blood γ-GTP, blood ALT, blood triglycerides, blood HbA1c, and blood high-sensitivity CRP levels. Therefore, if a subject whose data has been newly measured belongs to group G2, it can be seen that the subject is likely to be a subject who is not yet suffering from a lifestyle-related disease.

Furthermore, the subjects in group G3 were older and had higher blood glucose levels. Therefore, if a subject whose data has been newly measured belongs to group G3, it can be seen that the subject is highly likely to be in the pre-diabetic state.
The region where the first function X≦approximately 4 and the second function Y≦approximately 2 indicates a healthy group with generally no health problems.

The above groups G1 to G3 are just a few examples that can be evaluated using the health evaluation device of the present invention, and various other health risks can also be evaluated.

Example 4. Observation of changes in health status using a health positioning map
We investigated whether the health positioning map created in Example 1 could be used to observe changes in the subject's health status. We compared the mapping positions on the health positioning map before and after taking reduced CoQ10 for three months. The results are shown in Figure 11.

As is clear from Figure 11, the subjects in the comparison were observed to have shifted from Group G1, a mental health risk group, to a healthy group. Close examination of each test item for this subject revealed an increase in total blood CoQ10 and a decrease in fatigue and depression-related parameters: fatigue VAS, depression VAS, ChaTF11G, and PS (Figure 12). These results suggest that reduced CoQ10 may be effective in relieving fatigue and improving mental health, and confirm that the health positioning map of the present invention and the health assessment method using it can be used to track the health status of subjects.

(Example 5. Study of second parameter set for simplified measurement)
We attempted to use machine learning to identify a function that would allow appropriate placement on the health positioning map using a second set of parameters measured using measuring devices available at home.

A health positioning map was created using a first parameter set of 76 items from the data of 965 subjects. The data from half of the subjects was used as training data, and the data from the other half was used to evaluate the resulting function. A health function was created using a second parameter set of 19 items from four measurements (body composition measurement, blood pressure measurement, autonomic nervous function measurement, questionnaire measurement), and a health function was created using a second parameter set of 18 items from three measurements (body composition measurement, autonomic nervous function measurement, questionnaire measurement), and the prediction accuracy was verified.

The second set of 19 parameters from four measurements included two body composition parameters, one blood pressure parameter, five autonomic nervous system parameters, and 11 questionnaire parameters.

The second set of 18 parameters from three measurements included two body composition parameters, five autonomic nervous system parameters, and 11 questionnaire parameters.

As a result, the health function based on the second parameter set of 19 items from four measurements achieved a prediction accuracy of R = 0.9778 on the X axis and R = 0.9556 on the Y axis (Figure 13). Furthermore, the health function based on the second parameter set of 18 items from three measurements achieved a prediction accuracy of R = 0.9774 on the X axis and R = 0.9532 on the Y axis (Figure 13). It was unexpected that health status could be assessed with such high accuracy even using items measured using simple measuring equipment.

Furthermore, these measurement items do not include bone mineral density parameters or cognitive function parameters, and are items that can be measured using at most four measuring devices, preferably at most three measuring devices, and even more preferably one measuring device (e.g., a smartphone). In other words, using these measurement items can reduce the measurement burden on the subject and enable easy measurement at any time and any location, including locations other than testing facilities such as the subject's home.

(Example 6. Analysis using WH ratio)
As in Example 5, a health positioning map was created from the data of 965 subjects using the first parameter set of 76 items. Under the same conditions as in Example 5, the plot distribution patterns of the 965 plots plotted in two dimensions were clustered using the k-means method, and the results were clustered into 10 clusters.

Figure 14(a) shows data for 965 individuals plotted and clustered on a health positioning map. The horizontal axis is associated with physical health, with larger values on the horizontal axis indicating poorer physical health, while the vertical axis is associated with mental health, with larger values on the vertical axis indicating poorer mental health. Of the 10 clusters, the third cluster was assessed as a mental health disease risk group, the tenth cluster as a lifestyle-related disease risk group, and the sixth and ninth clusters as lifestyle-related disease and mental health disease risk groups.

The data for the 965 subjects included WH ratio and BMI. In particular, WH ratio and BMI were calculated from data obtained from a body composition analyzer (InBody Japan, Inc.). Using these WH ratios and BMI, subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) were identified from the data for the 965 subjects. Obese individuals with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) are known as having apple-shaped obesity (abdominal obesity), an obese body type that is prone to various complications. Figure 14(b) highlights the data for subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) on a health positioning map. 14(b), data of subjects with a BMI of 25 or more and a WH ratio of 1.0 or more (male) or 0.9 or more (female) are represented by black squares, and data of other subjects are represented by white circles. Note that the first parameter set for creating the health positioning map in this example included BMI but did not include WH ratio.

Figure 14(c) shows the results of an analysis of which of the clusters 1 to 10 shown in Figure 14(a) each subject with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) belonged to. The percentage of subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) is represented by solid black, while the percentage of other subjects is represented by diagonal lines. Subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) were most commonly classified into the ninth cluster, with nearly 40% of subjects having a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women). As described above, the ninth cluster is the cluster evaluated as a risk group for lifestyle-related diseases and mental health disorders. Next, cluster 10 and cluster 8 contained many individuals with a BMI of 25 or more and a WH ratio of 1.0 or more (men) or 0.9 or more (women), followed by cluster 6.

It is well known that obese individuals with apple-shaped obesity (abdominal obesity) (i.e., subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women)) have poorer physical health. The results shown in Figure 14(c) are consistent with this finding. Subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women) were more likely to be in clusters with poor physical health (e.g., clusters 6-10) than in clusters with good physical health (e.g., clusters 1-5). Since the results of the health positioning map created in this example are consistent with the facts regarding obese individuals with apple-shaped obesity (abdominal obesity), it is believed that the health positioning map created in this example can appropriately represent the health of subjects.

Furthermore, the health positioning map created in this example could be used for analysis using a parameter (WH ratio) that was not used in the first parameter set for creating the health positioning map. This suggests that the second parameter for simple measurement does not necessarily have to be part of the first parameter, and that analysis is possible even if the second parameter for simple measurement is not part of the first parameter.

Furthermore, as shown in Figure 14(c), a tendency was observed in the cluster of subjects with both poor physical and mental health to have a high proportion of subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (men) or 0.9 or higher (women). The fact that apple-shaped obesity (abdominal obesity) can be related not only to physical health but also to mental health was a new and unexpected finding. Based on this new finding, for example, based solely on the appearance of their body shape or the results of body composition measurements, subjects with apple-shaped obesity (abdominal obesity) can be made aware that they may have poor not only physical health but also mental health, and encouraged to improve their mental health as well.

100 Computer system 110 Interface unit 120, 130, 140 Processor unit 150 Memory unit 200 Database unit 300 User terminal device 400 Network

Claims (20)

1. A method for measuring a subject's health, the method being executed on a computer device having a processor, the method comprising:
Step (1): The processor acquires a data set for a parameter set, the parameter set including an autonomic nervous parameter, a body composition parameter, and a questionnaire parameter, and step (1) includes:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Obtaining data for said questionnaire parameters from a questionnaire processing device for measurement of said questionnaire parameters;
and
Step (2): The processor transmits the dataset to a server device;
Step (3): The processor receives health level information derived based on the data set from the server device;
Step (4): The processor represents the health level as a position on a health level positioning map of the health level information , and step (1) includes automatically acquiring other data from among the autonomic nervous parameters, the body composition parameters, and the questionnaire parameters in response to acquiring at least one of the data from the autonomic nervous parameters, the body composition parameters, and the questionnaire parameters . The method of claim 1, wherein the parameter set further includes a blood pressure parameter. The step (1)
IV. Obtaining data for said blood pressure parameter from a sphygmomanometer;
The method of claim 2 , comprising: The method of any one of claims 1 to 3, wherein step (1) comprises obtaining the data sets from at most four measurement instruments. The method of claim 4, wherein each of the at most four measuring devices is portable or handheld. The method of any one of claims 1 to 3, wherein step (1) includes acquiring the data set from one measurement device. A method according to any one of claims 1 to 6, wherein the health information includes an X-axis value and a Y-axis value on the health positioning map, and step (4) includes displaying the health information on the health positioning map based on the X-axis value and the Y-axis value. Step (5): The processor repeats steps (1) to (4) at least twice to determine a first position and a second position on the health positioning map;
The method of any one of claims 1 to 7 , further comprising: step (6): the processor assessing the health of the subject based on a trajectory including the first position and the second position. 9. The method of claim 8 , wherein step (5) comprises repeating steps (1) to (4) at least twice during one day. 10. The method of claim 1, wherein the server device is configured to derive the health information using a health function that correlates the data set with the subject 's position on the health positioning map. 11. The method of claim 1, wherein the time at which the data for the autonomic parameters, the time at which the data for the body composition parameters, and the time at which the data for the questionnaire parameters were acquired are each within about 5 minutes. The method according to any one of claims 1 to 11 , wherein steps (2) to (4) are performed within a predetermined time. The method of claim 12 , wherein the predetermined time is about 1 minute. the health positioning map is created using a parameter set for creating a health positioning map;
The method according to any one of claims 1 to 13 , wherein the parameter set is part of a parameter set for generating the health positioning map. 1. A method for controlling an apparatus in response to a health level of a subject, the method being executed on a computer device including a processor, the method comprising:
Step (1): The processor acquires a data set for a parameter set, the parameter set including an autonomic nervous parameter, a body composition parameter, and a questionnaire parameter, and step (1) includes:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Obtaining data for said questionnaire parameters from a questionnaire processing device for measurement of said questionnaire parameters;
and
Step (2): The processor transmits the dataset to a server device;
Step (3): The processor receives health level information derived based on the data set from the server device;
Step (4): The processor expresses the health level as a position on a health level positioning map of the health level information.
Includes
Step (5): The processor generates an instruction to control the device based on the position of the health information on a health positioning map;
Step (6): The processor controls the device in accordance with the instructions. After controlling the device,
the processor repeating steps (1) to (4) to determine a second position on the health positioning map;
The method of claim 15 , further comprising: the processor generating second instructions for controlling the device based on the location and the second location. An apparatus for measuring the health of a subject, comprising:
An acquisition means for acquiring a data set for a parameter set, the parameter set including autonomic nerve parameters, body composition parameters, and questionnaire parameters, the acquisition means comprising:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Acquiring data for the questionnaire parameters from a questionnaire processing device for measurement of the questionnaire parameters;
a transmitting means for transmitting the data set to a server device;
a receiving means for receiving health level information derived based on the data set from the server device;
and a representation means for representing the health degree as a position on a health degree positioning map of the health degree information , wherein the acquisition means is configured to automatically acquire other data from among the data on the autonomic nervous parameters, the data on the body composition parameters, and the data on the questionnaire parameters in response to acquiring at least one of the data on the autonomic nervous parameters, the data on the body composition parameters, and the data on the questionnaire parameters . A program for measuring the health of a subject, the program being executed on a computer device, the program comprising: step (1): acquiring a data set for a parameter set, the parameter set including autonomic nervous parameters, body composition parameters, and questionnaire parameters, and step (1) comprising:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Obtaining data for said questionnaire parameters from a questionnaire processing device for measurement of said questionnaire parameters;
and
Step (2): transmitting the data set to a server device;
Step (3): receiving health level information derived based on the data set from the server device;
Step (4): Expressing the health degree as a position on a health degree positioning map of the health degree information , wherein step (1) includes automatically acquiring other data from among the autonomic nervous parameters, the body composition parameters, and the questionnaire parameters in response to acquiring at least one of the data from among the autonomic nervous parameters, the body composition parameters, and the questionnaire parameters . An apparatus for controlling equipment according to the health status of a subject,
An acquisition means for acquiring a data set for a parameter set, the parameter set including autonomic nerve parameters, body composition parameters, and questionnaire parameters, the acquisition means comprising:
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Obtaining data for said questionnaire parameters from a questionnaire processing device for measurement of said questionnaire parameters;
an acquiring means configured to:
a transmitting means for transmitting the data set to a server device;
a receiving means for receiving health level information derived based on the data set from the server device;
a representation means for representing the health level as a position on a health level positioning map of the health level information;
a control means for generating a command for controlling the device based on a position on a health positioning map of the health information, and controlling the device in accordance with the command;
An apparatus comprising: A program for controlling an apparatus in accordance with a health level of a subject, the program being executed on a computer device, the program
Step (1): Acquiring a data set for a parameter set, the parameter set including autonomic nervous parameters, body composition parameters, and questionnaire parameters, and step (1)
I. acquiring data on the autonomic nerve parameters from an autonomic nerve measuring device for measuring the autonomic nerve parameters;
II. Obtaining data for the body composition parameters from a body composition monitor;
III. Obtaining data for said questionnaire parameters from a questionnaire processing device for measurement of said questionnaire parameters;
and
Step (2): transmitting the data set to a server device;
Step (3): receiving health level information derived based on the data set from the server device;
Step (4): Expressing the health level as a position on a health level positioning map of the health level information.
Step (5): generating a command to control the device based on the position of the health information on a health positioning map;
Step (6): Controlling the device according to the command;
A program that causes the computer device to perform processing including the steps of: