WO2022014538A1 - Procédé, dispositif, programme et système de mesure du degré de santé d'un individu - Google Patents

Procédé, dispositif, programme et système de mesure du degré de santé d'un individu Download PDF

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Publication number
WO2022014538A1
WO2022014538A1 PCT/JP2021/026167 JP2021026167W WO2022014538A1 WO 2022014538 A1 WO2022014538 A1 WO 2022014538A1 JP 2021026167 W JP2021026167 W JP 2021026167W WO 2022014538 A1 WO2022014538 A1 WO 2022014538A1
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Prior art keywords
health
data
parameters
positioning map
measuring
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PCT/JP2021/026167
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English (en)
Japanese (ja)
Inventor
恭良 渡辺
敬 水野
慧嗣 久米
恭介 渡辺
裕紀 青山
晃裕 奥野
浩史 紫藤
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国立研究開発法人理化学研究所
株式会社Splink
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Priority to JP2022536350A priority Critical patent/JPWO2022014538A1/ja
Publication of WO2022014538A1 publication Critical patent/WO2022014538A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/60—Healthcare; Welfare

Definitions

  • the present invention relates to a method for measuring the health of a subject and the like.
  • Patent Document 1 the health condition of a subject is evaluated by acquiring data of a plurality of test items for a plurality of subjects and creating a function in which a plurality of test data among the test items are used as variables.
  • the technology is disclosed.
  • the applicant has developed a means for easily evaluating health risks at home, etc., starting from this invention. This is because the above-mentioned conventional technique requires an invasive test such as a blood test and cannot be easily performed at home or the like.
  • One aspect of the present invention is to provide a means for evaluating the overall health of a person by a simple test.
  • Step (1) Acquiring a data set for a parameter set, wherein the parameter set includes an autonomic nerve parameter, a body composition parameter, and a questionnaire parameter, and the step (1) is I. Acquiring data for the autonomic nerve parameters using the autonomic nerve measuring device for measuring the autonomic nerve parameters, and II. Acquiring data for the body composition parameters using a body composition analyzer, III. Acquiring data for the questionnaire parameters using the questionnaire processing device for measuring the questionnaire parameters, Including, and Step (2): Sending the data set to the server device and Step (3): Receiving health level information derived based on the data set from the server device.
  • Step (4) A method including expressing the health degree as a position on the health degree positioning map of the health degree information.
  • the method of item 1 wherein the parameter set further comprises a blood pressure parameter.
  • the step (1) is IV. Obtaining data for the blood pressure parameters using a sphygmomanometer, 2. The method according to item 2.
  • the method according to item 2. (Item 4) The method according to any one of items 1 to 3, wherein the step (1) includes acquiring the data set using at most four measuring instruments. (Item 5) 4. The method of item 4, wherein the at most four measuring instruments are portable or portable, respectively.
  • the health information includes an X-axis value and a Y-axis value in the health positioning map, and the step (4) is based on the X-axis value and the Y-axis value.
  • the method according to any one of items 1 to 6, wherein the degree information is shown on the health degree positioning map.
  • the measuring device used in the step (1) receives health information including the X-axis value and the Y-axis value in the health positioning map from the server device.
  • the measuring device used in the step (1) is displayed on the display unit of the measuring device based on the X-axis value and the Y-axis value.
  • Step (5) By repeating the steps (1) to (4) at least twice, the first position and the second position on the health positioning map are determined.
  • Step (6) The item according to any one of items 1 to 9, further comprising evaluating the health of the subject based on a locus including the first position and the second position.
  • Method. (Item 11) 10. The method of item 10, wherein step (5) comprises repeating steps (1)-(4) at least twice during the day.
  • step (1) automatically acquires at least a part of the data set without making the subject aware of it.
  • the step (1) in response to the acquisition of the data for the questionnaire parameter, the data for the autonomic nerve parameter and the data for the body composition parameter are automatically acquired without being conscious of the subject.
  • the method according to any one of items 1 to 12, including the above.
  • Item 14 Item 1-13, 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. The method described in any one of the items.
  • (Item 16) The method according to any one of items 1 to 15, wherein the steps (2) to (4) are performed within a predetermined time.
  • (Item 17) The method according to item 16, wherein the predetermined time is about 1 minute.
  • (Item 18) The health degree positioning map is created by using the parameter set for creating the health degree positioning map.
  • a method comprising controlling the device according to the command.
  • a device for measuring the health of a subject An acquisition means for acquiring a data set for a parameter set, wherein the parameter set includes an autonomic nerve parameter, a body composition parameter, and a questionnaire parameter. I. Acquiring data for the autonomic nerve parameters from the autonomic nerve measuring device for measuring the autonomic nerve parameters, and II. Obtaining data for the body composition parameters from the body composition analyzer and III. An acquisition means configured to acquire data for the questionnaire parameters from the questionnaire processing device for measuring the questionnaire parameters.
  • a program for measuring the health of a subject the program being executed on a computer device, the program being step (1): acquiring a data set for a parameter set, wherein the parameter set is.
  • the above step (1) includes autonomic nerve parameters, body composition parameters, and questionnaire parameters. I. Acquiring data for the autonomic nerve parameters from the autonomic nerve measuring device for measuring the autonomic nerve parameters, and II. Obtaining data for the body composition parameters from the body composition analyzer and III.
  • Step (2) Acquiring data for the questionnaire parameters from the questionnaire processing device for measuring the questionnaire parameters, and Including, and Step (2): Sending the data set to the server device and Step (3): Receiving health level information derived based on the data set from the server device.
  • Items 23 A system for measuring the health of a subject, the system including a terminal device and a server device.
  • the terminal device is Step (1): Obtaining a data set for the parameter set, Step (2): It is configured to send the data set to the server device.
  • the server device is Step (A): Deriving health information by using a health function that correlates the data set with the position on the health positioning map of the subject.
  • the terminal device is Step (3): Receiving the health information from the server device and Step (4): A system configured to further represent the health degree as a position on the health degree positioning map of the health degree information.
  • the present invention it is possible to provide a means for evaluating the overall health of a person by a simple test. As a result, it is possible to provide a technology that can measure the health condition at any time and track the change of the condition in the home / office environment, and a technology that can individually promote the development of solutions such as improvement of daily lifestyle using it. ..
  • the inventor of the present invention has developed a new service for visualizing user's health.
  • the service is a service that acquires various data on a user's health and provides the user with information on which area on the health positioning map the user's health is located based on the acquired data.
  • the health degree positioning map refers to a map having a plurality of areas characterized by information on health.
  • Each area of the health positioning map can be characterized, for example, as various states of health.
  • one area of the health positioning map may be characterized as a youth / mental health disease risk group, and another area of the health positioning map may be characterized as a middle-aged / lifestyle-related disease risk group.
  • yet another area of the health positioning map can be characterized as a geriatric / diabetes risk group.
  • one area of the health positioning map may be characterized as an unaffected group for one health condition, and another area may be characterized as a high-risk group for that health condition. It should be noted that these characterizations are examples, and various characterizations can be made. For example, as shown in FIG.
  • each region of the health positioning map can be determined, for example, by analyzing the tendency of multiple subjects belonging to each region.
  • the new service users can obtain information on which area on the health positioning map their health level is located from the results of simple measurements that can be performed at home.
  • the new service will also give users visual, real-time information about where their health is located on the health positioning map.
  • FIG. 1A shows an example of a flow of a new service for visualizing the health condition of a user.
  • step S1 user U performs some measurements at home H.
  • the measurement includes, for example, a measurement using the measuring device 20 and a measurement using the computer device 30.
  • the measuring device 20 is any measuring device that can be used at home H, and includes, for example, a body composition meter 21 and a sphygmomanometer 22, but is not limited thereto.
  • the computer device 30 is any computer device that can be used at home H, and includes, for example, a smartphone 31 and a personal computer 32, but is not limited thereto.
  • the measurement using the measuring device 20 includes the measurement using the body composition analyzer 21, whereby the data for the body composition is acquired.
  • the measurement using the measuring device 20 may further include the measurement using the sphygmomanometer 22, whereby data on the blood pressure is also acquired.
  • the measurement using the computer device 30 includes the measurement of the autonomic nerve function using a camera, whereby data for the autonomic nerve function is acquired.
  • the measurement using the computer device 30 includes the measurement using the questionnaire for the user U, whereby the data including the living condition and / or the subjective evaluation is acquired.
  • the sphygmomanometer may be a body-worn sphygmomanometer (eg, brachial sphygmomanometer, wristwatch sphygmomanometer, ring sphygmomanometer), or a non-contact sphygmomanometer (eg, a camera-photographed face or It can be a sphygmomanometer) that measures blood pressure from a finger image.
  • the body composition analyzer may be a type of body composition analyzer that is placed on the body at the time of measurement, or a type of body composition analyzer that can be worn on the body (for example, a wristwatch-type body composition analyzer).
  • four measurements are performed using three devices (body composition meter, sphygmomanometer, computer device 30), but the present invention is not limited thereto.
  • four measurements were performed using four devices (for example, body composition measurement using body composition analyzer 21, blood pressure measurement using blood pressure monitor 22, and autonomic nerve measuring device capable of measuring autonomic nerve function were used.
  • Autonomic nerve function measurement, questionnaire measurement using a questionnaire processing device that can acquire data from the questionnaire) may be performed, or four measurements may be performed using two devices (for example, body composition and body composition).
  • Body composition measurement and blood pressure measurement using a device capable of measuring blood pressure, autonomic nerve function measurement and questionnaire measurement using a computer device 30) may be performed, or four measurements may be performed using one device.
  • measurement using a device capable of measuring body composition, blood pressure, autonomic function, and questionnaire data may be performed.
  • three measurements are performed using three devices (for example, body composition measurement using a body composition analyzer 21, autonomic nerve function measurement using an autonomic nerve measuring device capable of measuring autonomic nerve function, and a questionnaire).
  • body composition measurement, autonomic nerve function measurement using a computer device 30 and questionnaire measurement may be performed), or three measurements may be performed using one device (for example, body composition and autonomic nerve function and question).
  • the measurement may be performed using a device capable of measuring the data on the vote).
  • the device used for the measurement may be a plurality of separate devices or a single device.
  • the single device may be a computer device 30 (for example, a smartphone 31, a personal computer 32, a tablet, etc.).
  • the single device may be a device different from the computer device 30.
  • the computer device 30 is used for information communication and information display.
  • Measurement with such a measuring device is non-invasive measurement.
  • Such a measurement can be performed in a short time by a simple action such as, for example, just putting it on, just getting on it, just taking a picture on the camera, or just answering a question.
  • a plurality of data can be acquired almost at the same time, so that the time difference between the data can be reduced or ignored. For example, by performing multiple measurements at almost the same time, the difference between the measurement completion time when the earliest data was acquired and the measurement completion time when the latest data was acquired (that is, the time difference between the data) is 1 second.
  • the time between the completion time of the measurement in which the data is acquired earliest and the completion time of the measurement in which the data is acquired the latest is within 5 minutes. This is because if the time difference between the data is within 5 minutes, the influence of the time difference between the data can be ignored.
  • the data measured by the autonomic nerve measuring device and the sphygmomanometer can fluctuate from moment to moment, it is preferable to make it possible to ignore the influence of the time difference with other data. This makes it possible to represent the position on the health positioning map for the exact health at a given point in time.
  • the measurement may be performed by the user U consciously or unconsciously by the user U. For example, by automatically performing the measurement when a predetermined condition is satisfied, the measurement can be performed without making the user U aware of it.
  • the measurement can be performed, for example, by using a measuring device attached to the body of the user U without making the user U aware of it.
  • a measuring device attached to the body of the user U regardless of whether the user U is indoors or outdoors (for example, even if the user U is walking, the user U is stationary. Also), measurement can be performed.
  • the measurement can be performed without making the user U aware, for example, by using a measuring device installed within the action range of the user U.
  • measuring devices installed within the range of action of user U include, for example, sensors mounted on furniture used by user U (for example, chairs, beds, etc.), and electronic devices used by user U (for example, personal computers, refrigerators, etc.). , Air conditioner, smart speaker, etc.), including, but not limited to, a sensor installed in a room such as the user U's home.
  • blood pressure can be automatically measured by a sphygmomanometer worn on the body when a predetermined condition is met.
  • body composition can be automatically measured by a body composition analyzer worn on the body when certain conditions are met.
  • the body composition can be automatically measured by a body composition analyzer mounted on the seat surface of the user's chair when a predetermined condition is satisfied.
  • the autonomic nervous function can be automatically measured by the camera of the computer device 30 when a predetermined condition is satisfied.
  • the 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 satisfied.
  • the autonomic nervous function can be automatically measured by a camera installed in a room such as the home of the user U when a predetermined condition is satisfied.
  • Predetermined conditions include, for example, when a predetermined time is reached (temporal condition), when entering / exiting a predetermined place (geographical condition), when performing a predetermined action (behavioral condition), and the like.
  • the predetermined time is, for example, a predetermined time of the day (for example, between 6:00 am and 8:00 am, between 11:00 am and 1:00 pm, between 4:00 pm and 6:00 pm, and 9:00 pm to am.
  • Predetermined locations include, but are not limited to, for example, offices, homes, living rooms, bedrooms, and the like.
  • a predetermined action is, for example, sitting on a chair, lying on a bed, a camera mounted on an electronic device used by the user U, or a camera installed in a room such as the user U's home for a certain period of time (for example,). It includes, but is not limited to, entering for 10 seconds, 30 seconds, 1 minute) or more.
  • the predetermined action may be, for example, to acquire at least one piece of data.
  • the predetermined action may be, for example, to acquire at least one piece of data.
  • the predetermined action may be, for example, to acquire at least one piece of data.
  • the body composition, autonomic nervous function, and / or blood pressure may be automatically measured in response to the user U consciously answering the questionnaire.
  • the user U can perform a plurality of measurements with a simple action of answering the questionnaire.
  • step S2 the data measured in step S1 is taken into the computer device 30.
  • data for body composition measured using the body composition analyzer 21 is input to the computer device 30. This may be done, for example, by establishing communication between the body composition analyzer 21 and the computer device 30, or by the user U inputting the measured values by the body composition analyzer 21 into the computer device 30. You may be broken.
  • data for blood pressure measured using the sphygmomanometer 22 is input to 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 a measurement from the sphygmomanometer 22 into the computer device 30. May be good.
  • step S1 the data for the autonomic nervous function and the data for the questionnaire were measured using the computer device 30, and these data have already been taken into the computer device 30.
  • step S3 the data captured in step S2 is transmitted from the computer device 30 to the server device S of the service provider via the network 40.
  • the health degree information of the user U can be derived based on the measurement result of the user U received from the computer device 30.
  • the health information of the user U includes information indicating in which area the user U is positioned on the health positioning map.
  • step S4 the health degree information derived from the server device S is transmitted from the server device S to the computer device 30 via the network 40.
  • step S5 the computer device 30 displays to the user U in which area on the health degree positioning map the health degree of the user U is positioned based on the health degree information.
  • the user U can recognize his / her health condition by referring to the information associated with the area on the health degree positioning map in which his / her health degree is positioned. For example, if the user's health belongs to the region characterized as health B on the health positioning map as shown in FIG. 1B, the user U may have good health but not bad. It can be recognized that there is no such thing. This makes it possible, for example, to encourage the user U to try to improve his / her health level.
  • the user U considers that he / she has a mental health disease risk and his / her health.
  • the state can be recognized. This can, for example, encourage User U to take steps to prepare for the risk of mental health illness.
  • the process from the measurement of steps S1 to S5 to the display of the health degree positioning map may be performed by the user's conscious operation or may be performed automatically by the user unknowingly. That is, the user U can easily recognize the position of his / her health level on the health level positioning map by performing various measurements. Alternatively, by automatically performing the measurement, all of steps S1 to S5 can be automatically performed. As a result, the user U can recognize the position of his / her health level on the health level positioning map without the burden of measurement.
  • the time from when the data is taken into the computer device 30 in step S2 until the health positioning map is displayed in step S5 is shortened. can do.
  • the user can immediately know his / her health condition.
  • the time from when the data is taken into the computer device 30 in step S2 until the health degree positioning map is displayed in step S5 is, for example, 0.01 seconds, 0.02 seconds, 0.05 seconds, 0.
  • the user U can recognize the time-series change of his / her health condition.
  • the user U can recognize the direction in which his / her health condition is headed by referring to the time-series change of the position on the health degree positioning map. For example, when one's health level belonged to a region characterized as health level B on a health level positioning map as shown in FIG. 1B, after a predetermined period of time, one's own health level became health level positioning. If the user U belongs to the area characterized as health level B on the map but is approaching the area characterized as health level C, the user U has his / her health condition in the direction of health level C.
  • the user U can recognize his / her own health level at that time. Then, by performing the above-mentioned steps S1 to S5 after the event, the user U can recognize his / her health level at that time. By comparing the pre-event health level thus obtained with the post-event health level, the user U can recognize the effect of the event on the health level.
  • Events include, for example, epidemics of infectious diseases (Cvid-19, etc.), natural disasters (eg, damage caused by storms, heavy rains, heavy snowfalls, floods, high tides, earthquakes, tsunamis, eruptions and other unusual natural phenomena), overtime work, and experiences. (Includes, but is not limited to, direct or pseudo-experiences (eg, experiences via VR (virtual reality), AR (augmented reality), etc.), communication with others or animals, and the like.
  • the user U can recognize his / her health level at that time. Then, at the end of the day (for example, between 6 pm and midnight), by performing the above-mentioned steps S1 to S5, the user U can recognize his / her health level at that time. By comparing the health level at the beginning of the day thus obtained with the health level at the end of the day, the user U can recognize the chronotype of the health level. For example, if such a degree of health mainly represents the degree of fatigue, the user U can objectively recognize how much fatigue has been accumulated by the daily activity.
  • the user U can recognize his / her health level at that time. Then, by performing the above-mentioned steps S1 to S5 at the beginning of the next day (for example, between 6:00 am and 0:00 pm), the user U can recognize his / her health level at that time. By comparing the health level at the end of the day thus obtained with the health level at the beginning of the next day, the user U can recognize the nighttime fluctuation of the health level. Nighttime fluctuations in health can correspond to the resilience or elasticity of the body, allowing the user U to objectively recognize how much he / she has recovered from sleeping at night.
  • the above-mentioned steps S1 to S5 can be performed, for example, at a predetermined time in a day.
  • the predetermined time is, for example, a predetermined time of the day (for example, between 6:00 am and 8:00 am, between 11:00 am and 1:00 pm, between 4:00 pm and 6:00 pm, and 9:00 pm to am. It may be during midnight, or at 6:00 am, 0:00 pm, 6:00 pm, 0:00 am, etc., or at predetermined intervals (eg, every hour, every 3 hours, every 6 hours). It may be an interval, etc.), or it may be a time set freely by the user.
  • the measurement is not limited to the non-invasive tests.
  • the measurement may include an invasive test.
  • the "invasive test” refers to a test that injures the body to be inspected (for example, by blood sampling by injection or tissue excision), and the "non-invasive test” does not injure the body to be inspected at all.
  • Typical invasive tests are tests that detect the amount of components contained in blood and plasma
  • typical non-invasive tests are tests that detect components in the target excretion (urine, exhaled breath, saliva) and autonomic nerves.
  • Functional tests cognitive function tests, questionnaires / VAS (Visual Analogue Scale), etc.
  • the term "invasive parameter” refers to a parameter obtained by an invasive test
  • the "non-invasive parameter” refers to a parameter obtained by a non-invasive test.
  • the measurement is performed at the home H, but the place where the measurement is performed is not limited to the home H.
  • the measurement may be performed at a company other than the inspection facility, a pharmacy, a public hall, a cafe, or the like. Because simple measurement is sufficient, measurement can be performed at a place other than the inspection facility.
  • the measurement may be performed in a hospital or a specialized laboratory.
  • FIG. 1B shows an example of a screen 1000 showing a user's health condition.
  • the screen 1000 may be displayed on the display screen of the computer device 30 of the user U (for example, a personal computer, a smartphone, a tablet, etc.) and provided to the user.
  • the computer device 30 of the user U for example, a personal computer, a smartphone, a tablet, etc.
  • the screen 1000 includes a health degree positioning map display unit 1100 and a radar chart display unit 1200.
  • the health degree positioning map is displayed on the health degree positioning map display unit 1100.
  • the horizontal axis is associated with physical health, and the larger the value on the horizontal axis, the worse the physical health, while the vertical axis is associated with mental health. The larger the value on the vertical axis, the worse the mental health.
  • the health degree positioning map displayed on the health degree positioning map display unit 1100 includes 10 areas, one of the 10 areas is characterized as health degree A, and three areas. Is characterized as post-health B, and 6 regions are characterized as health C.
  • the health level A indicates that the health level is good
  • the health level B indicates that the health level is normal
  • the health level C indicates that the health level is poor and requires attention. There is.
  • the user can recognize his / her health condition according to which area on the health degree positioning map his / her health level is located.
  • the health of the user is plotted with an asterisk, indicating that the health of the user belongs within the region characterized as health B.
  • the degree of health level of a specific user in the group can be grasp.
  • This is useful, for example, in labor management of a company. For example, a company's labor personnel should look at a health positioning map that plots the health of employees of the company and consider whether systematic intervention is necessary or possible. Can be done.
  • the health of one particular employee is plotted in a worse position than the other, it can be considered whether to intervene in that employee.
  • the health of an employee in a particular department is plotted in a worse position than that of an employee in another department, it can be considered whether to intervene in that department.
  • a radar chart is displayed on the radar chart display unit 1200.
  • the health condition is shown in 6 stages from 0 to 5 from 6 viewpoints (musculoskeletal musculoskeletal system, metabolism / metabo system, autonomic nervous system, sleep-wake rhythm, mental health, fatigue).
  • the viewpoint of the musculoskeletal motor system shows the state such as muscle strength related to exercise
  • the viewpoint of the metabolism / metabo system shows the state such as energy metabolism and obesity in the body
  • the viewpoint of the autonomic nervous system is related to concentration and relaxation.
  • the viewpoint of sleep-wake rhythm shows the state of sleep and drowsiness
  • the viewpoint of mental health shows the state of depression
  • the viewpoint of fatigue shows the state of mind and body. Shows the state of tiredness.
  • each axis is characterized by information on health, and the score of the user is mapped for each axis. Therefore, in the present specification, such a radar is used. Charts can also be considered a type of health positioning map.
  • the screen 1000 may include a health ranking display unit (not shown).
  • the health ranking display unit displays how much the user's health is within a specific group.
  • the specific group includes, for example, a group having the same surname as the user, a group having the same age as the user, a group having the same age as the user, and the like. With such a ranking display, it is easier to understand one's health level more intuitively. Furthermore, by displaying the direction of what should be done in order to raise the ranking, it is possible to promote the behavior change of the user by using the principle of competition.
  • the above-mentioned service can be realized by, for example, the computer system 10 described below.
  • FIG. 2 shows an example of the configuration of computer system 10.
  • the computer system 10 includes a server device 100 and at least one terminal device 300.
  • the server device 100 is connected to the database unit 200. Further, the server device 100 is connected to at least one terminal device 300 via the network 400.
  • the server device 100 is, for example, a computer installed in a service provider that provides a new service for visualizing a user's health condition.
  • the network 400 can be any kind of network.
  • the network 400 may be, for example, the Internet or a LAN.
  • the network 400 may be a wired network or a wireless network.
  • the number of terminal devices 300 is not limited to this.
  • the number of terminal devices 300 can be any number of 1 or more.
  • An example of the terminal device 300 is a computer held by a user, but the terminal device 300 is not limited to the computer.
  • it may be a computer installed in a hospital, a computer installed in a room of an office capable of performing an examination, or the like.
  • the computer can be any type of computer.
  • the terminal device 300 can be any type of terminal device such as a smartphone, a tablet, a personal computer, a smart glass, or the like.
  • the server device 100 includes 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 via the interface unit 110, and can transmit the information to the outside of the server device 100.
  • the interface unit 110 can exchange information in any format.
  • the interface unit 110 includes, for example, 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 the information to be input to the server device 100.
  • the interface unit 110 includes, for example, an output unit that enables information to be output from the server device 100. It does not matter in what mode the output unit enables the 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 server device 100 as a whole.
  • the processor unit 120 reads the program stored in the memory unit 150 and executes the program. This makes it possible to make the server device 100 function as a system that executes a desired step.
  • the processor unit 120 may be implemented by a single processor or may be implemented by a plurality of processors.
  • the memory unit 150 stores a program required for executing the processing of the server device 100, data required for executing the program, and the like.
  • the memory unit 150 creates a program for causing the processor unit 120 to perform a process for creating a health degree positioning map (for example, a program for realizing the process shown in FIGS. 5A and 5B described later) and a health function.
  • a program for causing the processor unit 130 to perform the processing for the purpose for example, a program for realizing the processing shown in FIG. 6 described later
  • a program for causing the processor unit 140 to perform the processing for estimating the health level of the user may be stored.
  • the program may be pre-installed in the memory unit 150.
  • the program may be installed in the memory unit 150 by being downloaded via the network. In this case, the type of network does not matter.
  • the memory unit 150 may be implemented by any storage means.
  • data obtained from a plurality of subjects can be stored in the database unit 200.
  • the database unit 200 may store, for example, the data of the health degree positioning map generated by the server device 100.
  • the database unit 200 may store, for example, a health function generated by the server device 100.
  • the database unit 200 may store health degree information indicating the health degree of the user derived by the server device 100. The health degree information indicating the health degree of the user is stored in the database unit 200 when the consent of the user is obtained, and can be used for research by another user.
  • FIG. 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 includes an acquisition unit 121, a processing unit 122, a mapping unit 123, a clustering unit 124, and a characterization unit 125.
  • the acquisition means 121 is configured to acquire a first data set for a first parameter set, which will be described later, for each of the plurality of subjects.
  • the acquisition means 121 acquires a data set of a plurality of items (for example, 232 items in a certain embodiment) per subject.
  • the first parameter set is obtained, for example, by acquiring a data set of an initial parameter set, correlating each data of the data set of the initial parameter set, and extracting parameters having a correlation coefficient equal to or higher than a predetermined threshold. It may be an extraction parameter set.
  • the extraction parameter set can be extracted so as to include the four basic parameters described later.
  • the first parameter set is, for example, an extraction parameter set obtained by acquiring a data set of the initial parameter set and extracting a parameter set having a high influence on the health positioning map from the data set of the initial parameter set by machine learning. May be.
  • the acquisition means 121 can, for example, receive data about a plurality of subjects stored in the database unit 200 via the interface unit 110 and acquire the received data.
  • the acquisition means 121 receives, for example, data about a plurality of subjects stored in the database unit 200 from a computer system of an examination facility (for example, a hospital, a laboratory, etc.) via the interface unit 110, and the received data. Can be obtained.
  • the acquired first data set is passed to the processing means 122 for subsequent processing.
  • the database unit 200 may store the first data set for the first parameter set.
  • FIG. 4 shows an example of the data structure of the first data set stored in the database unit 200.
  • the database unit 200 stores the first data set for the first parameter set for each of the plurality of subjects.
  • An ID is assigned to each of the plurality of subjects.
  • the database unit 200 stores a set of values (first data set) of each parameter of the first parameter set such as age, muscle mass, BMI, fat percentage, ultrasonic conduction velocity, osteoporosis index, and the like. ing.
  • the acquisition means 121 further sub-first data the first data included in a part of the plurality of regions included in the created health positioning map. It may be configured to be obtained as. Alternatively, the acquisition means 121 may be configured to acquire the first data included in a part of the plurality of regions specified by the clustering means 124 described later as the sub-first data.
  • the acquisition means 121 can acquire, for example, the first data in the area selected by the health positioning map creator.
  • the health positioning map creator can input the area selection to the server device 100 via the interface unit 110.
  • the health positioning map creator is, for example, a specific group among a plurality of subjects, for example, a male subject group, a female subject group, a young group (group under 40 years old), and a middle-aged group (40 years old or older 60 years old).
  • a health positioning map focusing on a population under the age of (group under the age of 60), a population of the elderly (group over the age of 60), etc., the area to which those groups can belong can be selected.
  • the acquired sub-first data can be passed to the clustering means 124 for subsequent processing.
  • the processing means 122 is configured to process the first data set acquired by the acquisition means 121.
  • the processing means 122 can output the first data by processing the first data set.
  • the processing by the processing means 122 may include arbitrary processing as long as the first data to be output can be mapped. When creating a positioning map based on mixed gender data, it is preferable to correct gender differences.
  • the processing by the processing means 122 may include, for example, a dimension reduction processing.
  • the dimension reduction process is a process of converting m-dimensional data into n-dimensional data, where m> n.
  • the dimension reduction process can be performed using, for example, multidimensional scaling (MDS: multi-dimensional scaling), principal component analysis, multiple regression analysis, principal component analysis, machine learning, etc., but is a means of dimension reduction processing. Is not limited to these.
  • the dimension reduction process preferably reduces the first data set to two-dimensional data or three-dimensional data. This is because when the two-dimensional data or the three-dimensional data is mapped by the mapping means 123 described later, the map becomes a two-dimensional space or a three-dimensional space, and a visually easy-to-understand map can be obtained.
  • the dimension reduction process can be performed using multidimensional scaling. This is because when the first data obtained by the multidimensional scaling method is mapped by the mapping means 123 described later, a visually easy-to-understand map can be obtained.
  • the processing by the processing means 122 may include, for example, standardization processing.
  • the standardization process is a process of aligning the scale of data for each parameter of the first data set.
  • the standardization process is, for example, a process of calculating a Z score (a process of correcting data so that the average value is 0 and a standard deviation of 1) and a process of calculating a T score (the average value is 50). And, the process of correcting the data so that the standard deviation is 10) and the like.
  • the processing means 122 may perform the standardization processing of the data of the first data set for all the parameters in the first parameter set, or may perform the standardization processing of the data of the first data set for a specific parameter. You may do it.
  • the processing means 122 may perform standardization processing on the entire first data set of the plurality of subjects, or may perform standardization processing on the first data set of a specific population among the plurality of subjects. You may do it.
  • a specific population among multiple subjects is, for example, a male subject group, a female subject group, a young group (group under 40 years old), a middle-aged group (group between 40 years old and under 60 years old), and an elderly group. It includes, but is not limited to, a group (a group of 60 years or older) and the like.
  • the processing means 122 can form an arbitrary population from a plurality of subjects and perform standardization processing on the first data set of the population.
  • the processing means 122 classifies the first data set from a plurality of subjects into a data set of a male subject and a data set of a female subject, and standardizes the data set of the male subject to obtain a male subject population.
  • a standardization process for a female subject population can be performed, or both can be performed.
  • a parameter for example, the number of red blood cells in blood
  • a parameter for example, the number of red blood cells in blood
  • the processing by the processing means 122 may include, for example, a weighting processing.
  • the weighting process is a process of weighting at least a part of the data in the first data set. For example, at least a part of the data in the first data set may be weighted by adding a predetermined number, or at least a part of the data in the first data set may be multiplied by a predetermined number. It may be weighted.
  • the predetermined number to be added or multiplied may be constant or different for each weighted data. For example, the predetermined number can be varied so as to give a large or small weight to the data having a large influence on the health function derived by the derivation means 133 described later. Alternatively, for example, a predetermined number can be varied so as to give a large or small weight to the data having a small influence on the health function derived by the derivation means 133 described later.
  • the processing means 122 may perform weighting processing on the entire first data set of a plurality of subjects, or may perform weighting processing on the first data set of a specific population among a plurality of subjects. You may do it.
  • the processing means 122 can form an arbitrary population from a plurality of subjects and perform weighting processing on the first data set of the population.
  • the population to which the weighting process is performed may be the same as or different from the population to which the standardization process described above is performed.
  • the mapping means 123 is configured to map the first data, which is the output of the processing means 122, to each of the plurality of subjects.
  • the mapping by the mapping means 123 is a process of associating the n-dimensional first data with a position in the n-dimensional space.
  • the mapping means 123 can output a map to which the first data of each of the plurality of subjects is mapped.
  • the mapping means 123 is two-dimensional by mapping the first data so as to relate the first data to a two-dimensional space, that is, a position on a plane. You can output a map.
  • FIG. 9A is a diagram showing an example of a mapping result by the mapping means 123. As shown in FIG.
  • the mapping means 123 outputs a map by plotting the points determined by the first data obtained by the processing means 122 in a two-dimensional space (multidimensional space) for a plurality of subjects. ..
  • the mapping means 123 outputs, for example, a map (radar chart) in which the first data of each of a plurality of subjects is mapped by mapping the first data of n dimensions on a radar chart having n axes. You may do so.
  • the clustering means 124 is configured to cluster the first data mapped by the mapping means 123.
  • the clustering by the clustering means 124 is a process of dividing the mapped first data into a plurality of clusters and specifying each region to which the plurality of clusters belong.
  • a "region" refers to a range within an n-dimensional space and has an n-dimensional extent.
  • the clustering means 124 can divide the mapped first data into an arbitrary number of clusters.
  • the clustering means 124 preferably divides the mapped first data into at least three clusters. By using three clusters (for example, clusters with good health, normal health, poor health, etc. as shown in FIG.
  • 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 by using any known method.
  • the clustering means 124 can divide the data into a plurality of clusters by using a non-hierarchical clustering method (for example, k-means method, k-means ++ method, PAM method, etc.).
  • the clustering means 124 can preferably divide the data into a plurality of clusters using the k-means method. This is because the results of clustering using the k-means method reflected the tendency of the subject population better than the results of clustering by other methods, and the contained information was rich.
  • the clustering means 124 can specify a plurality of regions, for example, by defining a boundary that separates each of the plurality of clusters. The demarcation can be done using any known process. For example, in the radar chart example described above, the clustering means 124 can simply separate the value of each axis from the value of the other axis and specify the plurality of axes as a plurality of regions.
  • the clustering means 124 may be further configured to cluster the sub-first data acquired by the acquisition means 121.
  • the clustering means 124 can divide the sub-first data into a plurality of clusters and specify each region to which the plurality of clusters belong.
  • the clustering means 124 can divide the sub-first data into an arbitrary number of clusters.
  • the characterization means 125 is configured to characterize at least a portion of the plurality of regions identified by the clustering means 124.
  • the characterization means 125 may characterize at least a portion of the plurality of regions, for example, based on information input to the server device 100 via the interface unit 110 by the health positioning map creator.
  • the health positioning map creator may analyze the subject's characteristics corresponding to the first data contained in each of the plurality of areas, and based on the analysis result, input the information that the area should be characterized. can.
  • the characterization means 125 may characterize at least a portion of the plurality of regions without relying on input by the health positioning map creator.
  • characterization means 125 may characterize at least a portion of a plurality of regions based on relative position in a health positioning map, or at least one of a plurality of regions based on machine learning. The part may be characterized.
  • a health positioning map is created in which at least a part of the plurality of areas is characterized.
  • the health positioning map where a portion of the regions identified by clustering the subfirst data is characterized is for some subjects of the plurality of subjects. It becomes a health positioning map.
  • the health degree positioning map created by the processor unit 120 is output to the outside of the server device 100 via, for example, the interface unit 110.
  • the health degree positioning map may be transmitted to the database unit 200 via the interface unit 110 and stored in the database unit 200, for example. Alternatively, it may be transmitted to the processor unit 130 described later for creating a health function.
  • 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 another computer system.
  • FIG. 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 a subject's health on a health positioning map.
  • the processor unit 130 may be a processor unit included in the server device 100 as an alternative to the processor unit 120 described above, or may be a processor unit included in the server device 100 in addition to the processor unit 120.
  • the processor unit 130 is a processor unit included 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 may be implemented by different processors. good.
  • the processor unit 130 includes a first acquisition unit 131, a second acquisition unit 132, and a derivation unit 133.
  • the first acquisition means 131 is configured to acquire a health degree positioning map.
  • the acquired health degree positioning map may be the health degree positioning map created by the processor unit 120 described above as long as it is created using the first parameter set, or may be a health degree positioned map created separately. It may be a positioning map.
  • the acquired health 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, which will be described later, for at least a part of the plurality of subjects.
  • 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 items that are not in the first parameter set.
  • the second acquisition means 132 can acquire, for example, data about a part of a 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 the position on the health positioning map acquired by the first acquisition means 131. There is.
  • the derivation means 133 can derive a health function by, for example, machine learning, decision tree analysis, random forest regression, multiple regression analysis, principal component analysis, or the like.
  • 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, the health function X that correlates the second dataset with the X coordinates on the health positioning map, the two datasets, and the Y on the health positioning map. It is possible to derive a health function Y that correlates with the coordinates.
  • the derivation means 133 for example, arbitrarily increases or decreases the number of variables of the health function to create a plurality of health functions having identification accuracy, that is, a health function group (hereinafter, also referred to as a plurality of pattern health function groups). You may.
  • the derivation means 133 has only (1) a health function in which the data of the blood test item and the data of the other items are variables, and (2) the data of the blood test item, which have the same degree of accuracy as each other. You may create a health function with.
  • the derivation means 133 may create a health function group in which data selected from the data group of items other than the blood test item data is used as a variable as the plurality pattern health function group.
  • the health function can be, for example, a regression model.
  • the regression model may be a linear regression model or a non-linear regression model.
  • the derivation means 133 is a regression model by machine learning for at least a part of each of the plurality of subjects by using the second data set as an independent variable and the coordinates on the health positioning map of the subject as the dependent variable. Each coefficient of can be derived.
  • the second data set obtained from the subject is input to the independent variable of such a machine-learned regression model, the coordinates on the health positioning map of the subject are output. Using the output coordinates, the health level of the subject can be mapped on the health level positioning map.
  • the health function can 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. That is, the number of input nodes corresponds to the number of parameters in the second parameter set.
  • the hidden layer of the neural network model can contain 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. That is, when the X coordinate on the health positioning map is output from the neural network model, the number of nodes in the output layer is 1.
  • the number of nodes in the output layer is n.
  • the derivation means 133 performs machine learning for at least a part of each of the plurality of subjects, using the second data set as input teacher data and the position of the subject on the health positioning map as output teacher data. Therefore, the weight coefficient of each node can be derived.
  • a set of (teacher data for input, teacher data for output) for machine learning is on the health positioning map of the first subject, the second data set with respect to the second parameter set for the first subject. Coordinates), (second data set for the second parameter set for the second subject, coordinates on the health positioning map of the second subject), ... (second for the second parameter set for the i-th subject). 2 data set, coordinates on the health positioning map of the i-th subject), ..., etc.
  • the coordinates on the health positioning map of the subject are output to the output layer.
  • the health level of the subject can be mapped on the health level positioning map.
  • the health function created by the processor unit 130 is output to the outside of the server device 100 via, for example, 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, for example. Alternatively, it may be transmitted to the processor unit 140 described later for the process of estimating the health degree of the user.
  • 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 another computer system.
  • FIG. 3C shows an example of the configuration of the processor unit 140 in still another embodiment.
  • the processor unit 140 may have a configuration for a process of estimating the health of the user. In the process by the processor unit 140, the health degree of the user can be estimated by estimating in which region the health degree of the user is located on the health degree positioning map.
  • the processor unit 140 may be a processor unit included in the server device 100 as an alternative to the processor unit 120 and the processor unit 130 described above, or may be provided in the server device 100 in addition to the processor unit 120 and / or the processor unit 130 described above. It may be a processor unit.
  • the processor unit 140 is a processor unit included in the server device 100 in addition to the processor unit 120 and / or the processor unit 130, the processor unit 120, the processor unit 130, and the processor unit 140 are all mounted by the same processor. It may be implemented by different processors, or two of the processor unit 120, the processor unit 130, and the processor unit 140 may be implemented by the same processor.
  • the processor unit 140 includes a third acquisition unit 141, a fourth acquisition unit 142, an output generation unit 143, and an output mapping unit 144.
  • the third acquisition means 141 is configured to acquire a health function.
  • the health function is a function that correlates the data set for the second parameter set described above with the position on the health positioning map.
  • the acquired health function may or may not be the health function created by the processor unit 130 described above, as long as it can correlate the user dataset with its position on the health positioning map. It may be a health function.
  • the health positioning map may be a health positioning map created by the processor unit 120 described above, as long as it is created using the first parameter set, or may be a health positioning map created separately. There may be.
  • 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 acquire, for example, the user data set stored in the database unit 200 via the interface unit 110.
  • the fourth acquisition means 142 can acquire, for example, a 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 a 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 herein as "health information".
  • the health information includes coordinates on the health positioning map (eg, X-axis values, Y-axis values).
  • the coordinates on the health positioning map are output by inputting the user data set into the independent variable of the regression model.
  • the coordinates on the health positioning map are output by inputting the user data set to the input layer of the neural network model.
  • the output mapping means 144 is configured to map the output generated by the output generation means 143 on the health positioning map. Since the output generated by the output generation means 143 is the coordinates, the output mapping means 144 can map the coordinates within the n-dimensional space of the health positioning map.
  • the output mapped on the health degree positioning map by the processor unit 140 is output to the outside of the server device 100 via, for example, the interface unit 110.
  • the output may be transmitted to the terminal device 300 via, for example, the interface unit 110.
  • the processor unit 140 includes the output mapping means 144, but the processor unit 140 does not have to include the output mapping means 144.
  • the terminal device 300 may have a function of expressing the health degree information as a position on the health degree positioning map. At this time, the output by the output generation means 143, that is, the health degree information is transmitted to the terminal device 300 via the interface unit 110.
  • Each component of the server device 100 described above may be composed of a single hardware component or may be composed of a plurality of hardware components. When it is composed of a plurality of hardware parts, the mode in which each hardware part is connected does not matter. Each hardware component may be connected wirelessly or may be connected 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 that the processor units 120, 130, 140 are configured by an analog circuit instead of a digital circuit.
  • the configuration of the server device 100 of the present invention is not limited to the above-mentioned one as long as the function can be realized.
  • FIG. 3D is a diagram showing an example of a data flow 1 by the server device 100 in one embodiment.
  • the data flow 1 includes a data acquisition step 10, a data processing step 20, a health evaluation map creation step 30, a clustering map creation step 40, a health function value calculation step 50, and a positioning map creation.
  • a step 60 and an output step 70 are included.
  • the data acquisition step 10, the data processing step 20, the health evaluation map creation step 30, and the clustering map creation step 40 have a function as a health degree positioning map creation device, and for example, a server device including the processor unit 120 described above. Can be implemented by 100.
  • the data acquisition step 10 and the health function value calculation step 50 have a function as a health function value calculation device, and may be implemented by, for example, a server device 100 including the processor unit 140 described above.
  • the output step 70 the data generated in the data processing step 20, the health evaluation 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, and for example, a display device ( For example, it is output by a liquid crystal display).
  • the data set acquired in the data acquisition step 10 is sent to the data processing step 20.
  • the correction 21 and the dimension reduction 22 are performed.
  • the data processing step 20 may be implemented, for example, by the processing means 122 of the processor unit 120 described above.
  • the correction 21 is to correct the data acquired in the data acquisition step 10. Specifically, the correction 21 is set so that, for example, the value of the data acquired in the data acquisition step 10 is a value in a predetermined range (for example, the average value is 0 and the standard deviation is 1). Each data acquired in the data acquisition step 10 is corrected. The corrected data is sent to dimensionality reduction 22.
  • the dimension reduction 22 reduces the dimensions of a plurality of data passed from the data acquisition step 10 or the correction step 21.
  • the dimensionality reduction 22 is a plurality of data (multidimensional data) passed from the data acquisition step 10 or the correction step 21 using multiple regression analysis, multidimensional scaling method, principal component analysis, or machine learning.
  • the dimension of is set to an arbitrary dimension (two dimensions in this embodiment).
  • the dimensionally reduced data may be in the form of a function, for example.
  • this function is a function for calculating an index related to health.
  • the function is, for example, a function in which some or all of the data contained in the first data is used as a variable, and is created by giving a larger weight to the data having a particularly large influence among various disease factors. Function.
  • the function in this embodiment is created by using a linear or non-linear model for some or all of the data included in the first data.
  • a two-dimensional function in this embodiment, a function X on the horizontal axis (hereinafter referred to as a first function) and a function Y on the vertical axis (hereinafter referred to as a second function)). (Consists of) is created.
  • the created function is passed to the health evaluation map creation step 30 and the output step 70.
  • the dimension reduction 22 creates a two-dimensional function by lowering the multidimensional data to two dimensions as described above.
  • the first function and the second function are functions in which all or a part of a plurality of data passed from the data acquisition step 10 or the correction step 21 are variables, and are multiple regression analysis, multidimensional scale method, principal component analysis, and the like. Or it is a function calculated by machine learning.
  • machine learning means either machine learning including deep learning or machine learning not including deep learning.
  • the variables constituting the first function and the second function may be completely the same or completely different, or some variables may overlap with each other.
  • the health evaluation map creation step 30 can be implemented by, for example, the mapping means 123 of the processor unit 120 described above.
  • the function is two-dimensional
  • the health evaluation map is a two-dimensional map.
  • FIG. 9A is a diagram showing an example of a health evaluation map. As shown in FIG. 9A, in the health evaluation map creation step 30, the health evaluation map is created by plotting the points determined by the first function and the second function in a two-dimensional space (multidimensional space) for a plurality of subjects. Will be created.
  • the clustering map creation step 40 can be implemented, for example, by the clustering means 124 and the characterization means 125 of the processor unit 120 described above.
  • the clustering map creation step 40 for example, a map in which a plurality of points plotted on the health assessment map created in the health assessment map creation step 30 are clustered into several clusters and a plurality of regions are characterized (hereinafter, the map).
  • a health positioning map) is created.
  • a plurality of plotted points are clustered into an arbitrary number of clusters by using a non-hierarchical clustering method (k-means method in the present embodiment). In the example shown in FIG. 9A, it is classified into four clusters. By characterizing at least one of these four areas with health information, a health positioning map is created.
  • the created health degree positioning map may be output in the output step 70, may be passed to the health function value calculation step 50, or may be 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 by, for example, the processor unit 130 and the processor unit 140 described above.
  • a health function is created based on the health degree positioning map.
  • the number of variables of the health function may be arbitrarily increased or decreased to create a plurality of health functions having identification accuracy, that is, a health function group.
  • the value of the health function of the subject different from the subject who acquired the first data set is calculated in order to create the health function.
  • the value of the subject's health function can be obtained. calculate. Since the health function is a multidimensional function, the health function value is naturally a multidimensional value.
  • the value of the subject's health function calculated by the health function value calculation step 50 is created by the health evaluation map created by the health evaluation map creation step 30 or the clustering map creation step 40. Plot on the health positioning map to create a health prediction positioning map. This makes it possible to predict the health condition of the subject whose data is newly measured.
  • the value of the health function calculated in the health function value calculation step 50 using the data acquired at time intervals for a single subject is used as the health evaluation map or the above-mentioned health evaluation map.
  • the data acquired from the new subject can also be used as data for updating (updating) the health function.
  • the health function value calculation step 50 the health function can be updated by further using the data acquired from the new subject or the data output from the correction step 21 using the data. This makes it possible to evaluate various health risks and improve the accuracy of health risk assessment.
  • Each step of the data flow 1 is an integrated circuit (IC chip). ) Or the like, it may be realized by a logic circuit (hardware), or it may be realized by software.
  • the health evaluation device 1 includes a computer that executes instructions of a program that is software that realizes each function.
  • the computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. Then, in the computer, the processor reads the program from the recording medium and executes the program, thereby achieving the object of the present invention.
  • a CPU Central Processing Unit
  • a "non-temporary tangible medium" for example, a ROM (Read Only Memory) or the like, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used.
  • a RAM Random Access Memory
  • the program may be supplied to the computer via any transmission medium (communication network, broadcast wave, etc.) capable of transmitting the program. It should be noted that one aspect of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the above program is embodied by electronic transmission.
  • a health positioning map and / or a health function using various data. That is, it is possible to create a health degree positioning map and / or a health function that can comprehensively calculate the health degree of a subject, instead of a health index related to an existing individual disease.
  • health positioning maps and / or health functions can be created that can assess various health risks (ie, assess the overall health of the subject).
  • a health degree positioning map and / or a health function can be flexibly created by combining various data measured in the data acquisition step 10. That is, the flexibility of selecting the items to be measured in the data acquisition step 10 is very high.
  • a health function is created by machine learning using the second data set as input data. This makes it possible to create a health function that can more accurately generate an index of the health condition of the subject.
  • the data between genders is corrected before the health positioning map is created in the first data set. This makes it possible to create a health positioning map that can be applied to any gender.
  • the first data may include only the data acquired by the non-invasive measurement. According to the above configuration, data can be acquired without damaging the subject such as a blood test.
  • a map for evaluating health can be created using the function created by the data processing step 20. Specifically, when the function is a multidimensional vector, a map for evaluating health is created by plotting the points determined by the function for a plurality of subjects in a multidimensional space. This makes it possible to visually confirm the health condition of the subject.
  • the points plotted in the multidimensional space are clustered. Then, by referring to the data of the subjects belonging to each clustered cluster, it is possible to identify what kind of group each cluster is and characterize those clusters. As a result, by plotting the newly measured subject data on the health positioning map, it becomes possible to predict the measured health condition of the subject.
  • FIG. 3E shows an example of the configuration of the terminal device 300.
  • the terminal device 300 includes 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 can receive information from the outside of the terminal device 300 via the interface unit 310, and can transmit the information to the outside of the terminal device 300.
  • 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 in what manner the input unit enables the information to be input to the terminal device 300. For example, when the input unit is a touch panel, the user may input information by touching the touch panel. Alternatively, when the input unit is a mouse, the user may input information by operating the mouse. Alternatively, when the input unit is a keyboard, the user may input information by pressing a key on the keyboard. Alternatively, when the input unit is a microphone, the user may input information by inputting voice into the microphone. Alternatively, when the input unit is a camera, the information captured by the camera may be input.
  • the information may be input by reading the information from the storage medium connected to the terminal device 300.
  • the receiver may input information by receiving information from the outside of 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 mode the output unit enables the information to be output from the terminal device 300. For example, when the output unit is a speaker, information may be output by voice from the speaker. Alternatively, when the output unit is a data writing device, the information may be output by writing the information to the storage medium connected to the terminal device 300. Alternatively, when the output unit is a transmitter, the transmitter may output information by transmitting information to the outside of 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 may transmit information via LAN.
  • the terminal device 300 can receive measurement data from the measurement device by communicating with any measurement device via the interface unit 310.
  • the terminal device 300 can receive data for body composition parameters from the body composition meter, for example, by communicating with the body composition meter via the interface unit 310.
  • a body composition analyzer is any device that can measure the body composition of a subject.
  • the body composition analyzer may be, for example, a type of body composition analyzer that is placed on the body at the time of measurement, or a type of body composition analyzer that can be worn on the body (for example, a wristwatch-type body composition analyzer).
  • the body composition analyzer 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 for blood pressure parameters from the sphygmomanometer, for example, by communicating with the sphygmomanometer via the interface unit 310.
  • a sphygmomanometer is any device that can measure the blood pressure of a subject.
  • the sphygmomanometer is, for example, a sphygmomanometer of the type worn on the body (for example, an upper arm type sphygmomanometer, a wristwatch type sphygmomanometer, a ring type sphygmomanometer), or a non-contact type sphygmomanometer (for example, photographed by a camera). It can be a sphygmomanometer) that measures blood pressure from an image of a face or finger.
  • the sphygmomanometer may be implemented by the terminal device 300, for example by installing an application for measuring blood pressure.
  • the sphygmomanometer may also measure, for example, the body temperature of the subject.
  • the body temperature may be measured in a non-contact manner (for example, from an image taken by a camera), or the body temperature may be measured in a contact manner.
  • the terminal device 300 can receive data for the autonomic nerve parameter from the autonomic nerve measuring device by communicating with the autonomic nerve measuring device via the interface unit 310, for example.
  • the autonomic nerve measuring device is an arbitrary device capable of measuring the autonomic nerve function of the subject.
  • the autonomic nerve measuring instrument can 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 the autonomic nerve function.
  • the autonomic nerve measuring instrument can measure data related to pulse waves, for example, as data for autonomic nerve parameters.
  • the autonomic nerve measuring instrument can acquire data on the pulse wave of the subject from, for example, an image of the subject taken by a camera, and derive data on the autonomic nerve parameter from the data on the pulse wave.
  • the terminal device 300 can receive data for the questionnaire parameters by communicating with the questionnaire processing device via the interface unit 310, for example.
  • the questionnaire processing device can be any device capable of acquiring data from the answer result of the questionnaire by having the subject answer the questionnaire and processing the answer result of the questionnaire by the subject.
  • the questionnaire processing device may be, for example, an electronic device (for example, a game device) capable of processing the answer result of the questionnaire by the subject.
  • the questionnaire processing device may be implemented by the terminal device 300, for example, by installing an application for making the subject answer the questionnaire.
  • the questionnaire processing device may be, for example, an apparatus capable of optically reading the answer sheet of the questionnaire.
  • the questionnaire processing device can measure, for example, data for age, subjective evaluation parameters, and / or data for living conditions parameters, such as data for questionnaire parameters.
  • the questionnaire processing device for example, as data for questionnaire parameters, includes age, subjective evaluation regarding QOL (Quality of Life), subjective evaluation regarding fatigue, subjective evaluation regarding psychology, living conditions, and the like. Can be measured.
  • the questionnaire processing device may be, for example, a device capable of deriving the questionnaire parameters from the data acquired from the subject.
  • a questionnaire processing device can acquire data indicating a living situation from data acquired from, for example, an activity meter (for example, sleep time, exercise time, etc.).
  • an activity meter for example, sleep time, exercise time, etc.
  • such a questionnaire processing device may have a function of an activity meter, and may automatically acquire questionnaire parameters from the data acquired from the subject.
  • such a questionnaire processing device may have an imaging function and may automatically acquire questionnaire parameters from an image of the subject (for example, the facial expression of the subject).
  • the questionnaire processing device may be, for example, a group of questionnaire processing devices composed of a plurality of measuring devices capable of deriving questionnaire parameters.
  • at least one questionnaire parameter can be acquired from one measuring instrument and at least one questionnaire parameter can be acquired from another measuring instrument.
  • the terminal device 300 can receive data for the temperature by communicating with the thermometer via the interface unit 310, for example.
  • Data for temperature includes, for example, air temperature, body temperature.
  • the thermometer may measure the temperature, for example, in a non-contact manner (eg, from an image of a face or finger taken by a camera, or by bringing the temperature measuring part closer to the temperature measuring object), or in a contacting manner (for example, by bringing the temperature measuring part closer to the temperature measuring object).
  • the temperature may be measured (by bringing the temperature measuring part into contact with the temperature measuring object).
  • the non-contact thermometer may be mounted using an electronic device (for example, a terminal device 300) by connecting a temperature measuring unit to an earphone jack of the electronic device, for example.
  • the contact type thermometer can measure the body temperature, for example, in the armpit, in the mouth, in the ear, or the like.
  • thermometer that measures the body temperature in the ear may be, for example, an earphone type (wired earphone, wireless earphone, etc.). Such a contact thermometer is also mounted using an electronic device (eg, terminal device 300) by connecting a temperature detector to the earphone jack of the electronic device or by wirelessly connecting to the electronic device. You may do so.
  • an electronic device eg, terminal device 300
  • the above-mentioned measuring device can be portable or portable.
  • the term “portable” measuring device refers to a device that can be manually transported.
  • the term “portable” measuring device means a device intended to be carried in daily life. Since the place of use of the portable or portable measuring device is not restricted, the use of these devices facilitates simple measurement at any place and at any time.
  • the present invention is simplified by using parameters that can be measured using a small number (eg, 10 or less, 5 or less, 4 or less, preferably 3 or less) of portable or portable devices. It is an advantage that the overall health of the user can be evaluated.
  • a dedicated equipment configuration and a skilled measurer are required.
  • the measuring device can be made portable or portable as described above, or the same function as the above-mentioned measuring device can be performed on the terminal device 300. You will be able to implement it. This facilitates simple measurements at any location and at any time.
  • the above-mentioned measuring device can be a server-linked type.
  • the server-linked measuring device is a device having a function of communicating with the server, and the measured data can be directly transmitted to the server. In this case, the above-mentioned measuring device does not need to communicate with the terminal device 300.
  • the above-mentioned measuring device may be mounted by a separate device or may be mounted by a single device having a plurality of functions.
  • at least one of the above-mentioned measuring instruments may be implemented by the terminal device 300.
  • the number of measuring devices can be reduced, and the burden on the user for measurement can be reduced.
  • the user can perform measurement only by the terminal device 300.
  • the acquisition of data by the above-mentioned measuring device may be performed by the subject consciously performing the measurement, or may be performed by the subject unconsciously performing the measurement.
  • the acquisition of data by the above-mentioned measuring device can be performed without making the subject aware, for example, by automatically performing the measurement when a predetermined condition is satisfied.
  • the measurement can be performed, for example, by using a measuring device attached to the body of the subject without making the subject aware of it. For example, by using a measuring device worn on the subject's body, measurement can be performed regardless of whether the subject is indoors or outdoors (for example, whether the subject is walking or stationary). It can be performed.
  • the measurement can be performed without making the subject aware, for example, by using a measuring device installed within the range of action of the subject.
  • a measuring device installed within the subject's range of action whatever the subject is doing within the range of action (for example, whether the subject is walking or stationary).
  • Measurement can be performed.
  • Measuring instruments installed within the subject's range of motion include, for example, sensors mounted on furniture used by the subject (eg, chairs, beds, etc.), electronic devices used by the subject (eg, personal computers, refrigerators, air conditioners, etc.). It includes, but is not limited to, a sensor mounted on a smart speaker, etc.) and a sensor installed in a room such as the subject's home.
  • data for blood pressure parameters can be automatically measured by a sphygmomanometer worn on the body when certain conditions are met.
  • data for body composition parameters can be automatically measured by a body composition analyzer worn on the body when certain conditions are met.
  • the data for the body composition parameters can be automatically measured by a body composition analyzer mounted on the seat surface of the user's chair when a predetermined condition is satisfied.
  • the data for the autonomic nerve parameters can be automatically measured by the camera included in the autonomic nerve measuring device when a predetermined condition is satisfied.
  • the data for the autonomic nerve parameters are automatically derived from the subject's image taken automatically when a predetermined condition is met by the electronic device used by the subject or the camera mounted on the terminal device 300.
  • the data for the autonomic nerve parameters are automatically derived from the subject's image taken automatically when a predetermined condition is met by a camera installed in a room such as the subject's home. can do.
  • data for questionnaire parameters can be made to be automatically acquired from communication with an electronic device (eg, a smart speaker) used by the subject.
  • Predetermined conditions include, for example, when a predetermined time is reached (temporal condition), when entering / exiting a predetermined place (geographical condition), when performing a predetermined action (behavioral condition), and the like. include.
  • the predetermined time is, for example, a predetermined time of the day (for example, between 6:00 am and 8:00 am, between 11:00 am and 1:00 pm, between 4:00 pm and 6:00 pm, and 9:00 pm to am. It may be during midnight, or at 6:00 am, 0:00 pm, 6:00 pm, 0:00 am, etc., or at predetermined intervals (eg, every hour, every three hours, every six hours, etc.). , Every 12 hours, etc.).
  • Predetermined locations include, but are not limited to, for example, offices, homes, living rooms, bedrooms, and the like.
  • Predetermined actions include, but are not limited to, for example, sitting in a chair, lying on a bed, entering the field of view of a camera for a certain period of time (for example, 10 seconds, 30 seconds, 1 minute) or longer.
  • the predetermined action may be, for example, to acquire at least one piece of data.
  • data for blood pressure parameters, data for body composition parameters in response to the acquisition of at least one of data for blood pressure parameters, data for body composition parameters, data for autonomic nerve parameters, and data for questionnaire parameters.
  • Data for autonomic nerve parameters, data for questionnaire parameters, and other data can be automatically acquired.
  • the data for the questionnaire parameters in response to the questionnaire processing device acquiring the data for the questionnaire parameters from the result of the subject consciously answering the questionnaire, the data for the blood pressure parameter, the data for the body composition parameter, and the data for the autonomic nerve parameter. May be automatically measured.
  • a plurality of data can be automatically acquired by having the subject consciously perform a simple action of answering the questionnaire.
  • the terminal device 300 performs specific preprocessing on data received from a small number (eg, 10 or less, 5 or less, 4 or less, preferably 3 or less) of measuring devices. After that, the data may be transmitted to the server device 100 for subsequent processing. Certain pre-processing can streamline the subsequent processing of data received from, for example, a small number of measuring instruments.
  • the terminal device 300 may perform specific preprocessing on a part of the data received from a small number of measuring devices, or may perform a specific preprocessing on a part of the data received from a small number of measuring devices. You may want to perform a specific pretreatment. For example, the terminal device 300 performs the first preprocessing on the first subset of the data received from the small number of measuring instruments and the first of the data received from the small number of measuring instruments. A second preprocessing different from the first preprocessing may be performed on the subset of 2. For example, the terminal device 300 performs the first preprocessing on the data acquired by the measurement consciously performed by the subject, and the second preprocess on the data acquired by the measurement unconsciously performed by the subject. Preprocessing can be performed.
  • the terminal device 300 may organize the data received from a small number of measuring devices into a data structure of a specific format and then transmit the data to the server device 100.
  • a data structure of a specific format for example, subsequent processing of data received from a small number of measuring instruments can be streamlined.
  • the terminal device 300 may organize a part of the data received from a small number of measuring devices into a data structure of a specific format, or may organize the data received from a small number of measuring devices into a data structure. All of them may be organized into a data structure of a specific format. For example, the terminal device 300 organizes a first subset of the data received from a small number of measuring instruments into a data structure of the first format, and of the data received from a small number of measuring instruments. The second subset may be organized into a second format data structure that is different from the first format. For example, the terminal device 300 organizes the data acquired by the measurement performed by the subject consciously into the data structure of the first format, and the data acquired by the measurement performed unconsciously by the subject in the second format. Can be organized into a data structure of
  • the terminal device 300 may communicate with, for example, a device that can be connected to a network, for example, an electric product that can be connected to the network (so-called “IoT device”) via an interface unit 310.
  • the terminal device 300 can control the device according to the health level (for example, the position on the health level positioning map).
  • the device may be controlled by combining the data of the surrounding environment (for example, temperature, climate, weather, brightness, etc.) and the health degree (for example, the position on the health degree positioning map). good.
  • the device is, for example, an air conditioner.
  • the terminal device 300 can control the air conditioner to maintain an appropriate room temperature and / or humidity depending on the degree of health.
  • the device is, for example, lighting.
  • the terminal device 300 can control the lighting so as to maintain an appropriate brightness according to the degree of health.
  • the device is, for example, a music player.
  • the terminal device 300 plays music according to the degree of health (for example, music that relaxes the mind and body, music that enhances motivation, yoga, stretching, voice that guides aerobic exercise, etc.). You can control the player.
  • the device is, for example, a video player.
  • the terminal device 300 sets the video player so as to play an appropriate video (for example, a video that relaxes the mind and body, a video that enhances motivation, a video such as yoga, stretching, or aerobic exercise) according to the degree of health. Can be controlled.
  • the device is, for example, an incense generator.
  • the terminal device 300 can control the incense generator so as to emit an appropriate scent (for example, a scent that relaxes the mind and body, a scent that enhances motivation, etc.) according to the degree of health.
  • the device is, for example, a smart speaker.
  • the terminal device 300 is a smart speaker so that it can talk or chat with an appropriate person (for example, a carefree person (for example, family, friends, etc.) to relax) or an appropriate avatar, depending on the degree of health. Can be controlled.
  • the device is, for example, an exercise device.
  • the terminal device 300 can control the exercise device so as to provide an appropriate exercise menu according to the degree of health.
  • the device is, for example, an information terminal.
  • the terminal device 300 can control the information terminal so as to present arbitrary information (for example, a meal, a beverage, or a snack menu, an exercise menu, etc.) according to the degree of health, and the terminal device 300 can control the terminal device 300.
  • the information terminal may be the terminal device 300 itself, or may be a device different from the terminal device 300.
  • the camera 320 is an arbitrary camera capable of shooting a still image or a moving image. It may be a camera built in the terminal device 300, or it may be an external camera attached to the terminal device 300. For example, when the autonomic nerve measuring device is implemented by the terminal device 300, the image taken by the camera 320 can be used to derive data for the autonomic nerve parameters.
  • the camera 320 is, for example, a camera capable of acquiring at least one of data for body composition parameters, data for blood pressure parameters, data for autonomic nerve parameters, and data for questionnaire parameters, for example, all of these data. possible. Thereby, at least one of the body composition meter, the sphygmomanometer, the autonomic nerve measuring device, and the questionnaire processing device, for example, all of these devices can be replaced by the camera 320.
  • the display unit 330 is an arbitrary display that displays a screen.
  • the memory unit 340 stores a program required for executing the processing of the terminal device 300, data required for executing the program, and the like.
  • the memory unit 340 stores a program for causing the processor unit 350 to perform a process for measuring the health degree of the subject (for example, a program for realizing the process shown in FIG. 8 described later).
  • the program may be pre-installed in the memory unit 340.
  • the program may be installed in the memory unit 340 by being downloaded via the network 350. In this case, the type of network does not matter.
  • the memory unit 340 can be implemented by any storage means.
  • the processor unit 350 controls the operation of the entire terminal device 300.
  • the processor unit 350 reads the program stored in the memory unit 340 and executes the program. This makes it possible to make the terminal device 300 function as a device that executes a desired step.
  • the processor unit 350 may be configured to acquire data for blood pressure, for example, by analyzing an image acquired by the camera 320.
  • the processor unit 350 can acquire data on blood pressure by, for example, using a known application and analyzing a moving image of a face acquired by a camera 320.
  • the processor unit 350 can acquire data for blood pressure by, for example, using a known application and analyzing a moving image of a fingertip acquired by a camera.
  • the processor unit 350 may be configured to acquire data for the autonomic nerve function, for example, by analyzing an image acquired by the camera 320.
  • the processor unit 350 can acquire data for the autonomic nervous function from the image by using a method known in the art.
  • the processor unit 350 acquires, for example, an image capable of deriving data for the autonomic nerve parameter by the camera 320, and obtains the acquired image.
  • the data may be transmitted to the server device 100 via the interface unit 310 for analysis.
  • the processor unit 350 does not have the function of deriving the data for the autonomic nerve parameters, and instead, the server device 100 has the function. As a result, the processing load of the terminal device 300 can be reduced.
  • the processor unit 350 presents the questionnaire to the user via, for example, the display unit 330, and answers to the questionnaire input via the interface unit 310. Can be configured to retrieve data for the questionnaire parameters by processing.
  • the processor unit 350 can acquire data for the questionnaire parameters from the answers to the questionnaire by using a method known in the art.
  • the processor unit 350 receives, for example, an answer to the questionnaire and the received answer is analyzed via the interface unit 310. It may be transmitted to the server device 100.
  • the processor unit 350 does not have the function of deriving the data for the questionnaire parameter, and instead, the server device 100 has the function. As a result, the processing load of the terminal device 300 can be reduced.
  • each component of the terminal device 300 is provided in the terminal device 300, but the present invention is not limited to this. It is also possible that any of the components of the terminal device 300 is provided outside the terminal device 300.
  • the camera 320, the display unit 330, the memory unit 340, and the processor unit 350 may be composed of different hardware components, and each hardware component may be connected via an arbitrary network. At this time, the type of network does not matter.
  • Each hardware component may be connected, for example, via the Internet, may be connected via a LAN, may be wirelessly connected, or may be connected by wire.
  • the database unit 200 is provided outside the server device 100, but the present invention is not limited thereto. It is also possible to provide at least a part of the database unit 200 inside the server device 100. At this time, at least a part of the database unit 200 may be implemented by the same storage means as the storage means for mounting the memory unit 150, or may be implemented by a storage means different from the storage means for mounting the memory unit 150. You may. In any case, at least a part 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.
  • the database unit 200 may be composed of a single hardware component or may be composed of a plurality of hardware components.
  • the database unit 200 may be configured as an external hard disk device of the server device 100, or may be configured as a storage on the cloud connected via the network 400.
  • the first parameter set for obtaining the first data set in the present invention is "biooxidation parameter”, “repair energy reduction parameter”, “inflammation parameter” and “. It may include “autonomic nerve function parameters”. In the present specification, the following four parameters, (1) “biooxidation parameter”, (2) “repair energy reduction parameter”, (3) “inflammation parameter”, and (4) "autonomic nerve function parameter” are combined. Therefore, it may be referred to as a basic 4 parameter.
  • the first parameter set is a set of parameters used to create a health positioning map.
  • the first parameter set is also referred to as "a parameter set for creating a health positioning map" in the present specification and claims.
  • ROS reactive oxygen species
  • antioxidant enzymes such as superoxide dismutase (SOD) and catalase, and antioxidants such as coenzyme Q10, vitamin C, and vitamin E are present in the living body.
  • SOD superoxide dismutase
  • the measurement of the "biooxidative parameter" in the present invention may 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.
  • Measurement of reactive oxygen species oxidative damage may be performed by directly measuring the amount of reactive oxygen species, or by measuring oxidative damage of proteins, lipids or nucleic acids.
  • d-ROMs Derivatives of Reactive Oxygen Metalbolites
  • PCC Protein Carbonyl Content
  • 4-hydroxynonenal and isoplastane which are indicators of oxidative damage of lipids
  • 8-OHdG 8-hydroxy-deoxyguanosine
  • the method for measuring the antioxidant capacity is well known in the art, and a person skilled in the art can appropriately select and measure the measurement target.
  • specific markers used as an index of antioxidant capacity are BAP (Biological Antioxidant Potential), which quantifies the reducing power to iron, serum thiol status, glutathione measurement, vitamin C amount measurement, coenzyme Q10 total amount, and coenzyme. Examples include, but are not limited to, the Q10 reduced form ratio.
  • the total amount of coenzyme Q10 and the reduced coenzyme Q10 ratio can be measured using, for example, LC-MS / MS (specifically, for example, from the concentrations of reduced and oxidized coenzyme Q10 detected by multiple reaction monitoring. Can be calculated.).
  • the marker that is an index of the balance between oxidative damage and antioxidant capacity is OSI (oxidative stress index; Oxidation Stress Index), but is not limited thereto.
  • OSI oxidative stress index; Oxidation Stress Index
  • the OSI in the present invention is d-ROMs / BAP.
  • Preferred biooxidation parameters in the present invention include BAP, total amount of coenzyme Q10, coenzyme Q10 reduced ratio, and OSI.
  • biooxidation parameters are parameters that can be measured by non-invasive tests.
  • the "repair energy reduction parameter" in the present invention includes the total amount of coenzyme Q10, the coenzyme Q10 reduced ratio, and the metabolites of the glycolytic / TCA circuit (for example, pyruvic acid, lactic acid, citric acid, isocitric acid, succinic acid, etc.). Fumaric acid, malic acid, etc.), but are not limited to these.
  • the total amount of coenzyme Q10 and the reduced ratio of coenzyme Q10 are not only “biooxidation parameters” because they have antioxidant capacity, but also "repair energy reduction parameters” because they contribute to ATP production.
  • Preferred "repair energy reduction parameters" in the present invention may include coenzyme Q10 total amount and coenzyme Q10 reduced form ratio.
  • the method for measuring the total amount of coenzyme Q10 and the reduced ratio of coenzyme Q10 is as described above.
  • Glycolysis and citric acid cycle metabolites can be measured by extracting compounds that reflect glycolysis and the citric acid cycle from metabolome analysis.
  • the repair energy reduction parameter is a parameter that can be measured by a non-invasive test.
  • inflammation parameter When many tissues damaged by oxidation occur in a living body, a large amount of local inflammation occurs due to an immune response. Types of inflammation parameters and methods of measurement are well known in the art, and those skilled in the art can appropriately select and measure inflammation parameters.
  • Inflammation 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-1 ⁇ , interleukin-6, and the like.
  • the inflammation parameter is a parameter that can be measured by a non-invasive test.
  • autonomic nervous function parameters A chronological scrutiny of health fragility reveals that autonomic function (especially parasympathetic function) declines first, then sleep quality declines, then fatigue accumulates, and then motivation, depressive tendencies, Immune system disorders such as allergies, endocrine system abnormalities such as menstrual insufficiency, and digestive system abnormalities are observed. Therefore, abnormalities in autonomic nervous function are important parameters for understanding the early stages of health fragility.
  • the autonomic nervous function is evaluated from the heart rate parameters.
  • VLF Very Low Frequency ms 2
  • LF low frequency ms 2
  • HF radio frequency ms 2
  • HF radio frequency ms 2
  • LF / HF ratio The ratio of LF (low frequency) to HF (high frequency) power, which represents the overall balance of sympathetic and parasympathetic nerves.
  • a high value indicates sympathetic dominance
  • a low value indicates parasympathetic dominance.
  • the above-mentioned heart rate parameters expressing the autonomic nervous function can be measured by a method known in the art, but can also be measured by an accurate methodology for simultaneously measuring cardiac radio waves and fingertip pulse waves and performing heart rate variability analysis (patented). See No. 5455071, Japanese Patent No. 5491749. These documents are incorporated herein by reference).
  • the autonomic nervous function parameters of the present invention may be measured using a simple autonomic nervous system measuring device FMCC-VSM301 (Fatigue Science Institute, Osaka, Japan) that can simultaneously measure an electrocardiogram and a pulse wave.
  • Preferred "autonomic nerve function parameters" in the present invention may be, but are not limited to, mean HR, TP, LF, HF, LF / HF, and the like. Since the values of HF and LF have a large variance and do not have a normal distribution, it is preferable to perform logarithmic conversion for evaluation of TP, LF, HF and LF / HF related to HF and LF among the above parameters. .. Therefore, more preferred "autonomic function parameters" in the present invention include mean HR, ln (TP), ln (LF), ln (HF), and ln (LF / HF).
  • the autonomic function parameter is a parameter that can be measured by a non-invasive test.
  • the first parameter set of the present invention may include one or more of the following parameters in order to more appropriately create a positioning map for assessing the overall health of the subject.
  • the basic parameters include known parameters that represent the physical condition and health condition of the subject.
  • the basic parameters of the present invention include, but are not limited to, age, height, weight, abdominal circumference, body composition, bone density, blood pressure, muscle strength, body temperature, WH ratio, and the like.
  • Body composition includes, but is not limited to, muscle mass, BMI (body mass index), fat percentage, and the like.
  • Body composition can be, for example, a parameter measured using bioelectrical impedance analysis (BIA).
  • BIA is a technique for quantitatively measuring the components constituting the human body from the impedance generated when an electric current is passed through the human body.
  • Body Composition Analyzer can measure body composition using BIA.
  • Bone density is measured using the MD method, which measures the bones of the hand with X-rays and photographs, the ultrasonic method, which measures the bones of the heel using ultrasonic waves, the QCT method, which uses CT scans, X-rays, and a computer. Measurement methods such as the DEXA method (Dual energy X-ray absorptiometry) are known, and in the present invention, parameters obtained by any of these measurement methods can be used.
  • DEXA method Dual energy X-ray absorptiometry
  • the index is defined with the BMD value (reference value) as 0 and the standard deviation as 1SD), but the present invention is not limited thereto. Methods for measuring these basic parameters are well known in the art.
  • Blood pressure is customarily measured in the art, and systolic blood pressure can be used as the basic parameter of the present invention.
  • Muscle strength is customarily measured in the art, and muscle mass and left-right average grip strength can be used as basic parameters of the present invention.
  • the WH ratio is a value obtained by dividing the waist circumference by the hip circumference, and is used as an index of obesity. Whether the type of obesity is "pear-type obesity" or “apple-type obesity", which represents the body shape of an obese person, can be determined by the WH ratio.
  • the basic parameters of the present invention may preferably include age, muscle mass, BMI, fat percentage, SOS, OI, systolic blood pressure, and left-right average grip strength.
  • Basic parameters are parameters that can be measured by non-invasive tests.
  • the first parameter set further includes blood parameters generally used for evaluating the target renal excretion, hepatobiliary pancreas, and detoxification function system. ..
  • the blood parameters include HbA1c (hemoglobin A1c; glycated protein in which glucose is bound to hemoglobin), ALP (alkaline phosphatase), ALT (alanine aminotransferase), AST (aspartate aminotransferase), BS (blood plasma level), BUN ( Blood urea nitrogen), CK (creatine kinase; plasma muscle cell enzyme that can be used to evaluate motor, skeletal, and muscle function systems), G-GT (gamma, glutamir, transpeptidase), HDL-C (HDL-cholesterol).
  • HbA1c hemoglobin A1c
  • ALP alkaline phosphatase
  • ALT alanine aminotransferase
  • AST aspartate aminotransferase
  • BS blood plasma level
  • BUN Blood urea nitrogen
  • CK Creatine kinase
  • plasma muscle cell enzyme that can be used to evaluate motor, skeletal, and muscle function systems
  • HGB hemoglobin
  • LD lactic acid dehydrogenase
  • LDL-C LDL cholesterol
  • TG triglyceride; neutral fat
  • TOP total protein
  • UA uric acid
  • amylase albumin
  • potassium Creatine
  • chlor cortisol
  • sodium eGFR
  • vitamins eg, vitamin B1
  • minerals iron, copper, calcium, etc.
  • blood parameters are parameters that can be measured by an invasive test.
  • the first parameter of the present invention may further include a cognitive function parameter.
  • the cognitive function parameter of the present invention is TMT (Trail Making), which is a simple cognitive function test in which an index such as a number from 1 to 25 written on a sheet of paper is traced in order with a pencil. Test), ATMT (Advanced Trail Making Test) that performs TMT on the touch panel, modified ATMT (K. Mizuno e al. Brain & Development 33 (2011) 412-420) developed by the present inventors, and the like.
  • TMT, ATMT, and modified ATMT have different methodologies, they have a common object to be measured, and they are all cognitive function parameters of the present invention.
  • the present inventors have prepared, for example, five tasks for evaluating various elements of cognitive function, and these can be used alone or in combination.
  • each of these cognitive tasks may be evaluated based on the total reaction time or the total number of correct answers.
  • Cognitive function parameters are parameters that can be measured by non-invasive tests.
  • the first parameter set of the present invention may also include vascular and skin parameters.
  • Blood vessel parameters include, but are not limited to, blood vessel age, average capillary length, blood vessel turbidity, number of blood vessels, and the like.
  • the average value of capillary length, turbidity of blood vessels, and the number of blood vessels can be easily measured by image processing of the running of capillaries in the fingernail bed. Image processing of capillary running and measurement of these parameters can be measured, for example, by a capillary scope manufactured by Atto Co., Ltd. (Osaka, Japan).
  • Skin parameters include, but are not limited to, the amount of water in the skin of the arm, the amount of water evaporation, gloss, and the like. Methods for measuring the amount of water, the amount of water evaporation, and the luster of the skin of the arm are well known in the art.
  • Blood vessel and skin parameters are parameters that can be measured by non-invasive tests.
  • the first parameter set of the present invention may include the subjective evaluation parameters of the object in addition to the objective parameters obtained by the measurement as described above.
  • subjective evaluation By adding subjective evaluation to the first parameter set in addition to objective parameters based on various measured values, the physical, fatigue, and mental states of the subject, which cannot be grasped from the measurement of various components, can be used as a health positioning map. Can be reflected.
  • the subjective evaluation parameters in the present invention may include subjective evaluations such as fatigue, sleep, and mental state.
  • the subjective evaluation parameters in the present invention may also include subjective evaluations related to personality and temperament.
  • the subjective evaluation of fatigue is calculated using 11 items out of the subjective evaluation of fatigue duration, questions about disorders due to fatigue, fatigue VAS (Symptom Analogue Scale), Fatigue Index (Chalder Fatigue Scale; CFQ), and CFQ. It may include one or more fatigue symptom scores (CFQ11) (Tanaka M et al., Psychol Rep_2010, 106, 2, 567-575), presenteeism questionnaire or VAS, and fatigue questionnaire. "Questions about disability due to fatigue” refers to confirming the subject's subjective evaluation of the presence or absence of a causal relationship between fatigue and some disability.
  • the "question about disability due to fatigue” may be, for example, a question about whether or not fatigue interferes with work, housework, or schoolwork, or a question about a disease that is considered to be the cause of fatigue.
  • the "Fatigue Questionnaire” asks if you are aware of any symptoms of fatigue, such as whether you feel tired or if you feel tired even after sleeping overnight. Can be.
  • the presenteeism questionnaire for example, WHO's Health and Work Performance Questionnaire (HPQ) or Work Limitations Questionnaire (WLQ) may be used.
  • the subjective assessment of sleep may include one or more of the sleep (sleep onset and wake up) times, average sleep time, VAS for drowsiness, and questionnaires for sleep quality.
  • the subjective assessment of mental status may include one or more of the VAS and questionnaire on depression, the VAS and questionnaire on motivation.
  • a "questionnaire about depression” refers to a question about any symptom of depression, which may include whether or not you feel depressed, whether or not you feel uncomfortable with others, for example, K6 total (Kessler et al.)
  • K6 total Kessler et al.
  • One example is the index commonly used to represent mental problems developed by.
  • the "questionnaire" may be evaluated by the answer to a specific question, or the answer to a large number of questions may be evaluated as a score.
  • Subjective parameters are parameters that can be measured by non-invasive tests.
  • the first parameter set of the present invention may include living situation parameters in addition to objective parameters and subjective evaluation parameters.
  • the living condition parameters in the present invention are facts about the living condition of the subject, for example, years of education, marital status, presence / absence of living together, smoking status (presence / absence, frequency and / or amount), drinking status (presence / absence, frequency and / or presence / absence). Or amount), working hours, exercise (presence / absence, frequency and / or amount), dietary status (whether you feel that you eat fast, snack frequency after dinner, frequency of not eating breakfast, etc.), history, medication status , Supplement intake status, etc. may be included.
  • Living condition parameters are parameters that can be measured by non-invasive tests.
  • the first parameter set of the present invention may include parameters based on data on brain function / neuropsychiatric evaluation, cardiovascular / respiratory function evaluation, renal excretion / hepatobiliary pancreatic / detoxification function evaluation.
  • MRI Magnetic Resonance Imaging
  • nerve fascicle running anisotropy nerve fascicle size and robustness
  • data on cardiovascular and respiratory function evaluation include blood flow (for example, which can be measured by a Doppler blood flow meter), breath gas component analysis (NO (zensoku), acetone (diabetes), etc.). May include. Data related to breath gas component analysis can be measured by mass spectrometry or analysis with an ion mobility analyzer.
  • Data on renal excretion, hepatobiliary pancreas, and detoxification function system evaluation may include data such as skin gas component analysis in addition to the above-mentioned data.
  • Data on skin gas component analysis can be measured by mass spectrometry and high-sensitivity variable laser detectors.
  • the first parameter set of the present invention may include biooxidation parameters, repair energy reduction parameters, inflammation parameters, and autonomic nerve function parameters (basic 4 parameters).
  • biooxidation parameters repair energy reduction parameters
  • inflammation parameters inflammation parameters
  • autonomic nerve function parameters basic 4 parameters
  • the first parameter set of the present invention may include four basic parameters, basic parameters, cognitive function parameters, and subjective parameters.
  • the subjective parameters include an assessment of one or more of fatigue, sleep, and mental states, and more preferably include at least an assessment of fatigue.
  • the first parameter set of the present invention may include four basic parameters, basic parameters, cognitive function parameters, subjective parameters, and blood parameters. Although not intended to be bound by theory, it accurately assesses endocrine function in addition to the four basic parameters, basic parameters for assessing fatigue and mental state, cognitive function parameters, and subjective parameters. Adding the possible blood parameters to the first parameter set allows for a wide range of health assessments, including even different perspectives.
  • the first parameter set of the present invention may include four basic parameters, basic parameters, cognitive function parameters, subjective parameters, blood parameters, vascular and skin parameters, and living conditions parameters.
  • the first parameter set of the present invention typically includes both invasive and non-invasive parameters.
  • the positioning map on which the evaluation is based reflects the information of the invasive parameters.
  • the first parameter set is obtained, for example, by acquiring a data set of an initial parameter set, correlating each data of the data set of the initial parameter set, and extracting parameters having a correlation coefficient equal to or higher than a predetermined threshold. It may be an extraction parameter set obtained by extracting a parameter set having a high influence on the health positioning map from the data set of the initial parameter set by machine learning.
  • 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 condition of the subject can be appropriately shown on the health positioning map by using a smaller number of parameter sets than the first parameter set.
  • the second parameter set is also simply referred to as a "parameter set”.
  • the second parameter set may include items that are not in the first parameter set.
  • the second parameter set may consist only of non-invasive parameters.
  • the positioning map for evaluating this second parameter set may be created including invasive parameters, the target is comprehensively used using the second parameter set consisting only of non-invasive parameters.
  • the target is comprehensively used using the second parameter set consisting only of non-invasive parameters.
  • estimating the user's health level from a parameter set that does not include such an invasive test result enables the user to easily know his / her health level regardless of the test location.
  • users will be able to know their health level from simple tests at companies, pharmacies, public halls, cafes, homes, and the like.
  • a second parameter set consisting only of non-invasive parameters may include the following parameters: (1) Body composition parameters (2) Autonomic nerve parameters (3) Questionnaire parameters
  • (2) autonomic nerve parameters may include data on pulse waves.
  • (3) Questionnaire parameters may include, for example, age, living conditions parameters, and / or subjective evaluation parameters.
  • the subjective evaluation parameters included in the questionnaire parameters may include a subjective evaluation regarding QOL (Quality of Life), a subjective evaluation regarding fatigue, and a subjective evaluation regarding psychology.
  • the body composition parameter can be at least a part of the basic parameters of the first parameter set described above.
  • the body composition parameter may include at least one of the BMI and WH ratios, more preferably both the BMI and WH ratios. This makes it possible to correlate the visually easy-to-understand index of body shape with the state of the subject on the health positioning map.
  • the BMI and WH ratio may or may not be included in the first parameter set.
  • BMI may be included in the first parameter set and WH ratio may not be included in the first parameter set.
  • the autonomic nerve parameters can be at least a part of the autonomic nerve function parameters of the first parameter set described above.
  • Questionnaire parameters can be at least part of the basic parameters, living conditions parameters, and / or subjective evaluation parameters of the first parameter set described above.
  • (1) Data for body composition parameters are measured by a body composition analyzer, (2) data for autonomic nerve parameters are measured by an autonomic nerve measuring instrument, and (3) data for questionnaire parameters are measured by a questionnaire processing device. Can be measured by.
  • the parameters (1) to (3) are parameters that can be measured by at most three measuring instruments. This reduces the burden on the user for measurement and facilitates measurement at a place other than the inspection facility such as home.
  • one or both of the body composition parameters and the autonomic nerve parameters are measured automatically and unconsciously without the user's awareness, and those parameters are combined with the latest questionnaire parameters. An overall health assessment can be made on the user's health positioning map.
  • the second parameter set in this embodiment is (4) Blood pressure parameters may be further included.
  • Blood pressure parameters may be further included.
  • the accuracy of predicting the degree of health is improved as compared with the case of only the parameters (1) to (3).
  • any one, any two, or all three of the body composition parameters, autonomic nerve parameters, and blood pressure parameters are unconsciously and automatically measured without the user being aware of it. , These parameters can be combined with the latest questionnaire parameters to make an overall health assessment on the user's health positioning map.
  • Data for blood pressure parameters can be measured by a sphygmomanometer.
  • the parameters (1) to (4) are parameters that can be measured by at most four measuring instruments. As a result, while improving the accuracy of prediction, the burden on the user for measurement is still reduced, and measurement can be easily performed at any place such as home and at any time.
  • the second parameter set does not include parameters that are complicated to measure or that take time to measure, even among non-invasive parameters.
  • the second parameter set does not include cognitive function parameters and bone density parameters.
  • the second parameter set consists of parameters that can be easily measured using a simple measuring device, and in one embodiment, it consists of parameters that can be measured using at most four measuring devices. In the embodiment, it is composed of parameters that can be measured by using at most three measuring instruments, and in still another embodiment, it is composed of parameters that can be measured by using one measuring instrument.
  • the above-mentioned automatically measurable parameter is a predetermined condition (for example, contact with various devices, or various devices and the user) with each parameter measuring device within a time range set by the user. It can be measured automatically without the user being aware of it, triggered by (such as when the distance falls within a certain range).
  • FIG. 5A shows an example of processing in the server device 100.
  • the process 500 is a process for creating a health degree positioning map.
  • the process 500 is executed in the processor unit 120 of the server device 100.
  • the acquisition means 121 of the processor unit 120 acquires the first data set for the first parameter set for each of the plurality of subjects.
  • the acquisition means 121 can, for example, receive data about a plurality of subjects stored in the database unit 200 via the interface unit 110 and acquire the received data.
  • the acquisition means 121 receives, for example, data about a plurality of subjects stored in the database unit 200 from a computer system of an examination facility (for example, a hospital, a laboratory, etc.) via the interface unit 110, and the received data. Can be obtained.
  • step S502 the processing means 122 of the processor unit 120 processes the first data set acquired in step S501 to obtain the first data.
  • the processing by the processing means 122 may include, for example, at least one of a dimension reduction processing, a standardization processing, and a weighting processing for the first data set.
  • the processing by the processing means 122 includes the dimension reduction processing for the first data set.
  • This makes it possible to reduce the dimensions of the multidimensional and complex first dataset and create more comprehensible data, and thus health positioning maps.
  • the dimension reduction process reduces the first data set to two-dimensional data or three-dimensional data. This is because the health degree positioning map created from the two-dimensional data or the three-dimensional data becomes a map of the two-dimensional space or the three-dimensional space and is easy to understand visually.
  • the dimension reduction processing (1) the contribution of each measurement item to the value of each axis of the health degree positioning map can be obtained, and (2) the measurement that does not greatly affect the value of each axis of the health degree positioning map. Except for the item data, you will be able to understand the data on the health positioning map more clearly.
  • the processing by the processing means 122 includes a standardization processing for the first data set and a dimension reduction processing for the standardized data set. This eliminates the scale difference between the parameters of the first dataset, and evenly considers the impact of each parameter of the first dataset on the health positioning map, resulting in highly accurate and easy-to-understand health positioning. This is because you can create a map. In particular, for example, by performing standardization processing on parameters caused by gender differences between men and women, both data obtained from male subjects and data obtained from female subjects can be evaluated on the same health positioning map. Become.
  • 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 dimension reduction process for the standardized data set. This makes it possible to emphasize the magnitude of the influence of each parameter of the first data set on the health degree positioning map, and to create a health degree positioning map that is more accurate and easy to understand.
  • the standardization process by the processing means 122 may be performed for all the parameters in the first parameter set, or may be performed for a specific parameter, for example.
  • the weighting process by the processing means 122 may be performed, for example, on the entire first data set of a plurality of subjects, or may be performed on a first data set of a specific population of a plurality of subjects. good.
  • the mapping means 123 of the processor unit 120 maps the first data obtained in step S502 for each of the plurality of subjects.
  • the mapping means 123 maps the n-dimensional first data on the n-dimensional space.
  • the mapping means 123 can output a map to which the first data of each of the plurality of subjects is mapped. For example, when the first data is two-dimensional, the mapping means 123 can output a two-dimensional map by mapping the first data to a two-dimensional space, that is, a position on a plane.
  • step S504 the clustering means 124 of the processor unit 120 identifies a plurality of regions by clustering the first data mapped in step S503.
  • the clustering means 124 can specify an arbitrary number of regions by dividing the mapped first data into an arbitrary number of clusters.
  • the characterization means 125 of the processor unit 120 characterizes at least a portion of the plurality of regions identified in step S504.
  • the characterization means 125 may characterize at least a part of the plurality of regions based on the information input to the server device 100, for example, or may characterize at least a part of the plurality of regions without depending on the input. At least some may be characterized automatically. For example, characterization means 125 may characterize at least a portion of a plurality of regions based on relative position in a health positioning map, or at least one of a plurality of regions based on machine learning. The part may be characterized.
  • the above-mentioned process 500 creates a health positioning map in which at least a part of a plurality of regions is characterized.
  • the created health degree positioning map can be used in the processes 510, 600, and 700 described later.
  • FIG. 5B shows another example of processing in the server device 100.
  • the process 510 is a process of creating a health degree positioning map for the data included in a part of the health degree positioning map created by the process 500.
  • the process 510 is executed in the processor unit 120 of the server device 100.
  • step S511 the processor unit 120 receives an input for selecting a part of a plurality of areas of the health degree positioning map created in the process 500.
  • the input for selecting a part of the plurality of areas is input from the outside of the server device 100 via the interface unit 110, for example.
  • 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.
  • step S513 the clustering means 124 of the processor unit 120 identifies a plurality of regions by clustering the sub-first data acquired in step S512.
  • the process in step S513 may be the same process as the process in step S504.
  • step S514 the characterization means 125 of the processor unit 120 characterizes at least a portion of the plurality of regions identified in step S513.
  • the process in step S514 may be the same process as the process in step S505.
  • a health degree positioning map is created for some of the subjects among the plurality of subjects.
  • the health positioning map for some of the subjects may be, for example, a specific population of the subjects, such as a male subject population, a female subject population, or a young population (group under 40 years old).
  • a health positioning map focusing on a middle-aged group a group aged 40 to 60 years old
  • an elderly group a group aged 60 years or older
  • Multiple areas of a health positioning map focused on a particular population can have different characterization than multiple areas of the overall health positioning map of multiple subjects, allowing analysis of health status from different perspectives. Can be used to do.
  • FIG. 6 shows another example of processing in the server device 100.
  • Process 600 is a process for creating a health function.
  • the process 600 is executed in the processor unit 130 of the server device 100.
  • the processor unit 130 prepares a health degree positioning map.
  • the processor unit 130 can prepare the health degree positioning map by acquiring the health degree positioning map by the first acquisition means of the processor unit 130.
  • the health positioning map is created based on the first data set for the first parameter set described above for a plurality of subjects.
  • the prepared health degree positioning map may be a health degree positioning map created by process 500 or process 510 as long as it is created using the first parameter set, or may be an otherwise created health degree. It may be a positioning map.
  • step S602 the second acquisition means 132 of the processor unit 130 acquires the second data set for the second parameter set for at least a part of the plurality of subjects.
  • the second parameter set is part of the first parameter set.
  • the second acquisition means 132 can acquire, for example, data about a part of a plurality of subjects stored in the database unit 200 via the interface unit 110.
  • the derivation means 133 of the processor unit 130 determines the second data set of at least a part of the subjects acquired in step S602 and the health of at least a part of the subjects. Derive a health function that correlates with position on the degree positioning map.
  • the derivation means 133 can derive a health function by machine learning, for example.
  • 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.
  • the deriving means 133 uses the second data set as an independent variable and the coordinates on the health positioning map of the subject as the dependent variable for at least a part of each subject among the plurality of subjects. By machine learning as, each coefficient of the regression model can be derived.
  • the deriving means 133 uses a second data set as input teacher data for at least a part of each subject among a plurality of subjects, and positions the subjects on the health positioning map. By machine learning as the output teacher data, the weighting coefficient of each node can be derived.
  • the above-mentioned process 600 creates a health function for mapping the health level of the subject on the health level positioning map.
  • the created health function can be used in the process 700 described later.
  • the resulting health function is on the health positioning map from the data of the parameter set that does not include the invasive test result. It will be possible to identify the location and estimate the health of the user. Estimating a user's health level from a parameter set that does not include such invasive test results allows the user to know his or her health level regardless of the location of the test. In addition to hospitals, users will be able to know their health level from simple tests at companies, pharmacies, public halls, cafes, homes, and the like.
  • FIG. 7A shows another example of processing in the server device 100.
  • the process 700 is a process for estimating the health level of the user.
  • the process 700 is executed in the processor unit 140 of the server device 100.
  • the processor unit 140 prepares a health function.
  • the processor unit 140 can prepare the health function by acquiring the health function by the third acquisition means 141 of the processor unit 140.
  • the health function is a function that correlates the data set for the second parameter set with the position on the health positioning map.
  • the obtained health function may or may not be the health function created by process 600, as long as the user dataset can be correlated with its position on the health positioning map. It may be a health function.
  • 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 may be an otherwise created health degree map. It may be a positioning map.
  • the fourth acquisition means 142 of the processor unit 140 acquires the first user data set for the second parameter set of the user.
  • the fourth acquisition means 142 can acquire, for example, the first user data set stored in the database unit 200 via the interface unit 110.
  • the fourth acquisition means 142 can acquire, for example, the first user data set from the user's terminal device via the interface unit 110.
  • step S703 the output generation means 143 of the processor unit 140 obtains the first output by inputting the first user data set into the health function.
  • the health function is a regression model
  • the coordinates on the health positioning map are output as the first output.
  • the health function is a neural network model
  • the coordinates on the health degree positioning map are output as the first output.
  • step S704 the output mapping means 144 of the processor unit 140 maps the first output on the health degree positioning map. Since the output obtained in step S703 is the coordinates on the health degree positioning map, the output mapping means 144 can map the coordinates in the n-dimensional space of the health degree positioning map.
  • the process 700 described above it is possible to estimate what the user's health is from the characterization of the area to which the user's health is mapped. Can be done. For example, by using a health function that does not include the invasive test result in the second parameter set, the position on the health positioning map is specified from the data of the parameter set that does not include the invasive test result, and the health level of the user is estimated. Can be done. Estimating a user's health level from a parameter set that does not include such invasive test results allows the user to know his or her health level regardless of the location of the test. In addition to hospitals, users will be able to know their health level from simple tests at companies, pharmacies, public halls, cafes, homes, and the like.
  • the estimated health status can be used to correct other data.
  • Other data can be, for example, data that can vary depending on the state of health.
  • other data includes, but is not limited to, the results of tests including, but not limited to, cognitive tests, physical fitness tests, academic achievement tests, and the like.
  • a user whose test result is poor because of poor health can be corrected to add points to the test result.
  • FIG. 7B shows an example of a continuation process of the process 700 shown in FIG. 7A.
  • the process shown in FIG. 7B is a process for estimating the health level of the user after a lapse of a predetermined period.
  • step S705 the fourth acquisition means 142 of the processor unit 140 acquires the second user data set for the second parameter set of the user.
  • Step S705 is performed at least after a predetermined period of time has elapsed from step S702.
  • the fourth acquisition means 142 can acquire, for example, the second user data set stored in the database unit 200 via the interface unit 110.
  • the fourth acquisition means 142 can acquire, for example, the second user data set from the user's terminal device via the interface unit 110.
  • step S706 the output generation means 143 of the processor unit 140 obtains the second output by inputting the second user data set into the health function.
  • the health function is a regression model
  • the coordinates on the health positioning map are output as the second output.
  • the health function is a neural network model
  • the coordinates on the health positioning map are output as the second output.
  • step S707 the output mapping means 144 of the processor unit 140 maps the second output on the health degree positioning map. Since the output obtained in step S706 is the coordinates on the health degree positioning map, the output mapping means 144 can map the coordinates in the n-dimensional space of the health degree positioning map.
  • steps S705 to S707 it is possible to estimate what the user's health condition will be after a predetermined period. For example, by comparing the health state estimated in steps S701 to S704 with the health state estimated by steps S705 to S707, the time-series change in the health state can be specified.
  • the degree of health belongs to the area characterized as a healthy body on the health degree positioning map
  • the degree of health is as a healthy body. If you belong to a characterized area but approach the area characterized as a lifestyle-related disease risk group, you can predict that your health is heading towards a lifestyle-related disease risk.
  • the treatment 700 can be used to evaluate items for improving health (eg, pharmaceuticals, food and drink, health appliances, etc.). For example, by having the user use an item for improving the health condition for a predetermined period and comparing the first output before the predetermined period with the second output after the predetermined period has elapsed, on the health positioning map. It is possible to identify the time-series change in.
  • the time-series changes on the health positioning map due to the use of items to improve health reflect the effects of items to improve health, and use items to improve health. By contrasting with the time-series changes on the health positioning map when not present, it is possible to evaluate the superiority or inferiority of the effect of the item for improving the health condition.
  • an item for improving health generally refers to a time on a health positioning map that is opposite to the direction of time-series changes on the health positioning map due to the use of the item for improving health.
  • an item for improving health Effective for users with series changes. Therefore, it is possible to recommend to the user an item having a time-series change in the direction opposite to the time-series change on the user's health positioning map identified by the processes of steps S701 to S707.
  • the health of the subject is evaluated by pattern matching a locus including the first position of the result of mapping the first output and the second position of the result of mapping the second output after a predetermined period has elapsed. can do. This can be achieved, for example, by learning the locus pattern of a healthy person and the locus pattern of a subject having a specific disease risk.
  • the treatment 700 can be used to assess the effect of irritation on health. For example, by stimulating the user for a predetermined period and comparing the first output before the predetermined period with the second output after the elapse of the predetermined period, the time-series change on the health positioning map is specified. be able to.
  • the time-series changes on the health positioning map due to the stimulus reflect the effect of the stimulus on the health condition, and contrast with the time-series changes on the health positioning map without the stimulus. By doing so, it is possible to evaluate the effect of the stimulus on the health condition.
  • the stimulus includes, but is not limited to, for example, a stimulus to the sense of touch, a stimulus to the sense of smell, a stimulus to the sense of sight, a stimulus to the sense of taste, a stimulus to the auditory sense, and a stimulus to the external environment.
  • Tactile stimuli may include, for example, massage, acupoint pressing, vibration and the like.
  • the stimulus to the sense of smell may include, for example, smelling a specific odor.
  • Taste stimuli may include, for example, eating sweet foods, eating sour foods, eating salty foods, eating bitter foods, eating umami foods, and the like.
  • Visual stimuli can include, for example, viewing a particular still image, watching a particular moving image, seeing a particular color, seeing an object under lighting of a particular brightness, and the like.
  • Hearing stimuli may include, for example, listening to specific music, listening to noise, listening to high notes (eg, sounds above about 1000 Hz), listening to low notes (eg, sounds below about 100 Hz), and the like.
  • Stimulation by the external environment may include, for example, changes in the external environment, the presence of substances in the external environment (eg, viruses, allergens, NOx, etc.) and the like.
  • a stimulus for improving the health condition is generally a user who has a time-series change on a health-positioning map that is opposite to the direction of the time-series change on the health-positioning map due to the stimulus. It is effective for. Therefore, it is possible to recommend to the user a stimulus having a time-series change in the direction opposite to the time-series change on the user's health positioning map identified by the processes of steps S701 to S707.
  • the treatment 700 can be used to assess the effect of the event on health. For example, by comparing the first output before the event and the second output after the event, it is possible to specify the time-series change on the health positioning map.
  • the time-series changes on the health positioning map due to the experience of the event reflect the effect of the event on the health condition, and the time-series changes on the health positioning map when the event is not experienced.
  • the impact of the event on health can be assessed.
  • the event is, for example, an epidemic of an infectious disease (Covid-19, etc.), a natural disaster (for example, damage caused by a storm, heavy rain, heavy snow, flood, high tide, earthquake, tsunami, eruption, or other abnormal natural phenomenon). Includes, but is not limited to, overtime, experience (direct or pseudo-experience (eg, experience through VR (virtual reality), AR (augmented reality), etc.), communication with others or animals, etc.) .
  • experience direct or pseudo-experience
  • VR virtual reality
  • AR augmented reality
  • FIG. 8 shows an example of a data flow in the method implemented in the computer system 10 of the present invention.
  • FIG. 8 shows a 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.
  • step S801 the measuring device acquires a data set for the parameter set.
  • the parameter set corresponds to the above-mentioned second parameter set.
  • the parameter set can include autonomic nerve parameters, body composition parameters, and questionnaire parameters.
  • step S801 I. Acquiring data for autonomic nerve parameters using an autonomic nerve measuring instrument, II. Obtaining data for body composition parameters using a body composition analyzer III. It can include acquiring data for questionnaire parameters using a questionnaire processing device.
  • the autonomic nerve measuring device, the body composition analyzer, and the questionnaire processing device may be separate devices, each may be mounted by the same device, or at least two may be mounted by the same device. May be done.
  • at least two of the autonomic nerve measuring device, the body composition meter, and the questionnaire processing device may be implemented by the terminal device 300.
  • a data set is acquired using at most three measuring instruments, for example, a data set is acquired using two measuring instruments, and for example, a data set is acquired using one measuring instrument. Is obtained. The less measuring equipment is used, the less the burden on the user for measurement.
  • step S801 is I. Acquiring data for autonomic nerve parameters using an autonomic nerve measuring instrument, II. Obtaining data for body composition parameters using a body composition analyzer III. Acquiring data for questionnaire parameters using a questionnaire processing device, IV. It can include acquiring data for blood pressure parameters using a sphygmomanometer.
  • the autonomic nerve measuring device, the body composition meter, the questionnaire processing device, and the sphygmomanometer may be separate devices, each may be mounted by the same device, or at least two of them are the same. It may be implemented by the device. Alternatively, at least two of an autonomic nerve measuring device, a body composition meter, a questionnaire processing device, and a sphygmomanometer may be implemented by the terminal device 300.
  • the data set is acquired using at most four measuring instruments, for example, the data set is acquired using three measuring instruments, and the data set is acquired using, for example, two measuring instruments. Is acquired, for example, a data set is acquired using one measuring device. The less measuring equipment is used, the less the burden on the user for measurement.
  • the subject may consciously or unconsciously acquire the data set for the parameter set. Acquiring a data set for a parameter set can be performed without making the subject aware, for example, by automatically performing measurement when a predetermined condition is satisfied.
  • Predetermined conditions are, for example, temporal conditions (eg, between 6 am and 8 am, between 11 am and 1 pm, between 4 pm and 6 pm, between 9 pm and midnight, or 6 am.
  • 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.
  • the processor unit 350 is I. Obtaining data for autonomic nerve parameters from the autonomic nerve measuring instrument, II. Obtaining data for body composition parameters from the body composition analyzer and III. The data set can be obtained by acquiring the data for the questionnaire parameters from the questionnaire processing device.
  • the processor unit 350 is I. Obtaining data for autonomic nerve parameters from the autonomic nerve measuring instrument, II. Obtaining data for body composition parameters from the body composition analyzer and III. Obtaining data for the questionnaire parameters from the questionnaire processing device, IV. A dataset can be obtained by obtaining data for blood pressure parameters from a sphygmomanometer.
  • step S802 The timing at which step S802 is performed after step S801 does not matter. For example, each time the data set of one of the parameter sets is acquired in step S801, the processor unit 350 of the terminal device 300 may acquire the data set in step S801, or in step S801. After the data set of all the parameters of the parameter set is acquired, the processor unit 350 of the terminal device 300 may acquire the data set in step S802.
  • step S802 may be omitted.
  • step S803 the processor unit 350 of the terminal device 300 transmits a 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 a data set via the interface unit 100.
  • step S803 The timing at which step S803 is performed after step S802 does not matter. For example, each time the data set of one of the parameter sets is acquired in step S802, the processor unit 350 of the terminal device 300 may transmit the data set in step S803, or in step S802. After the data set of all the parameters of the parameter set is acquired, the processor unit 350 of the terminal device 300 may transmit the data set in step S803.
  • the server device 100 when an image acquired for analysis for deriving an autonomic nerve parameter is transmitted to the server device 100, the server is used each time the image is acquired or separately from the data set of other parameters. It is preferable to transmit to the device 100. This is because the image has a large data capacity, which may lead to a delay in communication.
  • step S804 the processor unit 140 of the server device 100 derives health degree information using a health function that correlates the data set with the position on the health degree positioning map of the subject.
  • the health information includes coordinates on the health positioning map (eg, X-axis values, Y-axis values).
  • the processor unit 140 can derive health level information by, for example, the processes of steps S701 to S703 of the process 700.
  • 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 health information via the interface unit 310.
  • the processor unit 140 of the server device 100 transmits health level information to the measuring device measured in step S801 via the interface unit 110.
  • the measuring device will receive the health information (for example, the coordinates on the health positioning map (for example, the value on the X-axis and the value on the Y-axis)).
  • step S806 the processor unit 350 of the terminal device 300 expresses the health degree of the subject as a position on the health degree positioning map of the health degree information.
  • the health degree information can be shown on the health degree positioning map based on the X-axis value and the Y-axis value included in the health degree information.
  • the processor unit 350 can display a health degree positioning map on the display unit 330 and plot the coordinates indicated by the health degree information on the displayed health degree positioning map.
  • the measuring device measured in step S801 has health information based on the X-axis value and the Y-axis value included in the health information. Will be shown on the health positioning map.
  • the terminal device 300 receives the data set (or the terminal device 300 transmits the data set to the server device 100 in step S803). Then, in step S806, the health level of the subject can be expressed as a position on the health level positioning map of the health level information within a predetermined time.
  • the predetermined time is, 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.
  • the user can immediately know his / her health condition. Due to such good responsiveness, the psychological burden on the user for the measurement can be reduced, and the user's motivation for the measurement can be prevented from being lowered.
  • the health level information transmitted from the server device 100 includes at least the coordinates on the health level positioning map (for example, the value on the X-axis and the value on the Y-axis).
  • the predetermined time can be further shortened.
  • the processor unit 350 of the terminal device 300 that has received the health level information can display the health level information on the health level positioning map only by plotting the coordinates on the positioning map, so that the terminal device 300 can display the health level information.
  • the process is simplified, and the time from receiving the health information to displaying it can be shortened. For example, since the amount of data transfer of the transmitted health degree information can be reduced, it is possible to prevent a decrease in communication speed. As a result, the health level information can be transmitted at high speed, so that the data transfer time can be reduced and the time required for transmitting the health level information can be shortened.
  • the health information received in step S805 and / or the position on the health positioning map expressed in step S806 can be stored in the memory unit 340 of the terminal device 300.
  • 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, for correction of other data.
  • Other data can be, for example, data that can vary depending on the state of health.
  • other data includes, but is not limited to, the results of tests including, but not limited to, cognitive tests, physical fitness tests, academic achievement tests, and the like.
  • a subject whose test result is poor because of poor health can be corrected to add points to the test result.
  • the position on the health positioning map represented in step S806 can be used, for example, to control other devices that can be connected to the network.
  • the other device may be an electrical product (so-called "IoT device") that can be connected to a network.
  • the processor unit 350 of the terminal device 300 can generate an instruction for controlling the device based on the position on the health degree positioning map. Next, the processor unit 350 of the terminal device 300 can control the device according to the command by transmitting the command to the device via the network.
  • 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 control the operation of the device according to the command by generating a command to control the device based on the position on the health positioning map.
  • the device is, for example, an air conditioner.
  • the air conditioner can be controlled to maintain adequate room temperature and / or humidity depending on its location on the health positioning map.
  • the device is, for example, lighting.
  • the lighting can be controlled to maintain proper brightness depending on its position on the health positioning map.
  • the device is, for example, a music player.
  • the music player can be controlled to play appropriate music (eg, music that relaxes the mind and body, music that enhances 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 an appropriate video (eg, a video that relaxes the mind and body, a video that enhances motivation, etc.) depending on the position on the health positioning map.
  • the control of such other devices is performed after the terminal device 300 receives the data set (or after the terminal device 300 transmits the data set to the server device 100 in step S803). It will be more useful if the health level of the subject is expressed as a position on the health level positioning map of the health level information within a predetermined time. That is, by acquiring the position on the health degree positioning map used for control within a predetermined time, particularly in substantially real time, it becomes possible to achieve real-time control according to the current health of the subject.
  • the health degree information derived in step S804 can be stored in the database unit 200 and used for research by another user. It will be like.
  • the health degree of the subject can be evaluated based on the locus including at least two positions obtained. For example, pattern matching a locus including the first position obtained in the first steps S801 to S805 and the second position obtained in the second steps S801 to S805 after a predetermined period has elapsed. Therefore, the health level of the subject can be evaluated. This can be achieved, for example, by learning the locus pattern of a healthy person and the locus pattern of a subject having a specific disease risk.
  • the predetermined period is an arbitrary period.
  • the predetermined period is, for example, half a day, one day, one week, two weeks, January, March, six months, one year, and the like.
  • the health level is evaluated from the chronotype of the health level of the subject. It can provide an indicator of how much fatigue has accumulated, for example, due to daily activity.
  • the predetermined period is the sleep time
  • the health level is evaluated from the nighttime fluctuation of the health level of the subject. This can provide an indicator of how much recovery has been achieved, for example, by sleeping at night.
  • 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 passes through the interface unit 310 to the server.
  • the data set is transmitted to the device 100
  • the present invention is not limited to this.
  • the measuring device is a server-linked type, the measuring device can directly transmit the data set to the server device 100. Therefore, step S802 can be omitted.
  • an item for improving a health condition (for example, a drug, a food or drink, a health appliance, etc.) can be evaluated based on a locus including at least two positions obtained. For example, in the first position obtained in the first steps S801 to S805, and after allowing the user to use the item for improving the health condition for a predetermined period, in the second steps S801 to S805.
  • a locus including at least two positions obtained For example, in the first position obtained in the first steps S801 to S805, and after allowing the user to use the item for improving the health condition for a predetermined period, in the second steps S801 to S805.
  • the time-series changes on the health positioning map due to the use of items to improve health reflect the effects of items to improve health, and use items to improve health.
  • By contrasting with the time-series changes on the health positioning map when not present it is possible to evaluate the superiority or inferiority of the effect of the item for improving the health condition.
  • an item for improving health generally refers to a time on a health positioning map that is opposite to the direction of time-series changes on the health positioning map due to the use of the item for improving health.
  • an item for improving health Effective for users with series changes. Therefore, it is possible to recommend to the user an item having a time-series change in the direction opposite to the time-series change on the user's health positioning map identified by the process of step S806.
  • step S806 can be used to assess the effect of irritation on health status. For example, the first position obtained in the first steps S801 to S805 and the second position obtained in the second steps S801 to S805 after stimulating the user for a predetermined period. By comparing, it is possible to identify the time-series changes on the health positioning map. The time-series changes on the health positioning map due to the stimulus reflect the effect of the stimulus on the health condition, and contrast with the time-series changes on the health positioning map without the stimulus. By doing so, it is possible to evaluate the effect of the stimulus on the health condition.
  • the stimulus includes, but is not limited to, for example, a stimulus to the sense of touch, a stimulus to the sense of smell, a stimulus to the sense of sight, a stimulus to the sense of taste, and a stimulus to the auditory sense.
  • Tactile stimuli may include, for example, massage, acupoint pressing, vibration and the like.
  • the stimulus to the sense of smell may include, for example, smelling a specific odor.
  • Taste stimuli may include, for example, eating sweet foods, eating sour foods, eating salty foods, eating bitter foods, eating umami foods, and the like.
  • Visual stimuli can include, for example, viewing a particular still image, watching a particular moving image, seeing a particular color, seeing an object under lighting of a particular brightness, and the like.
  • Hearing stimuli may include, for example, listening to specific music, listening to noise, listening to high notes (eg, sounds above about 1000 Hz), listening to low notes (eg, sounds below about 100 Hz), and the like. ..
  • a stimulus for improving the health condition is generally a user who has a time-series change on a health-positioning map that is opposite to the direction of the time-series change on the health-positioning map due to the stimulus. It is effective for. Therefore, it is possible to recommend to the user a stimulus having a time-series change in the direction opposite to the time-series change on the user's health positioning map identified by the processes of steps S701 to S707.
  • step S806 can be used to assess the impact of the event on health.
  • health is achieved by comparing the first position obtained in the first steps S801 to S805 before the event with the second position obtained in the second steps S801 to S805 after the event. It is possible to identify time-series changes on the degree positioning map. The time-series changes on the health positioning map due to the experience of the event reflect the effect of the event on the health condition, and the time-series changes on the health positioning map when the event is not experienced. By contrasting, the impact of the event on health can be assessed.
  • the event is, for example, an epidemic of an infectious disease (Covid-19, etc.), a natural disaster (for example, damage caused by a storm, heavy rain, heavy snow, flood, high tide, earthquake, tsunami, eruption, or other abnormal natural phenomenon).
  • a natural disaster for example, damage caused by a storm, heavy rain, heavy snow, flood, high tide, earthquake, tsunami, eruption, or other abnormal natural phenomenon.
  • experiences directly or pseudo-experience (eg, experience through VR (virtual reality), AR (augmented reality), etc.), communication with others or animals, etc.) ..
  • step S806 can be used to evaluate the effect of the control of the device on the health condition and to further control the device in consideration of the evaluation. For example, the first position obtained in the first steps S801 to S805 and the second position obtained in the second steps S801 to S805 after the device is controlled according to the first position. Based on the above, a second command for controlling the device can be generated. For example, by comparing the first position and the second position, it is possible to identify the time-series change on the health positioning map. The time-series changes on the health positioning map due to the control of the device reflect the effect of the control of the device on the health condition.
  • a second command can be generated to continue to control the device and the device can be controlled according to the second command. ..
  • the effect of controlling the device on the health condition is a negative effect, stop controlling the device or generate a second command to control in the opposite direction, and the second command is generated.
  • the equipment can be controlled according to the instructions.
  • the second step after the air conditioner is controlled to maintain an appropriate room temperature according to the first position obtained in the first steps S801 to S805. If the second position obtained in steps S801 to S805 has moved to a position in poorer health than (1) the first position, the air conditioner is controlled to raise or lower the room temperature. And (2) the air conditioner can be controlled to maintain the room temperature if it has moved to a better health position than the first position.
  • the second step after the lighting is controlled to maintain proper brightness according to the first position obtained in the first steps S801 to S805. If the second position obtained in steps S801 to S805 has moved to a position in poorer health than (1) the first position, the lighting is controlled to increase or decrease the brightness. And (2) lighting can be controlled to maintain brightness when moving to a better health position than the first position.
  • the processing of each step shown in FIGS. 5A, 5B, 6, 7A, and 7B is performed by the processor unit 120.
  • the processor unit 130 or the program stored in the processor unit 140 and the memory unit 150
  • the present invention is not limited to this.
  • At least one of the processes of each step shown in FIGS. 5A, 5B, 6, 7A, and 7B may be realized by a hardware configuration such as a control circuit.
  • at least one of the steps shown in FIGS. 5A, 5B, 6, 7A, and 7B may be performed by a person using a computer system or a measuring instrument.
  • each step shown in FIG. 8 is realized by the program stored in the processor unit 350 and the memory unit 340 of the terminal device 300.
  • the present invention is not limited to this.
  • At least one of the processes of each step shown in FIG. 8 may be realized by a hardware configuration such as a control circuit.
  • Example 1 Creation of health positioning map
  • 232 items of initial data on health were acquired for 720 subjects.
  • the acquisition of this initial data was carried out at the Kobe RIKEN IIB Building.
  • This 232 item included both invasive and non-invasive tests.
  • the value of the acquired initial data was corrected so that the average value was 0 and the standard deviation was 1.
  • a predetermined value specifically, the correlation coefficient is 0.9
  • Some data was extracted using the method. Some of this data included data on the four basic parameters.
  • one data is extracted when there is data having a correlation coefficient higher than 0.9, but the present invention is not limited to this, and the value of the correlation coefficient is not limited to this. Can be changed as appropriate.
  • the correlation coefficient By setting the correlation coefficient to 0.9 or more, it is possible to create a health function that can calculate the health degree of the subject more comprehensively.
  • the 81 items were extracted. These 81 items correspond to the "first parameter set" in the present invention.
  • the 81 items included 4 basic parameters, basic parameters, cognitive function parameters, subjective parameters, blood parameters, blood vessel and skin parameters, and living condition parameters.
  • the multidimensional data (that is, 81-dimensional data) was lowered to 2 dimensions by the multi-dimensional scale method, and the data for 692 people who had complete data out of 720 people were plotted on the 2 dimensions. ..
  • the plot distribution pattern of 692 plots plotted in two dimensions was clustered by the k-means method and clustered into 10 clusters (FIG. 9B). Each cluster was characterized with health information and a health positioning map of the present invention was created.
  • FIG. 10 is a diagram showing a health degree positioning map created in this embodiment. Although FIG. 10 also displays an actual plot, the health positioning map of the present invention does not need to include the plot on which it is based, and the area specified by the clustering of the plot and the area thereof. Please note that it is only necessary to include health information associated with.
  • Example 2 Creation of another health positioning map
  • initial data of 242 items related to health were acquired for 1000 subjects.
  • This 242 item included both invasive and non-invasive tests.
  • machine learning using a trained model in which the degree of influence of 242 items of data on the health degree positioning map was learned items having a relatively high degree of influence on the health degree positioning map were extracted.
  • 69 items were extracted. These 69 items correspond to the "first parameter set" in the present invention.
  • the 69 items included 30 items of blood parameters and SOS. These 69 items were items that could be measured through the measurement of 7.
  • a health degree positioning map of the present invention was created by the same method as in Example 1.
  • Example 3 Creation of health function
  • a function (health function) that can be appropriately arranged in the health degree positioning map created in Example 1 was specified by a smaller number of test items (second parameter set) than the first parameter set.
  • FIG. 10 is a health degree positioning map in this embodiment.
  • the plot on the left side that is, on the -X-axis side
  • the plot on the + X-axis side was a plot of a relatively old subject.
  • the subject group included in the group G1 of FIG. 10 was a group of subjects who were young, had a high degree of depression and anxiety, had a low autonomic nervous system coordination ability, and had a high number of errors in the cognitive task. Therefore, when a subject whose data is newly measured belongs to group G1, it can be seen that the subject is a subject who is likely to have no mental health disease.
  • the subject group included in group G2 was a group of subjects who were older and had high blood ⁇ -GTP, blood ALT, blood triglyceride, blood HbA1c, and blood high-sensitivity CRP values. Therefore, when the subject whose data is newly measured belongs to the group G2, it can be seen that the subject is a subject who is likely to have no lifestyle-related disease.
  • the subject group included in the group G3 was a subject group having a high age and a high blood glucose level. Therefore, when a subject whose data is newly measured belongs to group G3, it can be seen that the subject is a subject who is likely to have no diabetes.
  • the region of the first function X ⁇ about 4 and the second function Y ⁇ about 2 indicates a healthy group having almost no problem in health condition.
  • the above groups G1 to G3 are some examples that can be evaluated by the health evaluation device of the present invention, and various other health risks can be evaluated.
  • Example 4 Observation of changes in health status using a health positioning map
  • Example 1 Using the health positioning map created in Example 1, it was examined whether changes in the health condition of the subjects could be observed. The mapping positions on the health positioning map were compared before and after taking reduced CoQ10 for 3 months. The results are shown in FIG.
  • a health positioning map was created from the data of 965 subjects using the first parameter set of 76 items.
  • the data of half of the subjects were used as teacher data, and the data of the other half of the subjects were used for evaluation of the obtained functions.
  • Health function by the second parameter set of 19 items by 4 measurements (body composition measurement, blood pressure measurement, autonomic nerve function measurement, questionnaire measurement) and 3 measurements (body composition measurement, autonomic nerve function measurement, questionnaire measurement)
  • the second parameter set of 19 items by 4 measurements was 2 body composition parameters, 1 blood pressure parameter, 5 autonomic nerve parameters, and 11 questionnaire parameters.
  • the second parameter set of 18 items by 3 measurements was 2 body composition parameters, 5 autonomic nerve parameters, and 11 questionnaire parameters.
  • these measurement items do not include bone density parameters and cognitive function parameters, and are at most 4 measuring devices, preferably at most 3 measuring devices, and more preferably 1 measuring device (eg, smartphone). ) Is a measurable item. That is, by using these measurement items, the measurement burden of the subject can be reduced, and easy measurement can be performed at any time at any place including a place other than the inspection facility such as home.
  • Example 6 Analysis using WH ratio
  • Example 5 Similar to 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 pattern of 965 plots plotted two-dimensionally was clustered by the k-means method and clustered into 10 clusters.
  • FIG. 14 (a) shows the data of 965 people plotted and clustered on the health positioning map.
  • the horizontal axis is associated with physical health, and the larger the value on the horizontal axis, the worse the physical health, while the vertical axis is mental health. It is associated, and the larger the value on the vertical axis, the worse the mental health.
  • the third cluster was evaluated as a mental health disease risk group
  • the tenth cluster was evaluated as a lifestyle-related disease risk group
  • the sixth and ninth clusters were lifestyle-related diseases. It was evaluated as a disease risk and mental health disease risk group.
  • the data of 965 subjects included WH ratio and BMI.
  • the WH ratio and BMI were calculated from the data acquired from Body Composition Analyzer (InBody Japan Co., Ltd.). Using these WH ratios and BMIs, we identified subjects with a BMI of 25 or more and a WH ratio of 1.0 or more (male) or 0.9 or more (female) from the data of 965 subjects. did. Obese people with a BMI of 25 or more and a WH ratio of 1.0 or more (male) or 0.9 or more (female) are called apple-type obesity (abdominal obesity) and are prone to various complications. It is known to be obese. FIG.
  • FIG. 14B emphasizes the data of subjects having a BMI of 25 or more and a WH ratio of 1.0 or more (male) or 0.9 or more (female) on the health positioning map. ing.
  • the data of the subjects having 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.
  • the data of subjects other than are represented by white circles.
  • the first parameter set for creating the health positioning map in this example contained BMI but did not include the WH ratio.
  • each of the subjects having a BMI of 25 or more and a WH ratio of 1.0 or more (male) or 0.9 or more (female) is shown in FIG. 14 (a).
  • the result of analysis which belongs to the cluster 1 to the tenth cluster is shown.
  • the percentage of subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (male) or 0.9 or higher (female) is shown in black, and the percentage of other subjects is shown in diagonal lines.
  • Subjects with a BMI of 25 or higher and a WH ratio of 1.0 or higher (male) or 0.9 or higher (female) are most often classified in the 9th cluster, with nearly 40% of the subjects.
  • BMI was 25 or more
  • WH ratio was 1.0 or more (male) or 0.9 or more (female).
  • the ninth cluster is a cluster evaluated as a lifestyle-related disease risk and a mental health disease risk group.
  • clusters 10 and 8 had a BMI of 25 or more, and a WH ratio of 1.0 or more (male) or 0.9 or more (female) belonged to many, followed by cluster 6.
  • the health degree positioning map prepared in this example is used. Is considered to be able to appropriately express the health level of the subject.
  • the health degree positioning map created in this example could be used for analysis using parameters (WH ratio) that were not used as the first parameter set for creating the health degree positioning map. ..
  • WH ratio parameters that were not used as the first parameter set for creating the health degree positioning map.
  • a cluster of subjects having poor physical and mental health has a BMI of 25 or more and a WH ratio of 1.0 or more (male) or 0.9. It was observed that the number of subjects who were above (female) tended to be large. It was a new finding and an unexpected finding that apple-type obesity (abdominal obesity) could be related not only to physical health but also to mental health. Based on this new finding, for example, from the appearance of body shape alone or from the results of body composition measurement alone, it is possible that subjects with apple-type obesity (abdominal obesity) have poor physical health as well as mental health. It can be made aware of something and encouraged to improve mental health.
  • Computer system 110 Interface part 120, 130, 140 Processor part 150 Memory part 200 Database part 300 User terminal device 400 Network

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Abstract

La présente invention concerne un procédé de mesure du degré de santé d'un sujet, ledit procédé comprenant : l'étape (1) consistant à obtenir un ensemble de données correspondant à un ensemble de paramètres ; l'étape (2) consistant à transmettre l'ensemble de données à un dispositif serveur ; l'étape (3) consistant à recevoir, en provenance du dispositif serveur, des informations relatives au degré de santé qui ont été déduites sur la base de l'ensemble de données ; et l'étape (4) consistant à représenter le degré de santé en tant que position sur une carte de positionnement de degré de santé des informations relatives au degré de santé.
PCT/JP2021/026167 2020-07-13 2021-07-12 Procédé, dispositif, programme et système de mesure du degré de santé d'un individu WO2022014538A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007058565A (ja) * 2005-08-24 2007-03-08 Tottori Univ 健康診断用の自己組織化マップ、その表示装置及び表示方法並びに健康診断用の自己組織化マップの表示プログラム
JP2008006216A (ja) * 2006-06-30 2008-01-17 Hamamatsu Photonics Kk トレーニングメニュー作成システムおよびトレーニングメニュー作成方法
JP2017023477A (ja) * 2015-07-23 2017-02-02 公立大学法人大阪市立大学 疲労度評価システム
WO2020091053A1 (fr) * 2018-11-02 2020-05-07 国立研究開発法人理化学研究所 Procédé, système et programme pour créer une carte de positionnement de santé et une fonction de santé, et leur procédé d'utilisation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007058565A (ja) * 2005-08-24 2007-03-08 Tottori Univ 健康診断用の自己組織化マップ、その表示装置及び表示方法並びに健康診断用の自己組織化マップの表示プログラム
JP2008006216A (ja) * 2006-06-30 2008-01-17 Hamamatsu Photonics Kk トレーニングメニュー作成システムおよびトレーニングメニュー作成方法
JP2017023477A (ja) * 2015-07-23 2017-02-02 公立大学法人大阪市立大学 疲労度評価システム
WO2020091053A1 (fr) * 2018-11-02 2020-05-07 国立研究開発法人理化学研究所 Procédé, système et programme pour créer une carte de positionnement de santé et une fonction de santé, et leur procédé d'utilisation

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