Health condition determination method and health condition determination system

Abstract

This health state determination method includes: a first acquisition step (S40) for acquiring reference characteristics for determining the health state of a subject, the reference characteristics indicating the relationship between a first index and a second index, each of which is an index relating to a person's health or comfort felt by a person; and a control step (S44 or S52) for presenting, to the subject, a menu for improving the health state, on the basis of data indicating the subject's current health state and health state determination criteria for a class to which the subject belongs, determined on the basis of the acquired reference characteristics.

Description

[Technical Field]

[0001] The present invention relates to a health condition determination method and a health condition determination system. [Background technology]

[0002] In recent years, interest in health has been increasing. As a technology for understanding a person's health condition, Patent Document 1 discloses a biological information measuring device that can quickly determine the condition of a person being measured using a simple method. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-52463 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention provides a health condition determination method and a health condition determination system that can support improvement of a subject's health condition. [Means for solving the problem]

[0005] A health condition determination method according to one embodiment of the present invention is a health condition determination method executed by a computer, and includes a first acquisition step of acquiring reference characteristics for determining the health condition of a subject, the reference characteristics indicating the relationship between a first index and a second index, each of which is an index related to a person's health or the comfort felt by a person, and a control step of presenting a menu to the subject for improving their health condition, or executing the menu, based on data indicating the subject's current health condition and the health condition determination criteria for the class to which the subject belongs, which are determined based on the acquired reference characteristics.

[0006] A health condition determination system according to one embodiment of the present invention comprises an acquisition unit that acquires reference characteristics for determining a subject's health condition, the reference characteristics indicating the relationship between a first index and a second index, each of which is an index related to a person's health or the comfort felt by a person, and a control unit that presents a menu to the subject for improving the health condition based on data indicating the subject's current health condition and the health condition determination criteria for the class to which the subject belongs, which is determined based on the acquired reference characteristics, or that executes the menu. [Effects of the Invention]

[0007] A health condition determination method and a health condition determination system according to one aspect of the present invention can assist in improving the health condition of a subject. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing the functional configuration of a health condition determination system according to an embodiment. [Figure 2] FIG. 2 is a diagram showing a first example of a master curve. [Figure 3] FIG. 3 is a flowchart of the operation of generating a master curve in the health condition determination system according to the embodiment. [Figure 4] FIG. 4 is a first diagram for explaining another example of the master curve x and the master curve y. [Figure 5] FIG. 5 is a second diagram for explaining another example of the master curve x and the master curve y. [Figure 6] FIG. 6 is a diagram showing a second example of a master curve. [Figure 7] FIG. 7 is a diagram showing a third example of a master curve. [Figure 8] FIG. 8 is a diagram showing a fourth example of the master curve. [Figure 9] FIG. 9 is a diagram showing an example of setting a plurality of classes. [Figure 10] FIG. 10 is a flowchart of the operation for determining which class a subject belongs to. [Figure 11] FIG. 11 is a flowchart of the operation for supporting improvement of the subject's health condition. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, the embodiments will be described in detail with reference to the drawings. Note that the embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection forms, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present invention. Furthermore, among the components in the following embodiments, components not recited in independent claims will be described as optional components.

[0010] It should be noted that the drawings are schematic diagrams and are not necessarily strict illustrations. In addition, in the drawings, substantially the same components are denoted by the same reference numerals, and overlapping descriptions may be omitted or simplified.

[0011] (Embodiment) [composition] First, the configuration of a health condition determination system according to an embodiment will be described below: Fig. 1 is a block diagram showing the functional configuration of a health condition determination system according to an embodiment.

[0012] Health condition determination system 10 is a system for determining the health condition of a subject. As shown in Fig. 1, health condition determination system 10 includes a server device 20, multiple sensors 31, a first information terminal 40, a control device 51, an environmental adjustment device 52, a sensor 53, a second information terminal 60, and a third information terminal 70. Fig. 1 also illustrates multiple facilities 30 and a facility 50. Although facility 30 and facility 50 are distinguished for ease of explanation, facility 50 may be included in the multiple facilities 30.

[0013] The server device 20 is a computer that performs information processing for determining the health condition of a subject. The server device 20 includes a communication unit 21, an information processing unit 22, and a storage unit 23.

[0014] The communication unit 21 is a communication circuit (communication module) that enables the server device 20 to communicate with the multiple sensors 31, the first information terminal 40, the control device 51, the second information terminal 60, and the third information terminal 70 via a wide area communication network 80 such as the Internet. The communication unit 21 is, for example, a wireless communication circuit that performs wireless communication, but may also be a wired communication circuit that performs wired communication. There are no particular limitations on the communication standard used for communication by the communication unit 21.

[0015] The information processing unit 22 performs information processing for determining the health condition of the subject. The information processing unit 22 is realized, for example, by a microcomputer, but may also be realized by a processor. The information processing unit 22 includes, as functional components, an acquisition unit 24, a calculation unit 25, a determination unit 26, a control unit 27, a verification unit 28, and an analysis unit 29. The functions of the acquisition unit 24, the calculation unit 25, the determination unit 26, the control unit 27, the verification unit 28, and the analysis unit 29 are realized, for example, by the microcomputer or processor constituting the information processing unit 22 executing a computer program stored in the storage unit 23.

[0016] The storage unit 23 is a storage device that stores computer programs and the like executed by the information processing unit 22. The storage unit 23 is realized by, for example, a semiconductor memory.

[0017] The sensor 31 is provided in each of the multiple facilities 30 and senses people located within the facility 30 in a non-contact manner. The sensor 31 is, for example, a non-contact vital sensor such as a radio wave sensor, but may also be a camera (image sensor). The sensor 31 may also be a contact-type vital sensor, such as a mat-shaped (sheet-shaped) sensor, that senses people located within the facility 30 by contacting them. The specific form of the sensor 31 is not particularly limited. As described below, when a parameter related to human comfort (e.g., a parameter indicating whether a person feels comfortable or uncomfortable on two or more levels) is used to generate a master curve, the sensor 31 may be a sensor that senses the environment rather than a person. Regarding the method of acquiring the comfort-related parameter, the comfort-related parameter may be acquired by manually inputting a subjective evaluation result, or a change in the settings of the environmental adjustment device 52 may be considered to indicate that the person feels uncomfortable. Note that at least one sensor 31 is required to be provided in each of the multiple facilities 30, and two or more sensors may be provided in one facility 30.

[0018] The first information terminal 40 is held by a person and measures the person's biometric data. The first information terminal 40 has, for example, a pulse wave sensor, a blood pressure sensor, a sweat sensor, a body temperature sensor, a respiration sensor, an activity level sensor, and an electroencephalogram sensor. That is, the first information terminal 40 measures the subject's biometric data, such as the subject's pulse wave, blood pressure, sweat rate, body temperature, respiration, activity level, and electroencephalogram. The first information terminal 40 is, for example, a wristband-type or watch-type information terminal worn on the subject's wrist, but may also be an earhook-type information terminal. Furthermore, the first information terminal 40 is not limited to such wearable information terminals, and may also be a portable information terminal, such as a smartphone or tablet terminal, that has the above-mentioned sensors.

[0019] The control device 51 is provided in the facility 50 where the subject is located, and controls the environmental adjustment device 52 based on the assessment result of the subject's health condition. As the control device 51, for example, an EMS (Energy Management System) controller having a function of managing the amount of power consumption in the facility 50 is used, but other controllers that do not have a function of managing power consumption may also be used.

[0020] The environmental adjustment device 52 is provided in the facility 50 where the subject is located, and controls the environment around the subject. Specifically, the environmental adjustment device 52 is a lighting device, an air conditioner, an air blower, a ventilation device, a fragrance emitting device, a speaker device, etc. In other words, the environmental adjustment device 52 adjusts (controls) the light, temperature, airflow, carbon dioxide concentration, fragrance, sound, etc. around the subject. If the facility 50 is a residence, the environmental adjustment device 52 is provided in an indoor space such as the living room, bathroom, bedroom, or toilet within the facility 50, and adjusts the environment in these indoor spaces.

[0021] The sensor 53 is provided in the facility 50 where the subject is located and senses the subject. The sensor 53 is, for example, a non-contact vital sensor such as a radio wave sensor, but may also be a camera (image sensor) or the like. The sensor 53 may also be a contact vital sensor, such as a mat-shaped (sheet-shaped) sensor, that senses by coming into contact with a person located in the facility 50, and the specific form of the sensor 53 is not particularly limited. As will be described later, in cases where parameters related to the comfort felt by people are used in generating a master curve, the sensor 53 may be a sensor that senses the environment rather than a person. Note that at least one sensor 53 needs to be provided in the facility 50, and two or more sensors may be provided in one facility 50.

[0022] The second information terminal 60 is held by the subject and measures the subject's biological data. The second information terminal 60 has, for example, a pulse wave sensor, a blood pressure sensor, a sweat sensor, a body temperature sensor, a respiration sensor, an activity level sensor, and an electroencephalogram sensor. That is, the second information terminal 60 measures the subject's biological data, such as the subject's pulse wave, blood pressure, sweat rate, body temperature, respiration, activity level, and electroencephalogram. The second information terminal 60 is, for example, a wristband-type or watch-type information terminal worn on the subject's wrist, but may also be an earhook-type information terminal. Furthermore, the second information terminal 60 is not limited to such wearable information terminals, and may also be a portable information terminal, such as a smartphone or tablet terminal, that has the above-mentioned sensors.

[0023] The third information terminal 70 is an information terminal that the subject uses as a user interface for the health condition determination system 10, and specifically, is an information terminal through which the subject receives notification of the health condition determination result from the server device 20. The third information terminal 70 is, for example, a portable information terminal such as a smartphone or a tablet terminal, but may also be a stationary information terminal such as a personal computer.

[0024] The third information terminal 70 is, for example, a general-purpose information terminal, and by installing a dedicated application program, it is possible to receive a notification (notification information described below) of the health condition assessment result from the server device 20. Note that, for convenience, the second information terminal 60 and the third information terminal 70 are distinguished as separate information terminals in this specification, but the second information terminal 60 and the third information terminal 70 do not need to be separate information terminals and may be a single information terminal.

[0025] [Master curve generation behavior] In order to determine the health condition of a subject, the health condition determination system 10 calculates a reference characteristic called a master curve in advance and stores the calculated master curve in the storage unit 23 of the server device 20. Figure 2 is a diagram showing an example of the master curve.

[0026] The master curve shown in Figure 2 is a curve that shows the relationship between activity level and meal size in a two-dimensional coordinate system (in other words, coordinate space) where the horizontal axis (also written as x-axis) indicates activity level and the vertical axis (also written as y-axis) indicates meal size. The operation of generating such a master curve will be described below. Figure 3 is a flowchart of the operation of generating a master curve.

[0027] First, the acquisition unit 24 of the server device 20 acquires an amount of activity for each of a plurality of people (S11). The amount of activity is an example of first data indicating a first index related to the subject's health or the comfort felt by the subject. The amount of activity can be rephrased as the amount of calories burned. For example, the acquisition unit 24 acquires the amount of activity measured by the first information terminal 40 and received by the communication unit 21 from the first information terminal 40. If the amount of activity can be sensed by the sensor 31, the acquisition unit 24 may acquire the amount of activity sensed by the sensor 31 (the amount of activity determined by the sensing result). In this case, the acquisition unit 24 acquires the amount of activity received by the communication unit 21 from the sensor 31. Note that the amount of activity here refers to the amount of activity over a predetermined period, such as a few hours or a day (24 hours).

[0028] Next, the acquisition unit 24 acquires the amount of food eaten by each of the plurality of people (S12). The amount of food eaten is an example of second data that indicates a second index related to the health of the subject or the comfort felt by the subject. The amount of food eaten can be rephrased as the amount of calories ingested.

[0029] For example, the acquisition unit 24 acquires the amount of food eaten measured by the first information terminal 40, which is received by the communication unit 21 from the first information terminal 40. There is known technology for estimating calorie intake by measuring the movement of bodily fluids in and out of body cells using a bioimpedance sensor provided in a wristwatch-type wearable terminal, and the first information terminal 40 measures (estimates) the amount of food eaten using such technology.

[0030] If the sensor 31 is a camera or the like installed in a dining area of ​​the facility 30 and is capable of sensing (estimating) the amount of food eaten, the acquisition unit 24 may acquire the amount of food eaten sensed by the sensor 31 (the amount of food eaten determined by the sensing results). In this case, the acquisition unit 24 acquires the amount of food eaten received by the communication unit 21 from the sensor 31. Note that the amount of food eaten here is, for example, the amount of food eaten per meal or per day.

[0031] In order to generate a master curve, it is necessary to link the activity amount and the amount of food eaten of the same person. For example, when both the activity amount and the amount of food eaten are acquired from the first information terminal 40, the acquisition unit 24 can link the activity amount and the amount of food eaten of the same person by acquiring the activity amount and the amount of food eaten simultaneously (during the same communication). When the first information terminal 40 transmits the activity amount and the amount of food eaten together with the identification information of the first information terminal 40, the acquisition unit 24 can link the activity amount and the amount of food eaten of the same person based on the identification information.

[0032] Furthermore, when both the activity amount and the amount of food eaten are acquired from the sensor 31, the acquisition unit 24 can associate the activity amount and the amount of food eaten of the same person by acquiring the activity amount and the amount of food eaten simultaneously (during the same communication). In this case, it is assumed that the sensor 31 is installed in a private room or the like, and thereby senses the activity amount and amount of food eaten of a specific individual. Furthermore, under this assumption, if the sensor 31 transmits the activity amount and amount of food eaten together with the identification information of the sensor 31 attached, the acquisition unit 24 can associate the activity amount and amount of food eaten of the same person based on the identification information.

[0033] When one of the amount of activity and the amount of food eaten is obtained from the first information terminal 40 and the other is obtained from the sensor 31, the amount of activity and the amount of food eaten can be linked by pre-storing in the memory unit 23 information indicating the correspondence between the identification information of the first information terminal 40 held by a person and the identification information of the sensor 31 that senses that person.

[0034] It is not essential that the activity amount and the amount of food eaten are acquired from the first information terminal 40 or the sensor 31. For example, another server device that manages information on pairs of activity amount and amount of food eaten may provide the information to the server device 20, and the acquisition unit 24 may acquire this information. Alternatively, the acquisition unit 24 may acquire information on pairs of activity amount and amount of food eaten that is manually input to the server device 20 via a user interface device (not shown).

[0035] After step S12, the calculation unit 25 calculates a master curve based on the acquired activity amounts and meal amounts for multiple people (S13). The master curve is an example of a reference characteristic for determining the health state of a subject, which is a relationship between a first index indicated by the master curve x and a second index indicated by the master curve y.

[0036] The calculation unit 25 plots points determined by the linked activity amounts and meal amounts on the two-dimensional coordinates as many times as the number of data points of the acquired activity amounts and meal amounts (i.e., the number of people), and calculates a master curve by applying an approximation formula to the plotted points. There are no particular restrictions on the type of approximation formula to be applied, and any of various existing approximation formulas may be applied.

[0037] Next, the calculation unit 25 sets an upper limit curve and a lower limit curve (shown in FIG. 3) for the master curve based on the master curve (S14). The calculation unit 25 sets, for example, the upper limit curve and the lower limit curve as criteria used to determine the health condition using the master curve. The calculation unit 25 sets the upper limit curve and the lower limit curve for the master curve based on, for example, the variability (standard deviation) of the data on which the master curve is based. The calculation unit 25 may be configured to set the upper limit curve and the lower limit curve for the master curve manually by the designer of the health condition determination system 10 via a user interface device (not shown). Depending on the type of master curve, only one of the upper limit curve and the lower limit curve may be set.

[0038] Furthermore, the calculation unit 25 stores the calculated master curve, the set upper limit curve, and the set lower limit curve in the storage unit 23 (S15).

[0039] In this way, the health condition determination system 10 can calculate a master curve based on the amounts of activity and food intake of multiple people and store the master curve in the storage unit 23.

[0040] In addition, if at least one of the amount of activity and the amount of food consumed is determined based on the sensing results of sensors 31 installed in facilities 30, sensing can be performed at multiple types of facilities 30 to calculate a master curve suitable for determining health status.

[0041] For example, if the multiple types of facilities 30 include hospitals, nursing homes, private homes, and training facilities, the activity levels and food intakes of hospitalized individuals, nursing home residents, private homes, and training facility users can be obtained. In other words, activity and food intake data for individuals in a range of health conditions, from those in poor health to those in good health, are available. More specifically, as shown in FIG. 3, activity and food intake data for individuals requiring care, individuals undergoing treatment for illness, individuals not yet ill, healthy individuals, individuals undergoing training (even healthier individuals), and athletes (even healthier individuals) are available. A master curve calculated based on the activity levels and food intakes of individuals in a wide variety of health conditions can be considered suitable for assessing health status.

[0042] The amount of activity is an example of a master curve x (first data), and the amount of food eaten is an example of a master curve y (second data). Another first index related to health or comfort may be used as the master curve x, and another second index related to health or comfort may be used as the master curve y. Two different indexes that have a certain degree of correlation may be used as the master curve x and the master curve y. FIG. 4 is a diagram illustrating another example of the master curve x and the master curve y.

[0043] 4, by using a radio wave sensor as the sensor 31, the acquisition unit 24 acquires a plurality of types of parameters that can be used as a master curve x from the sensing results of the sensor 31. Specifically, based on the RRI (RR Interval) that is the sensing result of the radio wave sensor, it is possible to acquire LF (Low Frequency) / HF (High Frequency), heart rate variability parameters, and heart rate.

[0044] LF / HF is a parameter indicating the ratio of the LF component (e.g., a component between 0.05 Hz and 0.15 Hz) to the HF component (e.g., a component between 0.15 Hz and 0.40 Hz) of the power spectrum obtained by frequency analysis of the RRI. When the acquisition unit 24 acquires the LF / HF as the master curve x, the acquisition unit 24 acquires, as the master curve y, a parameter related to comfort, a parameter related to the relaxation level, a parameter related to stress, a parameter related to bipolar disorder, a parameter related to the concentration level, a parameter related to sleep, a parameter related to frailty, a parameter related to mild cognitive impairment (MCI), a parameter related to vascular age, or a parameter related to the ovulation date. These parameters may be acquired based on the sensing results of the sensor 31, may be measured by the first information terminal 40, or may be acquired based on the results of a subjective evaluation (such as a questionnaire), the results of a predetermined test, or the like.

[0045] The heart rate variability parameters are time domain parameters determined based on the RRI. Specific examples of the heart rate variability parameters include SDNN (Standard Deviation of all RR intervals) and RMSSD (Root Mean Square of Successive Differences). When acquiring these heart rate variability parameters as the master curve x, the acquiring unit 24 acquires, as the master curve y, parameters related to high blood pressure, parameters related to hyperglycemia, or parameters related to high lipids. These parameters may be acquired based on the sensing results of the sensor 31, may be measured by the first information terminal 40, or may be acquired based on the results of a subjective evaluation (such as a questionnaire).

[0046] The heart rate is a parameter determined based on the RRI and indicating the number of times the heart beats per minute. When the acquisition unit 24 acquires the heart rate as the master curve x, it acquires a parameter related to the amount of activity or a parameter related to the quality of diet as the master curve y. These parameters may be acquired based on the sensing results of the sensor 31, may be measured by the first information terminal 40, or may be acquired based on the results of a subjective evaluation (such as a questionnaire).

[0047] FIG. 5 is a diagram illustrating yet another example of the master curve x and the master curve y. FIG. 5 shows two sets of parameters (either of which may be the master curve x(y)) that can be used as the master curve x and the master curve y. Such sets of parameters include a set of a parameter related to comfort and a parameter related to a level of relaxation. Such sets of parameters also include a set of a parameter related to sleep and a parameter related to one of activity level, dietary quality, stress, MCI, hyperglycemia (diabetes), and hormone-related cancers (prostate cancer, breast cancer). Such sets of parameters also include a set of a parameter related to either MCI or hyperglycemia and a parameter related to frailty.

[0048] Specific examples of master curves using parameters such as those shown in Figs. 4 and 5 include master curves such as those shown in Figs. 6 to 8. Fig. 6 is a diagram showing an example of a master curve showing the relationship between LF / HF and sleep quality (sleep depth). Fig. 7 is a diagram showing an example of a master curve showing the relationship between the daily sleep duration of people aged 50 or younger and the incidence of MCI when they reach 60 years of age or older, with the horizontal axis in Fig. 7 indicating shorter sleep duration toward the right. Fig. 8 is a diagram showing an example of a master curve showing the relationship between LF / HF and the incidence of bipolar disorder.

[0049] The parameters that can be used as the master curve x (first data) described above are just examples. The master curve x may be any parameter that indicates a first index related to a person's health (including beauty) or the comfort that a person feels, and more specifically, may be any parameter related to at least one of heart rate, pulse, movement, activity level, sleep, posture, skin, and facial expression. The master curve x may also be a parameter that indicates a physiological index of a person.

[0050] Similarly, the parameters that can be used as the master curve y (second data) described above are just examples. The master curve y may be any parameter that indicates a second index related to human health (including beauty) or the comfort felt by a person, and more specifically, may be any parameter related to at least one of comfort, physical condition, stress, beauty, weight loss, sleep, bodily function, body shape, aging, pre-illness, illness, treatment, and nursing.

[0051] As described above, health condition determination system 10 acquires first data and second data for multiple people, each of which indicates an index related to the person's health or the comfort felt by the person, calculates a master curve (reference characteristic) that is a reference characteristic for determining the subject's health condition and indicates the relationship between the first index indicated by the first data and the second index indicated by the second data based on the acquired first data and second data for the multiple people, and sets upper and lower limit curves, which are criteria for determining the subject's health condition, for the master curve. The calculated master curve, the set upper limit curve, and the set lower limit curve are stored in memory unit 23.

[0052] The reference characteristic does not necessarily have to be a curve, but may be a straight line. The same applies to the judgment criteria. The master curve may be calculated (generated) by a designer of the health condition judgment system 10 or the like, and stored (registered) in the memory unit 23.

[0053] [Determine the class to which the subject belongs] Next, an operation of dividing the master curve into a plurality of classes and determining which class a subject belongs to will be described. For example, the calculation unit 25 sets a plurality of classes for the master curve. FIG. 9 is a diagram showing an example of setting a plurality of classes. In the example of FIG. 9, the plurality of classes are set according to the amount of activity, and include 15 classes from class A to class O. The classes can also be referred to as segments. In the following explanation, a predetermined range (upper limit curve and lower limit curve) and a master curve with classes set therein are defined as reference characteristics.

[0054] 10 is a flowchart of the operation of determining which class a subject belongs to. The acquisition unit 24 of the server device 20 acquires the activity amount of the subject (S31). For example, the acquisition unit 24 acquires the activity amount measured by the second information terminal 60, which is received by the communication unit 21 from the second information terminal 60. If the activity amount can be sensed by the sensor 53, the acquisition unit 24 may acquire the activity amount sensed by the sensor 53 (the activity amount determined by the sensing result). In this case, the acquisition unit 24 acquires the activity amount received by the communication unit 21 from the sensor 53. The acquisition unit 24 may also acquire from the third information terminal 70 the activity amount manually input by the subject to the third information terminal 70.

[0055] Next, the determination unit 26 determines which of class A to class O the subject belongs to based on the activity amount of the subject acquired in step S31 (S32). The determination unit 26 may determine the subject's class by majority vote by making a determination multiple times based on the activity amount acquired in different periods.

[0056] [Health improvement support actions] The health condition determination system 10 can perform an operation to support improvement of the subject's health condition. Such an operation will be described below with reference to Fig. 11. Fig. 11 is a flowchart of the operation to support improvement of the subject's health condition.

[0057] In the following description of Fig. 11, it is assumed that the subject's class (segment) has been determined in advance by the class determination operation. For simplicity of explanation, it is assumed that only one predetermined range determined by an upper limit curve and a lower limit curve is set for each class. The predetermined range is, for example, a range in which the average value of the values ​​indicated by the upper limit curve is the upper limit, and the average value of the values ​​indicated by the lower limit curve is the lower limit.

[0058] Specifically, a first predetermined range determined by a first upper limit value and a first lower limit value is set for class A, a second predetermined range determined by a second upper limit value and a second lower limit value is set for class B, and so on. In other words, a predetermined range is a judgment criterion. The predetermined range set for each class is stored in advance in storage unit 23.

[0059] First, the acquisition unit 24 of the server device 20 acquires a master curve from the storage unit 23 (S40). Specifically, the acquisition unit 24 acquires the master curves shown in FIGS.

[0060] Next, the acquisition unit 24 acquires the amount of food eaten by the subject (S41). The amount of food eaten is an example of data indicating the health condition of the subject. For example, the acquisition unit 24 acquires the amount of food eaten measured by the second information terminal 60, which is received by the communication unit 21 from the second information terminal 60. If the sensor 53 is a camera or the like installed in a dining area of ​​the facility 50 and is capable of sensing (estimating) the amount of food eaten, the acquisition unit 24 may acquire the amount of food eaten sensed by the sensor 53 (the amount of food eaten determined by the sensing result). In this case, the acquisition unit 24 acquires the amount of food eaten received by the communication unit 21 from the sensor 53. The acquisition unit 24 may also acquire from the third information terminal 70 the amount of food eaten that has been manually input into the third information terminal 70 by the subject.

[0061] Next, the determination unit 26 determines whether the amount of food eaten by the subject acquired in step S41 is outside a predetermined range set for the class to which the subject belongs (S42). The process in step S42 corresponds to the process of determining the health condition of the subject. As described above, the predetermined range is stored in advance in the storage unit 23. Note that the class of the subject is stored in the storage unit 23, for example, in association with a user account (a user account linked to a dedicated application program described below) through which the subject receives services via the health condition determination system 10.

[0062] Note that "outside a predetermined range" in step S42 basically means outside the upper and lower limits of a certain range within a class. If classes are not used, pinpoint upper and lower limits may be used. However, if classes are not used, the process of reviewing the classes in step S48 is omitted.

[0063] First, the operation will be described when the determination unit 26 determines in step S42 that the acquired amount of food eaten by the subject is outside the predetermined range (Yes in S42). In this case, the control unit 27 notifies the subject of the health condition (in other words, issues an alert) (S43). Specifically, the control unit 27 generates notification information and causes the communication unit 21 to transmit the generated notification information to the third information terminal 70. Upon receiving the notification information, a notification screen for notifying the subject of a worsening health condition (inappropriate amount of food eaten) is displayed on the display unit (display) of the third information terminal 70. The process of the third information terminal 70 receiving the notification information and displaying the notification screen based on the received notification information is realized, for example, by pre-installing a dedicated application program in the third information terminal 70.

[0064] This notification screen (notification information) includes a menu for improving the activity of the subject. That is, the control unit 27 presents (recommends) a menu for improving the activity of the subject (S44) to improve the health condition (optimize the amount of food consumed).

[0065] A plurality of improvement menus are prepared in advance in the storage unit 23, and one is selected from the plurality of types based on, for example, the position of a point indicating the subject's meal amount in two-dimensional coordinates. The improvement menu may be selected from the plurality of types based on at least one piece of data indicating the subject's health condition at rest (not limited to the amount of meal, but at least one piece of data of the first index and the second index described above). The algorithm for selecting the improvement menu is determined, for example, by the designer of the health condition determination system 10, but may also be determined by a machine learning model or the like.

[0066] Thereafter, the determination unit 26 determines whether or not the activity improvement menu presented in step S44 has been executed (S45). For example, when the subject executes the activity improvement menu, the subject performs a predetermined input to the third information terminal 70, and the determination unit 26 determines whether or not the activity improvement menu has been executed based on the presence or absence of such a predetermined input. Furthermore, when the activity improvement menu is a menu related to the number of steps or the amount of sleep, the determination unit 26 can determine whether or not the activity improvement menu has been executed by acquiring a sensing result from the sensor 53 or the second information terminal 60.

[0067] When the determination unit 26 determines that the activity improvement menu has been executed (Yes in S45), it determines whether the amount of food eaten by the subject has returned to the predetermined range (S46). The processing in step S46 corresponds to the processing for determining the health condition of the subject. Specifically, the determination unit 26 performs the same processing as steps S41 and S42. When the determination unit 26 determines that the amount of food eaten by the subject has returned to the predetermined range (Yes in S46), the operation ends. On the other hand, when the determination unit 26 determines that the amount of food eaten by the subject has not returned to the predetermined range (No in S46), it determines whether the cumulative number of notifications in step S43 has exceeded an upper limit (S47). In other words, the cumulative number of notifications in step S43 is the number of times the amount of food eaten has been determined to be outside the predetermined range (inappropriate).

[0068] If the determination unit 26 determines that the cumulative number of notifications has not exceeded the upper limit (No in S47), a notification regarding the health condition in step S43 is made (S43). On the other hand, if the determination unit 26 determines that the cumulative number of notifications has exceeded the upper limit (Yes in S47), the control unit 27 reviews the class to which the subject belongs (S48). That is, if the subject's health condition is frequently deteriorating or if the subject's health condition continues to deteriorate, the control unit 27 suspects that the class determination is inappropriate and attempts to review the class. Specifically, the control unit 27 changes the class from one of classes A to O to another by performing the class determination operation of FIG. 10 again, but the algorithm for reviewing the class is not particularly limited.

[0069] After the processing of step S48, the control unit 27 notifies the verification unit 28 that the cumulative number of notifications to the subject has exceeded the upper limit, etc. (S49). When the notification that the cumulative number of notifications to the subject has exceeded the upper limit, etc. is made, the cumulative number of notifications is reset to 0. Note that even if it is determined in step S45 above that the subject has not implemented the activity improvement menu (No in S45), the control unit 27 notifies the verification unit 28 that the subject has not implemented the activity improvement menu, etc.

[0070] In this way, in health condition determination system 10, control unit 27 presents a menu for improving the activity of the subject when it is determined that the value of data indicating the subject's health condition (food amount in FIG. 9) is outside a predetermined range. Here, determination unit 26 determines whether the value of the data is outside the predetermined range multiple times, and control unit 27 corrects (reviews) the class to which the subject belongs based on the number of times it is determined that the value of the data is outside the predetermined range, and notifies verification unit 28. More specifically, control unit 27 notifies verification unit 28 based on the number of times it is determined that the value of the data is outside the predetermined range and that the value of the data does not return to the predetermined range even after executing the activity improvement menu.

[0071] Such a health condition determination system 10 can improve the health condition of the subject by presenting a menu of activity improvement options to the subject, and can also encourage the subject to review the classes to which the subject belongs.

[0072] Next, the operation when the determination unit 26 determines in step S42 that the acquired meal amount of the subject is within the predetermined range (No in S42) will be described. In this case, the determination unit 26 determines whether the fluctuation in meal amount is large (S50). For example, the determination unit 26 considers the difference between the maximum and minimum meal amounts over a predetermined period, such as one week, to be the magnitude of the fluctuation, and determines that the fluctuation is large if the difference exceeds a threshold, and determines that the fluctuation is small if the difference is equal to or less than the threshold. The determination unit 26 may also calculate the variation (standard deviation or variance) of the meal amount over the predetermined period, and determine that the variation is large if the calculated variation exceeds a threshold, and determine that the variation is small if the calculated variation is equal to or less than the threshold.

[0073] If the determination unit 26 determines that the acquired amount of food eaten by the subject is within the predetermined range but fluctuates greatly (Yes in S50), the control unit 27 notifies the subject about the health condition (in other words, issues an alert) (S51). Specifically, the control unit 27 generates notification information and causes the communication unit 21 to transmit the generated notification information to the third information terminal 70. Upon receiving the notification information, a notification screen is displayed on the display unit (display) of the third information terminal 70 to notify that the health condition is tending to deteriorate (the amount of food eaten fluctuates greatly). The process of the third information terminal 70 receiving the notification information and displaying the notification screen based on the received notification information is realized, for example, by pre-installing a dedicated application program in the third information terminal 70.

[0074] This notification screen (notification information) includes a menu for improving the environment around the subject. That is, the control unit 27 presents (recommends) a menu for improving the environment around the subject (S52) to improve the health condition (reduce fluctuations in food intake). A plurality of improvement menus are prepared in advance in the storage unit 23, and a menu is selected from the plurality of menus based on, for example, the amount of fluctuation in the amount of food the subject eats.

[0075] The improvement menu may be selected from a plurality of types based on at least one data item indicating the subject's health condition at rest (not limited to the amount of food eaten, but at least one data item of the first index and the second index described above). The selection algorithm for the improvement menu is determined, for example, by the designer of the health condition determination system 10, but may also be determined by a machine learning model or the like.

[0076] Thereafter, the determination unit 26 determines whether or not the environmental improvement menu presented in step S44 has been executed (S53). For example, when the subject executes the environmental improvement menu, the subject performs a predetermined input to the third information terminal 70, and the determination unit 26 determines whether or not the environmental improvement menu has been executed based on the presence or absence of such a predetermined input. The determination unit 26 can also determine whether or not the environmental improvement menu has been executed by inquiring of the control device 51 about the control history (operation history) of the environmental adjustment device 52.

[0077] When the determination unit 26 determines that the environmental improvement menu has been executed (Yes in S53), it determines whether or not the fluctuation in the amount of food eaten by the subject has been suppressed to below the threshold (S54). The processing in step S54 corresponds to the processing for determining the health condition of the subject. Specifically, the determination unit 26 performs the same processing as steps S41, S42, and S50. When the determination unit 26 determines that the fluctuation in the amount of food eaten by the subject has been suppressed to below the threshold (Yes in S54), the operation ends. On the other hand, when the determination unit 26 determines that the fluctuation in the amount of food eaten by the subject has not been suppressed to below the threshold (No in S54), it determines whether or not the cumulative number of notifications in step S51 has exceeded an upper limit (S55). In other words, the cumulative number of notifications in step S51 is the number of times it has been determined that the fluctuation in the amount of food eaten exceeds the threshold (large fluctuation).

[0078] If the determination unit 26 determines that the cumulative number of notifications has not exceeded the upper limit (No in S55), a notification regarding the health condition in step S51 is made (S51). On the other hand, if the determination unit 26 determines that the cumulative number of notifications has exceeded the upper limit (Yes in S55), the control unit 27 notifies the verification unit 28 that the cumulative number of notifications to the subject has exceeded the upper limit, etc. (S56). When the notification that the number of notifications to the subject has exceeded the upper limit is made, the cumulative number of notifications is reset to 0. Note that, if it is determined in the above step S53 that the environmental improvement menu has not been executed by the subject (No in S53), the control unit 27 also notifies the verification unit 28 that the environmental improvement menu has not been executed by the subject, etc.

[0079] In this way, in health condition determination system 10, when it is determined that the value of the data indicating the subject's health condition is within a predetermined range, a menu for improving the environment surrounding the subject is presented. Here, when it is determined that the data value is within the predetermined range, determination unit 26 further determines whether the data value is within the predetermined range. Determination unit 26 performs a determination regarding the data variation multiple times, and control unit 27 notifies verification unit 28 based on the number of times it is determined that the data value is within the predetermined range but has large variation. Specifically, control unit 27 notifies verification unit 28 based on the number of times it is determined that the data value is within the predetermined range but has large variation, and that the data variation has not been suppressed even when the environment improvement menu is executed.

[0080] Such a health condition determination system 10 can improve the health condition of a subject by presenting a menu of environmental improvement options to the subject.

[0081] In the above steps S52 and S53, the environmental improvement menu is executed by the subject. However, the environmental improvement menu may be executed automatically by the control unit 27.

[0082] For example, the control unit 27 generates control information for implementing an environment improvement menu determined according to the degree of variation in the amount of food eaten by the subject, and causes the communication unit 21 to transmit the generated control information to the control device 51. The control device 51 controls the environment adjustment device 52 based on the received control information. In other words, the control unit 27 (control device 51) can execute a menu for improving the environment around the subject.

[0083] [Improvement menu learning] According to steps S44 to S46 of the improvement support operation in Fig. 11, health condition determination system 10 can acquire, as information, a change in the amount of food eaten by the subject as a result of the subject carrying out an activity improvement menu. Here, if acquisition unit 24 acquires various data other than the amount of food eaten before and after carrying out the activity improvement menu (data that can be acquired through sensor 53 and second information terminal 60), acquisition unit 24 can acquire (store in storage unit 23) information indicating the degree of influence of the activity improvement menu on the various data.

[0084] Such impact information (information indicating the relationship between Input / Output) indicating the impact of the activity improvement menu on various data can be used as learning data for the machine learning model. Specifically, by having the machine learning model learn the impact information, control unit 27 can select and present an activity improvement menu using the machine learning model when selecting an activity improvement menu in step S44.

[0085] This machine learning model may be built as a machine learning model customized for a single subject using the influence information of that subject, or may be built as a machine learning model for subjects belonging to the same class using the influence information of multiple subjects belonging to the same class. By building machine learning models at least for each class, it is believed that it will be possible to propose an effective menu of activity improvement options.

[0086] Furthermore, according to steps S52 to S54, health condition determination system 10 can acquire, as information, changes in fluctuations in food intake due to the execution of an environmental improvement menu. Here, if acquisition unit 24 acquires various data other than food intake before and after the execution of an environmental improvement menu (biometric data that can be acquired through sensor 53 and second information terminal 60), control unit 27 can acquire information indicating the degree of influence of the environmental improvement menu on the various data (store the changes in the various data in storage unit 23).

[0087] Such impact information (information indicating the relationship between Input / Output) indicating the impact of the environmental improvement menu on various data can be used as learning data for the machine learning model. Specifically, by having the machine learning model learn the impact information, the control unit 27 can select and present an environmental improvement menu using the machine learning model when selecting an environmental improvement menu in step S52.

[0088] This machine learning model may be constructed as a machine learning model customized for a single subject using the influence information of that subject, or may be constructed as a machine learning model for subjects belonging to the same class using the influence information of multiple subjects belonging to the same class. By constructing machine learning models at least for each class, it is believed that it will be possible to propose effective environmental improvement menus.

[0089] [Master Curve Verification] In step S49 of the improvement support operation in Fig. 11, a notification is sent from the control unit 27 to the verification unit 28. Here, the notification from the control unit 27 to the verification unit 28 is sent when the subject's health condition is frequently deteriorating and when the subject's health condition remains deteriorating, and since there is a possibility that the master curve itself is not appropriate, it is also considered that verification of the master curve is necessary.

[0090] Therefore, the verification unit 28 may verify the master curve upon receiving a notification from the control unit 27. The verification unit 28 corrects the master curve, for example, by performing at least one of a first correction process for enlarging or reducing the master curve in the vertical axis direction and a second correction process for enlarging or reducing the master curve in the horizontal axis direction.

[0091] The method of correction may be instructed by an administrator of the health condition determination system 10 via a user interface (not shown), or the correction may be repeated by a trial-and-error method. After the correction is repeated several times by the trial-and-error method, it is also possible to automatically determine the correction parameters (amount of enlargement or reduction) by a machine learning model constructed using the information obtained during the trial-and-error method as learning data.

[0092] Furthermore, the verification unit 28 may correct the way in which the classes are set in the master curve. For example, the verification unit 28 corrects the number of classes to be set and the boundary values ​​of the classes. Alternatively, the verification unit 28 may perform correction so as to reduce the total number of classes by integrating the current classes (a total of 15 classes, from class A to class O, in the example of FIG. 9) into groups of a predetermined number (for example, three).

[0093] The method of correction may be instructed by an administrator of the health condition determination system 10 via a user interface (not shown), or the correction may be repeated by a trial-and-error method. After the correction is repeated several times by the trial-and-error method, it is also possible to automatically determine the correction parameters by a machine learning model constructed using the information obtained during the trial-and-error process as learning data.

[0094] Furthermore, the verification unit 28 may correct a predetermined range (at least one of the upper limit curve and the lower limit curve) set in the master curve.

[0095] For example, the verification unit 28 performs a correction to widen the specified range (relax the judgment criteria so that the health condition is less likely to be considered to have deteriorated) so as to reduce the number of cases in which the judgment is Yes in step S42 and the number of cases in which the judgment is No in step S46.

[0096] The method of correction may be instructed by an administrator of the health condition determination system 10 via a user interface (not shown), or the predetermined range may be widened stepwise by a trial-and-error method. After the correction is repeated several times by the trial-and-error method, it is also possible to automatically determine the correction parameters (correction amount) by a machine learning model constructed using the information obtained during the trial-and-error as learning data.

[0097] The verification unit 28 may correct at least one of the master curve, the classification method set in the master curve, and the predetermined range based on the notification received by the verification unit 28. Note that the correction here is intended to be a dedicated customization for the individual subject, and after the correction, the learning content explained in the learning of the improvement menu is reset or is carried over by linking the information or the like.

[0098] Here, the verification unit 28 may perform correction in the sense of customization for a class, rather than in the sense of customization for an individual subject. For example, when the number of notifications regarding multiple subjects belonging to the same class reaches a predetermined number (for example, when multiple subjects belonging to class A are frequently determined to be in poor health), the verification unit 28 may correct at least one of the master curve for the class, the classification method, and the predetermined range.

[0099] The above describes an example in which the master curve is verified based on the notification in step S49 of the improvement support operation in Fig. 11 , but in addition to or instead of the notification in step S49, the master curve may be verified based on the notification in step S56. The notification in step S56 is performed when there is a large fluctuation within a predetermined range of data values, such as the amount of food eaten. In this case, for example, a correction may be made to narrow the predetermined range (to tighten the criteria so that the health condition is more likely to be deemed to have deteriorated) so that the fluctuation in the data value is determined to be outside the predetermined range.

[0100] Although the verification unit 28 has been described as a component (function) included in the server device 20, it may be realized as a component included in a device (e.g., another server device) different from the server device 20 that is included in the health condition determination system 10, or may be realized as a component included in a system other than the health condition determination system 10. The verification unit 28 is an example of a verification system.

[0101] [Extraction of biometric data to be monitored] Incidentally, as explained in the section on learning the improvement menu above, when learning the improvement menu, it is desirable that various biological data be acquired through the sensor 53 and the second information terminal 60. However, since the second information terminal 60 (for example, a wearable information terminal) has limitations in CPU performance, memory capacity, and the like, it is better to narrow down the biological data monitored (acquired) through the second information terminal 60.

[0102] Therefore, the analysis unit 29 may extract biometric data that is likely to fluctuate due to changes in activity or environment based on influence information (information indicating the above-mentioned Input / Output relationship) of multiple subjects belonging to the same class, and may cause the second information terminal 60 to acquire (monitor) only the extracted biometric data. The analysis unit 29 extracts biometric data by, for example, extracting highly weighted parameters or hidden layers in a neural network. The analysis unit 29 may extract biometric data using sensitivity analysis or covariance structure analysis instead of a neural network.

[0103] Furthermore, the analysis unit 29 may compare the impact information of multiple subjects belonging to a first class with the impact information of multiple subjects belonging to a second class different from the first class to extract biometric data that should be monitored with priority for the subjects belonging to the first class. In this comparison, important factors are identified. In this case, the analysis unit 29 may consider the important factors by, for example, generating a critical path and modeling composite factors.

[0104] Although the analysis unit 29 has been described as a component (function) provided by the server device 20, it may be realized as a component provided by a device different from the server device 20 that is provided by the health condition determination system 10 (for example, another server device), or as a component provided by a system other than the health condition determination system 10.

[0105] [Variations] The storage unit 23 may store a plurality of types of master curves, each of which is different from the other, such as the master curve x (first data) and the master curve y (second data). The plurality of types of master curves may be calculated individually, for example, or may be calculated using the sensing result of one sensor 31. Specifically, as described above with reference to FIG. 4, when a plurality of types of first data can be acquired based on the sensing result of the sensor 31, the calculation unit 25 can use this to calculate a plurality of types of master curves corresponding to the plurality of types of first data.

[0106] In this way, when multiple types of master curves are stored in the memory unit 23, the determination unit 26 can determine the subject's health condition using each of the multiple types of master curves. At this time, the determination of the subject's health condition is performed at a predetermined time interval (unit of hour / day / week / month / year, etc.) suitable for determination using the master curve depending on the type of master curve.

[0107] Furthermore, the health condition determination system 10 may store (accumulate) the subject's first data, the subject's second data, and the subject's health condition assessment results in association with each other in the storage unit 23. Once data on multiple subjects is accumulated, this data can be used as training data for constructing a machine learning model for assessing health conditions. Thus, the present invention may be realized as a method for generating training data for constructing a machine learning model. Furthermore, the health condition determination system 10 may be realized as a system that uses the machine learning model constructed in this manner to assess the subject's health condition, notify the assessment results, or control the environment based on the assessment results. As described above, the master curve can also be optimized by correction or machine learning. Furthermore, the master curve itself can be compared and analyzed based on the subject's attributes, such as external environment factors such as residential area or race.

[0108] [Effects, etc.] As described above, a health condition determination method executed by a computer such as health condition determination system 10 includes a first acquisition step of acquiring reference characteristics for determining the health condition of a subject, the reference characteristics indicating the relationship between a first index and a second index, each of which is an index related to a person's health or the comfort felt by a person, and a control step of presenting to the subject a menu for improving the health condition or executing the menu based on data indicating the subject's current health condition and the health condition determination criteria for the class to which the subject belongs, which are determined based on the acquired reference characteristics. The reference characteristics correspond to the master curve in the above embodiment, and the determination criteria correspond to the upper limit curve and lower limit curve in the above embodiment.

[0109] Such a health condition assessment method can help improve the health condition of a subject.

[0110] Furthermore, for example, the health condition determination method further includes a second acquisition step of acquiring changes in the subject's biometric data due to the execution of the menu, and an information processing step of performing at least one of a process of storing the acquired changes in the biometric data and a process of learning the degree of impact of the execution of the menu on the subject's biometric data based on the acquired changes in the biometric data.

[0111] Such a health condition determination method can collect and accumulate information for improving health conditions.

[0112] Furthermore, for example, the health condition assessment method further includes a determination step of determining whether the data value is outside a predetermined range indicated by the assessment criterion, and in the control step, if it is determined that the data value is outside the predetermined range indicated by the assessment criterion, a menu of options for improving the subject's activity is presented as a menu, and if it is determined that the data value is within the predetermined range, a menu of options for improving the subject's surrounding environment is presented as a menu, or the option of improving the environment is executed.

[0113] Such a health condition determination method can switch the type of improvement menu depending on a first case in which the subject's health condition is poor, and a second case in which the subject's health condition is good (or tends to be slightly poor).

[0114] Also, for example, in the control step, a menu is selected based on data indicating the subject's health condition at rest.

[0115] This health condition assessment method allows for menu selection based on data indicating the subject's health condition at rest. Note that data indicating the health condition at rest is superior to data indicating the health condition at rest in that it is less susceptible to environmental fluctuations and makes it easier to capture changes in physical condition over time.

[0116] Furthermore, for example, the health condition determination method further includes a changing step of changing the class to which the subject belongs based on the number of times the data value is determined to be outside the predetermined range.

[0117] Such a health condition determination method can optimize the class to which the subject belongs.

[0118] Furthermore, for example, the health condition determination method further includes a first notification step of notifying a verification system that verifies the reference characteristics based on the number of times the data value is determined to be outside the predetermined range. The verification system corresponds to the verification unit 28 in the above embodiment.

[0119] Such a health condition determination method can optimize the reference characteristics when, for example, the data value is likely to be determined to be outside a predetermined range.

[0120] Also, for example, in the first notification step, a notification is sent to the verification system based on the number of times it is determined that the data value is outside the specified range and that the data value does not return to the specified range even after executing an activity improvement menu.

[0121] Such a health condition determination method can optimize the reference characteristics when, for example, the data value is likely to be determined to be outside a predetermined range.

[0122] In addition, for example, in the determination step, it is determined whether the data value is outside a predetermined range indicated by the determination criterion, and if it is determined that the data value is within the predetermined range, a determination regarding fluctuations in the data is further performed. The health condition determination method further includes a second notification step of notifying a verification system that verifies the reference characteristics based on the number of times the data value is determined to be within the predetermined range but has large fluctuations.

[0123] Such a health condition determination method can provide notification when the data value is within a predetermined range but has large fluctuations.

[0124] Also, for example, in the second notification step, a notification is sent to the verification system based on the number of times it is determined that the data value is within a predetermined range but has large fluctuations, and that the data fluctuations have not been suppressed even when the environmental improvement menu is executed.

[0125] Such a health condition determination method can notify when the data value is within a predetermined range but fluctuates significantly and no improvement is observed.

[0126] Also, for example, the health condition determination method further includes a correction step in which the verification system corrects at least one of the reference characteristics, the classification method set for the reference characteristics, and the specified range based on the notification received by the verification system.

[0127] This type of health condition assessment method can optimize the reference characteristics by correcting at least one of the reference characteristics, the classification method set for the reference characteristics, and the specified range, in cases where the data value is likely to be determined to be outside the specified range.

[0128] Also, for example, in the information processing step, a menu that is effective in improving the health condition of the subject is learned.

[0129] Such a health condition assessment method makes it possible to learn a menu that is effective in improving the health condition of a subject.

[0130] Furthermore, for example, the health condition determination method further includes an analysis step of extracting biometric data that should be monitored with priority for subjects belonging to the first class by comparing changes in first biometric data of subjects belonging to the first class when a menu is executed with changes in second biometric data of subjects belonging to a second class different from the first class when the menu is executed.

[0131] Such a health condition determination method can extract biological data that should be monitored intensively for subjects belonging to the first class.

[0132] Furthermore, for example, in the analysis step, important factors are identified in the comparison, and biological data that should be monitored intensively for subjects belonging to the first class is extracted.

[0133] By identifying important factors, this health condition assessment method can extract biometric data that should be monitored intensively by subjects belonging to the first class. Since important factors are expected to differ depending on the class, the health condition assessment method makes it possible to propose more detailed analysis or improvements. On the other hand, important factors may occur that span multiple classes. In such cases, covariance analysis or correlation analysis can be used as a so-called cross-sectional approach.

[0134] The health condition determination system 10 also includes an acquisition unit 24 that acquires reference characteristics for determining the health condition of the subject, which are reference characteristics that indicate the relationship between a first index and a second index, each of which is an index related to a person's health or the comfort felt by a person, and a control unit 27 that presents to the subject a menu for improving the health condition or executes the menu based on data indicating the subject's current health condition and the health condition determination criteria for the class to which the subject belongs, which are determined based on the acquired reference characteristics.

[0135] Such a health condition determination system 10 can assist in improving the health condition of a subject.

[0136] (Other embodiments) Although the embodiments have been described above, the present invention is not limited to the above-described embodiments.

[0137] For example, in the above embodiment, the health condition determination system is realized by multiple devices, but it may also be realized as a single device. For example, the health condition determination system may be realized as a single device corresponding to a server device. When the health condition determination system is realized by multiple devices, the components (especially functional components) of the health condition determination system may be allocated in any way among the multiple devices.

[0138] For example, in the above embodiment, a process executed by a specific processing unit may be executed by another processing unit. Also, the order of multiple processes may be changed, or multiple processes may be executed in parallel.

[0139] In the above-described embodiments, each component may be realized by executing a software program suitable for that component, or by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0140] Furthermore, each component may be realized by hardware. Each component may be a circuit (or integrated circuit). These circuits may form a single circuit as a whole, or each may be a separate circuit. Furthermore, each of these circuits may be a general-purpose circuit or a dedicated circuit.

[0141] Furthermore, the general or specific aspects of the present invention may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0142] For example, the present invention may be realized as a health condition determination method, as a program for causing a computer to execute the health condition determination method (in other words, a computer program product), or as a computer-readable non-transitory recording medium on which such a program is recorded.

[0143] In addition, the present invention also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art would think of, or forms realized by arbitrarily combining the components and functions of each embodiment within the scope of the present invention. [Explanation of symbols]

[0144] 10 Health Status Assessment System 20 Server device 21 Communications Department 22 Information Processing Department 23 Memory section 24 Acquisition Department 25 Calculation section 26 Judgment section 27 Control Unit 28 Verification Department 29 Analysis Department 30, 50 facilities 31, 53 Sensor 40 First Information Terminal 51 Control device 52 Environmental control device 60 Second information terminal 70 Third Information Terminal 80 Wide Area Communication Network

Claims

1. A computer-implemented health condition determination method, comprising: a first acquisition step of acquiring a reference characteristic for determining the health state of a subject, the reference characteristic indicating a relationship between a first index and a second index, each of which is an index related to a person's health or a comfort felt by a person; a control step of presenting a menu for improving the health condition of the subject to the subject or executing the menu based on data indicating the current health condition of the subject and a health condition judgment criterion for the class to which the subject belongs, which is determined based on the acquired reference characteristics; a second acquisition step of acquiring a change in the biometric data of the subject due to the execution of the menu; and an information processing step of performing at least one of a process of storing the acquired change in the biometric data and a process of learning the degree of influence of the execution of the menu on the biometric data of the subject based on the acquired change in the biometric data. Health status determination method.

2. A computer-implemented health condition determination method, comprising: a first acquisition step of acquiring a reference characteristic for determining the health state of a subject, the reference characteristic indicating a relationship between a first index and a second index, each of which is an index related to a person's health or a comfort felt by a person; a control step of presenting a menu for improving the health condition of the subject to the subject or executing the menu based on data indicating the current health condition of the subject and a health condition judgment criterion for the class to which the subject belongs, which is determined based on the acquired reference characteristics; a determining step of determining whether the value of the data is outside a predetermined range indicated by the determination criterion; In the control step, presenting a menu for improving the activity of the subject as the menu when it is determined that the value of the data is outside the predetermined range indicated by the determination criterion; When it is determined that the value of the data is within the predetermined range, a menu for improving the environment around the subject is presented as the menu, or the menu for improving the environment is executed. Health status determination method.

3. A computer-implemented health condition determination method, comprising: a first acquisition step of acquiring a reference characteristic for determining the health state of a subject, the reference characteristic indicating a relationship between a first index and a second index, each of which is an index related to a person's health or a comfort felt by a person; and a control step of presenting to the subject a menu for improving the health state, or executing the menu, based on data indicating the subject's current health state and a health state judgment criterion for the class to which the subject belongs, which is determined based on the acquired reference characteristics; In the control step, the menu is selected based on data indicating the health condition of the subject at rest. Health status determination method.

4. Further, the method includes a change step of changing the class to which the subject belongs based on the number of times the value of the data is determined to be outside the predetermined range. The health condition determination method according to claim 2.

5. Further, the method includes a first notification step of notifying a verification system that verifies the reference characteristics based on the number of times the value of the data is determined to be outside the predetermined range. The health condition determination method according to claim 2.

6. In the first notification step, a notification is given to the verification system based on the number of times that it is determined that the value of the data is outside the predetermined range and that the value of the data does not return to the predetermined range even after the activity improvement menu is executed. The health condition determination method according to claim 5.

7. In the determining step, determining whether the value of the data is outside a predetermined range indicated by the criteria; If it is determined that the value of the data is within the predetermined range, a further determination is made regarding fluctuations in the data; The health condition determination method further includes a second notification step of notifying a verification system that verifies the reference characteristics based on the number of times the data value is determined to be within the predetermined range but has large fluctuations. The health condition determination method according to claim 2.

8. In the second notification step, a notification is given to the verification system based on the number of times that it is determined that the data value is within the predetermined range but has a large fluctuation, and that the fluctuation of the data has not been suppressed even after the environment improvement menu is executed. The health condition determination method according to claim 7.

9. The method further includes a correction step in which the verification system corrects at least one of the reference characteristics, the classification method set for the reference characteristics, and the predetermined range based on the notification received by the verification system. The health condition determination method according to claim 5.

10. In the information processing step, the menu that is effective in improving the health condition of the subject is learned. The health condition determination method according to claim 1 .

11. The method further includes an analysis step of extracting biometric data of the subject belonging to the first class that should be monitored intensively by comparing a change in first biometric data of the subject belonging to a first class when the menu is executed with a change in second biometric data of the subject belonging to a second class different from the first class when the menu is executed. The health condition determination method according to claim 10.

12. In the analysis step, important factors are identified in the comparison, and biological data that should be monitored intensively for the subjects belonging to the first class is extracted. The health condition determination method according to claim 11.

13. A program for causing a computer to execute the health condition determination method according to any one of claims 1 to 12.

14. an acquisition unit that acquires a reference characteristic for determining a subject's health state, the reference characteristic indicating a relationship between a first index and a second index, each of which is an index related to a person's health or a person's comfort; a control unit that presents a menu to the subject for improving the health state based on data indicating the subject's current health state and a health state judgment criterion for the class to which the subject belongs, which is determined based on the acquired reference characteristics, or that executes the menu; the acquisition unit acquires a change in the biometric data of the subject resulting from the execution of the menu; The control unit performs at least one of a process of storing the acquired change in the biometric data and a process of learning the degree of influence of the execution of the menu on the biometric data of the subject based on the acquired change in the biometric data. Health status determination system.

15. an acquisition unit that acquires a reference characteristic for determining a subject's health state, the reference characteristic indicating a relationship between a first index and a second index, each of which is an index related to a person's health or a person's comfort; a control unit that presents a menu to the subject for improving the health condition based on data indicating the subject's current health condition and a health condition assessment criterion for the class to which the subject belongs, which is determined based on the acquired reference characteristics, or that executes the menu; a determination unit that determines whether the value of the data is outside a predetermined range indicated by the determination criterion, The control unit presenting a menu for improving the activity of the subject as the menu when it is determined that the value of the data is outside the predetermined range indicated by the determination criterion; When it is determined that the value of the data is within the predetermined range, a menu for improving the environment around the subject is presented as the menu, or the menu for improving the environment is executed. Health status determination system.

16. an acquisition unit that acquires a reference characteristic for determining a subject's health state, the reference characteristic indicating a relationship between a first index and a second index, each of which is an index related to a person's health or a person's comfort; a control unit that presents a menu to the subject for improving the health state based on data indicating the subject's current health state and a health state judgment criterion for the class to which the subject belongs, which is determined based on the acquired reference characteristics, or that executes the menu; The control unit selects the menu based on data indicating the subject's health condition at rest. Health status determination system.

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