Health risk estimation system

Abstract

To provide a health risk estimation system which allows a health risk of a subject to be suitably estimated.SOLUTION: A health risk estimation system is equipped with: an action estimation section capable of estimating (step S20) an action of a subject in an area of a movement destination; a temperature blood pressure data acquisition section for acquiring (steps S15, S21, S22) temperature blood pressure data indicating a relationship of a temperature difference that is a difference between a second temperature of an area of a movement destination and a first temperature of an area before moving, and a blood pressure difference that is a difference between a second blood pressure value when a subject returns from an area of a movement destination to an area before moving, and a first blood pressure value when a subject was present in an area before moving, for each action estimated by the action estimation section; and a health risk estimation section capable of estimating (step S23) a health risk of a subject, on the basis of a result of comparing first temperature blood pressure data that is temperature blood pressure data in a first period, with second temperature blood pressure data that is data of a past period older than the first period, and that is temperature blood pressure data of an action similar to an action of the first temperature blood pressure data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a technology for a health risk estimation system. [Background technology]

[0002] Conventionally, technology for a system for estimating a subject's health risk (health risk) has been publicly known, as described in Patent Document 1, for example.

[0003] The cited document 1 discloses a technology for estimating a subject's blood pressure fluctuation value based on the difference between room temperature and a reference temperature, and estimating the subject's health risk based on the blood pressure fluctuation value. The invention described in the above-mentioned patent document 1 makes it possible to indicate the health risk associated with changes in the indoor temperature environment.

[0004] However, blood pressure fluctuates under various influences. The invention described in Patent Document 1 does not take into account factors that affect blood pressure fluctuations other than the indoor temperature environment, so it is expected that it will be difficult to appropriately estimate the health risk of a subject. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2019-67183 Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention has been made in consideration of the above-mentioned circumstances, and the problem it aims to solve is to provide a health risk estimation system that can suitably estimate the health risk of a subject. [Means for solving the problem]

[0007] The problem to be solved by the present invention is as described above, and the means for solving this problem will now be described.

[0008] That is, claim 1 provides a health risk estimation system for estimating a health risk of a subject in a building having a plurality of areas, the health risk estimation system including a movement detection unit that detects movement of the subject between areas, a behavior estimation unit that can estimate the behavior of the subject in the destination area, a temperature detection unit that detects a first temperature that is the temperature of the area before movement and a second temperature that is the temperature of the area to which the subject has moved, and a temperature detection unit that detects a first blood pressure value that is the blood pressure value of the subject when the subject was in the area before movement and a second blood pressure value that is the blood pressure value of the subject when the subject returned from the destination area to the area before movement. a temperature and blood pressure data acquisition unit that acquires, for each of the behaviors estimated by the behavior estimation unit, temperature and blood pressure data that indicate the relationship between a temperature difference, which is the difference between the second temperature and the first temperature, and a blood pressure difference, which is the difference between the second blood pressure value and the first blood pressure value; and a health risk estimation unit that can estimate the health risk of the subject based on a comparison result between first temperature and blood pressure data, which is the temperature and blood pressure data for a first period, and second temperature and blood pressure data, which is the temperature and blood pressure data for a behavior that is earlier than the first period and similar to the behavior of the first temperature and blood pressure data.

[0009] In claim 2, the temperature and blood pressure data acquisition unit acquires the temperature and blood pressure data by plotting the temperature difference and the blood pressure difference on a graph and calculating a regression line based on each plotted point, and the health risk estimation unit is capable of estimating the health risk of the subject based on the comparison result between the regression line of the first temperature and blood pressure data and the regression line of the second temperature and blood pressure data.

[0010] In claim 3, the health risk estimation unit is capable of estimating the health risk of the subject based on the comparison result between the slope of the regression line of the first temperature blood pressure data and the slope of the regression line of the second temperature blood pressure data.

[0011] In claim 4, the health risk estimation unit is capable of estimating the health risk of the subject based on the comparison result between the intercept of the regression line of the first temperature and blood pressure data and the intercept of the regression line of the second temperature and blood pressure data.

[0012] In claim 5, the behavior estimation unit estimates the behavior of the subject person based on the duration of stay in the destination area.

[0013] In claim 6, the device is provided with a movement detection unit capable of detecting the movement of the subject, the movement detection unit detects the movement of the subject between areas based on the detection result of the movement detection unit, and the blood pressure detection unit and the movement detection unit are a common device. [Effects of the Invention]

[0014] The present invention has the following effects.

[0015] In the present invention, the health risk of a subject can be suitably estimated. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a schematic diagram showing the overall configuration of a health risk estimation system according to one embodiment of the present invention and a room to which the health risk estimation system is applied. [Figure 2] A table showing the relationship between areas in the room and subject behavior. [Figure 3] 10 is a flowchart showing the contents of a health risk estimation process. [Figure 4] (a) A schematic diagram showing an example of blood pressure data etc. when moving between areas, (b) A schematic diagram showing another example of blood pressure data etc. when moving between areas, and (c) A schematic diagram showing another example of blood pressure data etc. when moving between areas. [Figure 5] 10 is a graph showing the relationship between temperature difference and blood pressure difference. [Figure 6](a) Graph showing the relationship between temperature difference and blood pressure difference when the behavior was "excretion (small)". (b) Graph showing the relationship between temperature difference and blood pressure difference when the behavior was "excretion (large)". [Figure 7] (a) Graph showing the relationship between temperature difference and blood pressure difference when the behavior was "Other (short stay)." (b) Graph showing the relationship between temperature difference and blood pressure difference when the behavior was "Grooming." DETAILED DESCRIPTION OF THE INVENTION

[0017] A health risk estimation system 100 according to one embodiment of the present invention will be described below with reference to FIG.

[0018] The health risk estimation system 100 is capable of estimating the health risks (health risks) of subjects in a building having multiple areas. In this embodiment, "health risks" refers to health risks that can be estimated from the health of blood vessels, such as the risk of developing a vascular disease. First, an example of a subject assumed in this embodiment and a room 1 for the subject will be described.

[0019] In this embodiment, elderly people are assumed to be the people (hereinafter referred to as "subjects") whose health risks are estimated by the health risk estimation system 100. In this embodiment, it is also assumed that the health risk estimation system 100 is applied to a room 1 in a facility for the elderly where the subject lives.

[0020] FIG. 1 shows an example of a subject's room 1. Room 1 is equipped with a bed 10, a dining table 20, dining chairs 30, a toilet 40, a bathtub 50, a kitchen 60, a sofa 70, and an entrance / exit 80. The entrance / exit 80 is the entrance to room 1. The space in which the toilet 40 and the bathtub 50 are located is separated from other spaces by a partition wall 41. A sink (not shown) is also located in the space in which the bathtub 50 is located. The partition wall 41 is provided with an entrance / exit for accessing the toilet 40 and the bathtub 50. Although the illustration does not show a wall separating the space in which the toilet 40 is located from the space in which the bathtub 50 is located, a wall separating the above-mentioned spaces may be provided. The configuration of room 1 is not limited to the one described above, and various spaces such as a study may be arranged.

[0021] As shown in Figure 1, a room 1 is divided into multiple areas depending on the arrangement of furniture, etc. Figure 1 shows an example in which a bed area A, a dining area B, a toilet area C, a bathroom area D, a kitchen area E, a sofa area F, a miscellaneous area G, and an outdoor area H are set up.

[0022] The bed area A is an area (bedroom) where the bed 10 is placed. The bed area A is set to be slightly larger than the bed 10 in a plan view, for example. Similarly, a dining area B is set in the area where the dining table 20 and dining chairs 30 are placed. A kitchen area E is set in the area where the kitchen 60 is placed. A sofa area F is set in the area where the sofa 70 is placed. The toilet area C and bathroom area D (washroom) are set within the area surrounded by the partition wall 41, where the toilet 40 and bathtub 50 are placed. Areas of the room 1 other than the bed area A, dining area B, toilet area C, bathroom area D, kitchen area E, and sofa area F are set as other areas G. The area outside the entrance 80 is set as outdoor area H. Note that the above areas are merely examples, and in addition to the above areas A to H, any other area (e.g., a study) can be set depending on the furniture and other factors placed in the room 1.

[0023] Next, we will explain the configuration of the health risk estimation system 100. The health risk estimation system 100 mainly comprises a subject detection sensor 110, a temperature sensor 120, a staff terminal 130, and a server 140.

[0024] The subject detection sensor 110 is for detecting the position, posture, and blood pressure of a subject. The subject detection sensor 110 is a non-contact sensor. In this embodiment, a millimeter wave sensor is used as the subject detection sensor 110. The subject detection sensor 110 irradiates a subject with millimeter waves (radio waves with a wavelength of 1 to 10 mm and a frequency of 30 to 300 GHz) and observes the reflected radio waves, thereby measuring the position, speed, angle, movement, etc. of the subject.

[0025] In this embodiment, the subject detection sensor 110 is installed in a position (for example, on the ceiling or wall) in the room 1 where it can irradiate radio waves to the entire area of ​​the room 1. Note that although this embodiment shows an example in which one subject detection sensor 110 detects the entire area of ​​the room 1, multiple subject detection sensors 110 may be used as necessary.

[0026] The temperature sensor 120 acquires the room temperature of the room 1. The temperature sensor 120 is placed in each of the areas A to H of the room 1. The temperature sensor 120 is placed at an appropriate position where it can measure the room temperature of each of the areas A to H. Note that the temperature sensors 120 placed in the areas B, E, G, and H are not shown in FIG. 1.

[0027] The staff terminal 130 is used to notify the estimation results, etc., obtained by the health risk estimation system 100. For example, a personal computer can be used as the staff terminal 130. The staff terminal 130 is installed in a location where it can be seen by people who provide assistance (caregiving, nursing care, etc.) with the subject's daily life. In this embodiment, it can be placed, for example, in a staff station at an elderly care facility. By displaying various information on the monitor of the staff terminal 130, the information can be notified to the staff. It is to be noted that various other devices (for example, a portable terminal, etc.) can also be used as the staff terminal 130.

[0028] The server 140 is used to perform processing for estimating health risks using the health risk estimation system 100. The server 140 is configured, for example, as a cloud server. The server 140 can exchange information with the subject detection sensor 110, the temperature sensor 120, and the staff terminal 130.

[0029] The server 140 can determine the position and posture of the subject based on the data obtained by the subject detection sensor 110. Specifically, the server 140 can determine that data with movement among the point cloud data obtained based on reflected radio waves is a person (in this embodiment, the subject). In particular, since a millimeter wave sensor is used in this embodiment, even the slightest movement of the subject can be detected with high accuracy. The server 140 can extract only the data with movement and determine that the position of the data in the room 1 is the position of the subject (each area described below). Furthermore, the server 140 can determine the posture of the subject, such as whether the subject is standing (standing position), sitting (sitting position), or lying down (supine position), by extracting only the data with movement and checking its distribution.

[0030] The server 140 can also measure the subject's blood pressure based on data obtained by the subject detection sensor 110. Specifically, the server 140 acquires data on the person's heart rate waveform based on data indicating the subject's physical movements (e.g., heart movement, pulse rate, etc.) obtained by reflected radio waves. The server 140 also detects features correlated with blood pressure from the heart rate waveform data and calculates an estimated value (measured value) of blood pressure based on the detection results. Values ​​such as systolic blood pressure, diastolic blood pressure, and mean blood pressure can be used as the estimated value of blood pressure. Hereinafter, the estimated value of blood pressure calculated as described above will be referred to as "blood pressure data."

[0031] Server 140 pre-stores location information for each of the areas into which room 1 is divided (bed area A, dining area B, toilet area C, bathroom area D, kitchen area E, sofa area F, other area G, and outdoor area H).

[0032] The server 140 can estimate the behavior of the subject based on information on the area where the subject is located (location information), etc. More specifically, the server 140 estimates the behavior based on information such as the subject's posture data, time (the time when the subject is located in a predetermined area), and time spent in the area, in addition to the subject's location information.

[0033] 2 is a table showing the relationship between the area where the subject is located and the subject's behavior. In server 140, several types of behavior (e.g., "sleeping") shown in the table are set according to behaviors expected from information such as the subject's position information and posture data (e.g., lying down in bed area A). When server 140 detects any of the above behaviors, it infers that a behavior corresponding to the behavior has been performed. An example of a method by which server 140 infers the subject's behavior using the table will be described below.

[0034] When the subject is located in bed area A (bedroom), server 140 estimates that the subject's behavior is either "sleeping" or "asleep." For example, when server 140 detects the subject in bed area A at a bedtime in a preset schedule and the subject is lying down, server 140 estimates that the subject is about to go to sleep (the subject's behavior is "sleeping"). Furthermore, when server 140 detects that the subject is lying down for a predetermined period or longer after "sleeping," server 140 estimates that the subject is "asleep."

[0035] Furthermore, when the subject is located in toilet area C, server 140 estimates that the subject's behavior is either "excretion (small)" or "excretion (large)." More specifically, when the subject detection sensor 110 becomes unable to detect the subject at the entrance / exit of toilet area C, server 140 determines that the subject is located in toilet area C (entered toilet 40), and when detection of the subject resumes thereafter, server 140 determines that the subject has left toilet area C. For example, when the subject's stay time in toilet 40 (the period from entering to leaving toilet area C) is less than a preset period, server 140 estimates that the subject's behavior is "excretion (small)," and when the subject's stay time in toilet 40 is equal to or longer than the above period, server 140 estimates that the subject's behavior is "excretion (large)."

[0036] Furthermore, when the subject is located in a study (not shown), the server 140 estimates that the subject's behavior is either "work" or "hobby." For example, when the subject's stay time in the study (time spent in the study) is less than a preset period, the server 140 estimates that the subject's behavior is "hobby," and when the subject's stay time in the study is equal to or greater than the above period, the server 140 estimates that the subject's behavior is "work."

[0037] Furthermore, when the subject is located in kitchen area E, server 140 estimates that the subject's activity is either "cooking" or "washing dishes." For example, when server 140 detects that the subject is located in kitchen area E before a preset mealtime, server 140 estimates that the subject's activity is "cooking," and when server 140 detects that the subject is located in kitchen area E after a mealtime, server 140 estimates that the subject's activity is "washing dishes."

[0038] Furthermore, when the subject is located in bathroom area D, server 140 estimates that the subject's behavior is either "bathing" or "grooming." More specifically, when the subject detection sensor 110 becomes unable to detect the subject at the entrance / exit of bathroom area D, server 140 determines that the subject is located in bathroom area D (entering the bathroom (washroom)), and when detection of the subject resumes thereafter, server 140 determines that the subject has left bathroom area D. For example, when the subject's stay time in the bathroom (washroom) (the period from entering bathroom area D to leaving it) is less than a preset period, server 140 estimates that the subject's behavior is "grooming," and when the subject's stay time in the bathroom is equal to or longer than the preset period, server 140 estimates that the subject's behavior is "bathing."

[0039] Furthermore, when the subject is located in dining area B, server 140 estimates that the subject's behavior is one of "eating," "resting," and "watching television." For example, when server 140 detects that the subject is located in dining area B at a preset mealtime, server 140 estimates that the subject's behavior is "eating." For example, when server 140 detects that the subject is located in dining area B at a preset period, server 140 estimates that the subject's behavior is "resting," and when server 140 detects that the subject's stay in dining area B is shorter than a preset period, server 140 estimates that the subject's behavior is "watching television."

[0040] Furthermore, even when the subject is located in sofa area F, server 140 can estimate that the subject's behavior is one of "eating," "resting," and "watching television." For example, when server 140 detects that the subject is located in sofa area F at a preset mealtime, server 140 estimates that the subject's behavior is "eating." Furthermore, for example, when the subject's stay time in sofa area F is less than a preset period, server 140 estimates that the subject's behavior is "resting," and when the subject's stay time in sofa area F is equal to or greater than the above period, server 140 estimates that the subject's behavior is "watching television."

[0041] Furthermore, when the subject is located in other area G, server 140 estimates that the subject's behavior is either a "short stay" or a "long stay." For example, when the subject's stay time in other area G is less than a preset period, server 140 estimates that the subject's behavior is a "short stay," and when the subject's stay time in other area G is equal to or longer than the above period, server 140 estimates that the subject's behavior is a "long stay."

[0042] Furthermore, when the target person is located in the outdoor area H, the server 140 estimates that the target person's behavior is "going out." More specifically, when the server 140 no longer detects the target person at the entrance / exit 80, the server 140 estimates that the target person is located in the outdoor area H (the target person's behavior is "going out").

[0043] The above-described method for estimating the behavior of the subject is merely an example, and the method for estimating the behavior of the subject is not limited to the above-described example. Specifically, the behavior of the subject may be estimated using any of information on the time spent in an area, the current time, and a calendar (schedule). In addition to the above-described example, the server 140 can estimate the behavior of the subject using various methods that use data obtained by the subject detection sensor 110.

[0044] Below, an outline of the process of estimating the health risk of a subject using the health risk estimation system 100 configured as described above will be described.

[0045] The health risk estimation system 100 executes the process ("health risk estimation process") shown in Fig. 3. The health risk estimation process is executed, for example, constantly.

[0046] In step S11, the server 140 starts measuring the blood pressure of the subject. The server 140 can continuously acquire the blood pressure data of the subject based on the data obtained by the subject detection sensor 110.

[0047] After performing the process of step S11, the server 140 proceeds to step S12.

[0048] In step S12, server 140 acquires the temperature (room temperature) of the area where the subject is located. Specifically, server 140 acquires the area where the subject is located based on data obtained by subject detection sensor 110. Then, server 140 acquires the temperature of the area where the subject is located (hereinafter referred to as the "first temperature") based on measurement data of temperature sensor 120 in the area where the subject is located (hereinafter referred to as the "first area").

[0049] The first area may be limited to an area where the subject often stays, and for example, the first area may be set to sofa area F. In this case, when server 140 detects that the subject is located in sofa area F, it acquires the temperature of sofa area F (first temperature) based on the measurement data of temperature sensor 120 in sofa area F.

[0050] After performing the process of step S12, the server 140 proceeds to step S13.

[0051] In step S13, the server 140 determines whether the target person has moved to another area. Specifically, the server 140 determines whether the target person has moved from the first area to another area other than the first area, based on the data obtained by the target person detection sensor 110.

[0052] If the server 140 determines that the subject has moved to another area ("YES" in step S13), the process proceeds to step S14. On the other hand, if the server 140 determines that the subject has not moved to another area ("NO" in step S13), the process of step S13 is performed again. Note that if the server 140 determines that the subject has moved to another area ("YES" in step S13), the process may return to step S12 and reacquire (update) the first temperature.

[0053] In step S14, server 140 acquires the temperature of the destination area. Specifically, server 140 acquires the temperature of the second area (hereinafter referred to as the "second temperature") based on the measurement data of temperature sensor 120 in the destination area (hereinafter referred to as the "second area").

[0054] After performing the process of step S14, the server 140 proceeds to step S15.

[0055] In step S15, server 140 calculates a temperature difference. Here, the "temperature difference" is the difference between the second temperature (the temperature of the second area to which the target person has moved) acquired in step S14 and the first temperature (the temperature of the first area where the target person was located before moving) acquired in step S12. In this embodiment, the temperature difference is calculated by subtracting the first temperature from the second temperature.

[0056] As shown in FIG. 4(a), if the temperature (first temperature) of the sofa area F, which is the first area, is 24°C and the temperature (second temperature) of the bathroom area D, which is the second area, is 10°C, the temperature difference is "-14°C." As shown in FIG. 4(b), if the temperature (first temperature) of the sofa area F, which is the first area, is 24°C and the temperature (second temperature) of the other area G, which is the second area, is 20°C, the temperature difference is "-4°C." As shown in FIG. 4(c), if the temperature (first temperature) of the sofa area F, which is the first area, is 18°C ​​and the temperature (second temperature) of the bathroom area D, which is the second area, is 20°C, the temperature difference is "+2°C." In this way, when a subject moves from an area with a higher temperature to an area with a lower temperature, the temperature difference is a negative value. On the other hand, when a subject moves from an area with a lower temperature to an area with a higher temperature, the temperature difference is a positive value.

[0057] After performing the process of step S15, the server 140 proceeds to step S16.

[0058] In step S16, the server 140 starts measuring the stay time of the subject in the second area (destination area).

[0059] After performing the process of step S16, the server 140 proceeds to step S17.

[0060] In step S17, the server 140 determines, based on the data obtained by the target person detection sensor 110, whether or not the target person has returned from the second area to the first area.

[0061] If the server 140 determines that the subject has returned from the second area to the first area ("YES" in step S17), the process proceeds to step S19. On the other hand, if the server 140 determines that the subject has not returned from the second area to the first area ("NO" in step S17), the process proceeds to step S18.

[0062] In step S18, server 140 determines whether a predetermined time has elapsed since the measurement of the stay time started (step S16). Here, the "predetermined time" is set, for example, to the upper limit value for which the subject is considered to stay in the second area. If server 140 determines that the predetermined time has elapsed since the measurement of the stay time started ("YES" in step S18), it terminates the health risk estimation process of FIG. 3. On the other hand, if server 140 determines that the predetermined time has not yet elapsed since the measurement of the stay time started ("NO" in step S18), it returns the process to step S17.

[0063] If a predetermined time has passed since the start of measurement of the stay time without the subject returning to the first area (original area) (NO in step S17) (YES in step S18), this means that the subject has either gone out or there is a high possibility of a measurement error. In such cases, it is difficult to estimate the subject's behavior in step S20, which will be described later, and the health risk estimation process in FIG. 3 is terminated.

[0064] In step S19, server 140 ends the measurement of the staying time. In this way, server 140 measures the time from when it starts measuring the staying time in step S16 until it is detected by target person detection sensor 110 that the target person has moved to the second area as the staying time in the second area.

[0065] After performing the process of step S19, the server 140 proceeds to step S20.

[0066] In step S20, the server 140 estimates the subject's behavior. Specifically, the server 140 estimates the subject's behavior based on information about the area (second area) where the subject was located (step S13) and the duration of stay in the second area (step S19). For example, if the subject's destination (second area) is toilet area C, if the subject's duration of stay in the toilet 40 (the period from entering toilet area C to leaving it) is less than a preset period, the server 140 estimates the subject's behavior as "excretion (small)," and if the subject's duration of stay in the toilet 40 is equal to or greater than the preset period, the server 140 estimates the subject's behavior as "excretion (large)." As described above, the server 140 may estimate the subject's behavior using not only the duration of stay in the area but also the subject's posture, the current time, and calendar (schedule) information.

[0067] After performing the process of step S20, the server 140 proceeds to step S21.

[0068] In step S21, the server 140 calculates a blood pressure difference. Here, the "blood pressure difference" is the difference (fluctuation difference) between the subject's blood pressure data when the subject was in the first area (more specifically, immediately before moving from the first area to the second area) (hereinafter referred to as "first blood pressure data") and the subject's blood pressure data when the subject returned from the second area to the first area (more specifically, immediately after returning from the second area to the first area) (hereinafter referred to as "second blood pressure data"). In this embodiment, the blood pressure difference is calculated by subtracting the first blood pressure data from the second blood pressure data. Note that the blood pressure data (first blood pressure data and second blood pressure data) may be either the systolic blood pressure or the diastolic blood pressure, or the average value of the systolic blood pressure and the diastolic blood pressure may be used. In this embodiment, the systolic blood pressure is used as the blood pressure data.

[0069] As shown in FIG. 4(a), if the subject's systolic blood pressure is 115 when the subject is in the first area, sofa area F (more specifically, immediately before moving from sofa area F to the second area, bathroom area D), and is 135 when the subject returns from the second area, bathroom area D, to the first area, sofa area F (more specifically, immediately after returning from bathroom area D to sofa area F), the blood pressure difference is "+20°C." As shown in FIG. 4(b), if the subject's systolic blood pressure is 115 when the subject is in the first area, sofa area F, and is 120 when the subject returns from the second area, bathroom area D, to the first area, sofa area F, the blood pressure difference is "+5°C." As shown in Figure 4(c), if the subject's systolic blood pressure is 125 when the subject is in the first area, sofa area F, and 135 when the subject returns to the first area, sofa area F, from the second area, bathroom area D, the blood pressure difference is "+10°C." In this way, if the subject's blood pressure increases when they move from the first area to the second area, the blood pressure difference will be a positive value. On the other hand, if the subject's blood pressure decreases when they move from the first area to the second area, the blood pressure difference will be a negative value.

[0070] In this way, by repeatedly executing the processes up to step S21, data on temperature difference (step S15), estimated behavior (step S20), and blood pressure difference (step S21) are accumulated in the server 140. More specifically, the server 140 accumulates data on temperature difference and blood pressure difference for each estimated behavior (by behavior).

[0071] After performing the process of step S21, the server 140 proceeds to step S22.

[0072] In step S22, the server 140 plots the temperature difference and the blood pressure difference on a graph for each estimated behavior (by behavior). By repeatedly executing the process up to step S22, a scatter diagram is created in which the temperature difference and the blood pressure difference are plotted.

[0073] FIG. 5 is an example of a graph (scatter plot) showing the relationship between temperature difference and blood pressure difference. In the graph shown in FIG. 5, the horizontal axis represents temperature difference, and the vertical axis represents blood pressure difference. In the graph shown in FIG. 5, the upper left portion represents blood pressure fluctuations mainly in winter, and the lower right portion represents blood pressure fluctuations mainly in summer. As shown in the upper left and lower right portions of the graph shown in FIG. 5, generally, as the temperature in the area where the subject is located decreases (the temperature difference becomes negative), the subject's blood pressure increases (the blood pressure difference becomes positive). However, as shown in the upper right and lower left portions (shaded areas) of the graph shown in FIG. 5, in cases where the risk of dehydration, severe hypothermia, etc. is high, the subject's blood pressure may increase (the blood pressure difference becomes positive) as the temperature in the area where the subject is located increases (the temperature difference becomes positive). Therefore, when the temperature difference and blood pressure difference show a trend as shown in the shaded areas, the server 140 determines that the subject's health risk is high without having to compare them with past data (step S23, described below).

[0074] In this way, the server 140 acquires data indicating the relationship between the temperature difference and the blood pressure difference (hereinafter referred to as "temperature-blood pressure data") for each estimated behavior.

[0075] After performing the process of step S22, the server 140 proceeds to step S23.

[0076] In step S23, the server 140 compares the most recent temperature and blood pressure data obtained when the estimated behavior (step S20) is the same with past temperature and blood pressure data, and estimates the subject's health risk based on the comparison results. Here, "the most recent temperature and blood pressure data" refers to temperature and blood pressure data (data showing the relationship between temperature and blood pressure differences) from a most recent first period (e.g., the period from the most recent data acquisition date to one week before that). Also, "past temperature and blood pressure data" refers to temperature and blood pressure data (data showing the relationship between temperature and blood pressure differences) from a second period earlier than the first period (e.g., the period from one year before the most recent data acquisition date to one week before that).

[0077] In step S23, the server 140 calculates (creates) a regression line showing the distribution trend of each point based on each point plotted on the graph. More specifically, the server 140 creates the regression line based on the point cloud for the most recent first period. The server 140 also creates the regression line based on the point cloud for a second period that precedes the first period. The regression line is calculated using the least squares method or the like. The server 140 then compares the regression line for the first period (the most recent temperature and blood pressure data) with the regression line for the second period (the past temperature and blood pressure data), and estimates the subject's health risk based on the comparison result.

[0078] 6 and 7 are graphs showing the relationship between temperature difference and blood pressure difference for each behavior. As shown in FIGS. 6(a) and 6(b), if the intercept of the regression line of the most recent temperature and blood pressure data is larger than the intercept of the regression line of past temperature and blood pressure data by at least a first threshold, the subject's baseline blood pressure is considered to be increasing. In this case, the server 140 determines that the subject's health risk (especially the risk of hypertension) is increasing. The first threshold can be set to any value based on the actual relationship between the intercept of the regression line and the health risk.

[0079] Furthermore, as shown in Figure 7(a), if the slope of the regression line of the most recent temperature-blood pressure data is greater than the slope of the regression line of the past temperature-blood pressure data by at least the second threshold, the subject's blood pressure fluctuations are considered to be sensitive to temperature changes. In this case, the server 140 determines that the subject's health risk (particularly vascular health risk) is high. The second threshold can be set to any value based on the actual relationship between the slope of the regression line and health risk, etc.

[0080] Furthermore, as shown in Figure 7(b), if the intercept of the regression line of the most recent temperature and blood pressure data is not greater than the intercept of the regression line of the past temperature and blood pressure data by more than a first threshold, and the slope of the regression line of the most recent temperature and blood pressure data is not greater than the slope of the regression line of the past temperature and blood pressure data by more than a second threshold, the server 140 determines that the subject's health risk has not increased.

[0081] In this way, the health risk of the subject can be evaluated. The server 140 can display the health risk evaluation result on the staff terminal 130.

[0082] After performing the process of step S23, the server 140 ends the health risk estimation process shown in FIG.

[0083] As described above, health risk estimation system 100 according to this embodiment measures the temperature in each area of ​​room 1 using temperature sensor 120, and constantly measures the subject's blood pressure data using subject detection sensor 110. Health risk estimation system 100 then detects the subject's movement between areas, collects data on the temperature difference between each area (temperature differential) and the blood pressure difference when the subject was in each area (blood pressure differential), and compares the relationship between the most recent temperature differential and blood pressure differential (blood pressure fluctuation in response to temperature change) with the relationship between the past temperature differential and blood pressure differential (blood pressure fluctuation in response to temperature change), thereby estimating the health of the blood vessels.

[0084] Furthermore, blood pressure fluctuates due to factors other than temperature changes (such as physical movement during travel and behavior at the destination), but the health risk estimation system 100 according to this embodiment compares the relationship between blood pressure fluctuations in response to the most recent temperature change and blood pressure fluctuations in response to past temperature changes under the same condition of "same behavior." This makes it possible to detect the influence of "blood pressure fluctuations" due to "temperature changes" more precisely than before. This allows for an appropriate estimation of the subject's health risk.

[0085] As described above, the health risk estimation system 100 according to this embodiment: A health risk estimation system 100 for estimating a health risk of a subject in a building having a plurality of areas, A movement detection unit (subject detection sensor 110) detects movement of the subject between areas (step S13), A behavior estimation unit (server 140) capable of estimating (step S20) the behavior of the subject in a destination area (second area); a temperature detection unit that detects a first temperature, which is the temperature of the area before movement (first area), and a second temperature, which is the temperature of the area to which the object is moved (second area) (steps S12 and S14); a blood pressure detection unit (subject detection sensor 110) that detects a first blood pressure value, which is the blood pressure value of the subject when the subject was in the area before movement (first area), and a second blood pressure value, which is the blood pressure value of the subject when the subject returns from the area to which the subject moved (second area) to the area before movement (first area); a temperature and blood pressure data acquisition unit that acquires, for each of the behaviors inferred by the behavior inferrer, temperature and blood pressure data indicating the relationship between a temperature difference, which is the difference between the second temperature and the first temperature, and a blood pressure difference, which is the difference between the second blood pressure value and the first blood pressure value (steps S15, S21, S22); a health risk estimation unit (subject detection sensor 110) capable of estimating the health risk of the subject based on a comparison result between first temperature and blood pressure data, which is the temperature and blood pressure data in a first period, and second temperature and blood pressure data, which is the temperature and blood pressure data from an earlier period than the first period and is of a behavior similar to the behavior of the first temperature and blood pressure data (step S23); It is equipped with the following.

[0086] This configuration allows for a suitable estimation of health risks. That is, by comparing the relationship between the current and past temperature differences and blood pressure differences when similar behaviors are performed, the health of blood vessels can be suitably estimated.

[0087] The temperature and blood pressure data acquisition unit further comprises: The temperature difference and the blood pressure difference are plotted on a graph, and a regression line is calculated based on each plotted point, thereby obtaining the temperature and blood pressure data. The health risk estimation unit The health risk of the subject can be estimated based on the comparison result between the regression line of the first temperature and blood pressure data and the regression line of the second temperature and blood pressure data.

[0088] By configuring in this way, health risks can be more appropriately estimated.

[0089] Moreover, the health risk estimation unit The health risk of the subject can be estimated based on the comparison result between the slope of the regression line of the first temperature blood pressure data and the slope of the regression line of the second temperature blood pressure data.

[0090] By configuring in this way, health risks can be more appropriately estimated.

[0091] Moreover, the health risk estimation unit The health risk of the subject can be estimated based on the comparison result between the intercept of the regression line of the first temperature and blood pressure data and the intercept of the regression line of the second temperature and blood pressure data.

[0092] By configuring in this way, health risks can be more appropriately estimated.

[0093] Furthermore, the behavior estimation unit The behavior of the subject is estimated based on the time spent in the destination area.

[0094] By configuring in this way, it is possible to suitably estimate the behavior of the subject.

[0095] Furthermore, the health risk estimation system 100 according to this embodiment includes: A motion detection unit (subject detection sensor 110) capable of detecting the motion of the subject is provided. The movement detection unit Detecting movement of the subject between areas based on the detection result of the motion detection unit; The blood pressure detection unit and the movement detection unit are a common device (subject detection sensor 110).

[0096] By configuring in this manner, both the subject's movements and blood pressure can be detected using the subject detection sensor 110, which is a common device, thereby simplifying the configuration of the health risk estimation system 100.

[0097] Although one embodiment of the present invention has been described above, the present invention is not limited to the above configuration and various modifications are possible within the scope of the invention described in the claims. For example, the processes executed by the health risk estimation system 100 are not limited to those described above and can be modified as desired.

[0098] For example, in this embodiment, an example is shown in which the health risk estimation system 100 is applied to a room 1 of a facility for the elderly, but the present invention is not limited to this and can be applied to various other facilities, homes, etc.

[0099] In addition, in this embodiment, an example is shown in which both the subject's movements and blood pressure are detected using the subject detection sensor 110, but the present invention is not limited to this, and a device for detecting the subject's movements and a device for detecting the subject's blood pressure may be provided separately.

[0100] In addition, in this embodiment, an example has been shown in which subject detection sensor 110 that detects information related to the subject's blood pressure in a non-contact manner is used as the blood pressure detection unit, but the present invention is not limited to this. For example, a wearable device or the like that detects the subject's blood pressure while in contact with the subject's body may be used as the blood pressure detection unit.

[0101] Furthermore, in the present embodiment, an example has been shown in which heart rate waveform data is used as an example of "blood pressure information" detected using the blood pressure detection unit (subject detection sensor 110), but the present invention is not limited to this. Various data having features correlated with blood pressure can be used as blood pressure information. Furthermore, instead of indirectly detecting the subject's blood pressure using data having features correlated with blood pressure as described above, a mode in which the subject's blood pressure value is directly detected as "blood pressure information" can also be employed. In this case, a device capable of measuring the subject's blood pressure value can be used as the blood pressure detection unit.

[0102] In addition, in this embodiment, the subject detection sensor 110 (millimeter wave sensor) is used as an example of a detection unit that detects the position and posture of the subject, but the present invention is not limited to this, and various sensors that can detect the posture, blood pressure, etc. of the subject can also be used.

[0103] In addition, in this embodiment, in step S17, it is determined whether the subject has returned from the destination area (second area) to the original area (first area) where the subject was located before moving, but it may also be determined whether the subject has returned from the destination area (second area) to any area other than the second area.

[0104] In addition, in this embodiment, an example is shown in which various processes are executed by the server 140, but the present invention is not limited to this, and various processes can also be executed using the staff terminal 130 or various other devices (personal computers, tablet terminals, etc.). [Explanation of symbols]

[0105] 1 room 100 Health Risk Estimation System 110 Target detection sensor 120 Temperature Sensor 130 Staff Terminal 140 servers

Claims

1. A health risk estimation system for estimating a health risk of a subject in a building having a plurality of areas, comprising: a movement detection unit that detects movement of the subject between areas; a behavior estimation unit capable of estimating the behavior of the subject in a destination area; a temperature detection unit that detects a first temperature, which is the temperature of the area before movement, and a second temperature, which is the temperature of the area to which the movement is made; a blood pressure detection unit that detects a first blood pressure value, which is the blood pressure value of the subject when the subject was in the area before movement, and a second blood pressure value, which is the blood pressure value of the subject when the subject returns from the area to the destination to the area before movement; a temperature and blood pressure data acquiring unit that acquires, for each of the behaviors inferred by the behavior inferrer, temperature and blood pressure data indicating a relationship between a temperature difference, which is a difference between the second temperature and the first temperature, and a blood pressure difference, which is a difference between the second blood pressure value and the first blood pressure value; a health risk estimation unit capable of estimating the health risk of the subject based on a comparison result between first temperature and blood pressure data, which is the temperature and blood pressure data in a first period, and second temperature and blood pressure data, which is the temperature and blood pressure data from an earlier period than the first period and is of a behavior similar to the behavior of the first temperature and blood pressure data; A health risk estimation system comprising:

2. The temperature and blood pressure data acquisition unit The temperature difference and the blood pressure difference are plotted on a graph, and a regression line is calculated based on each plotted point, thereby obtaining the temperature and blood pressure data. The health risk estimation unit A health risk of the subject can be estimated based on a comparison result between the regression line of the first temperature and blood pressure data and the regression line of the second temperature and blood pressure data. The health risk estimation system according to claim 1 .

3. The health risk estimation unit A health risk of the subject can be estimated based on a comparison result between the slope of the regression line of the first temperature and blood pressure data and the slope of the regression line of the second temperature and blood pressure data. The health risk estimation system according to claim 2 .

4. The health risk estimation unit A health risk of the subject can be estimated based on a comparison result between an intercept of the regression line of the first temperature and blood pressure data and an intercept of the regression line of the second temperature and blood pressure data. The health risk estimation system according to claim 2 .

5. The behavior estimation unit The behavior of the subject is estimated based on the duration of stay in the destination area. A health risk estimation system according to any one of claims 1 to 4.

6. a motion detection unit capable of detecting a motion of the subject; The movement detection unit Detecting movement of the subject between areas based on the detection result of the motion detection unit; The blood pressure detection unit and the motion detection unit are a common device. A health risk estimation system according to any one of claims 1 to 4.

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