Deep part body temperature estimation device

Abstract

To provide a deep part body temperature estimation device capable of accurately estimating a deep part body temperature of a bather taking a bath.SOLUTION: A deep part body temperature estimation device 1 includes: a control unit 41 for executing deep part body temperature estimation processing for estimating a deep part body temperature of a bather P1; and detection means 50 and 60 for detecting bathing information on the bather P1. The detection means 50 and 60 includes at least one of biological information detection means 50 for detecting biological information at least including a vital state of the bather P1 as bathing information, and bathing condition information detection means 60 for detecting bathing condition information on a condition during bathing of the bather P1 as bathing information. The control unit 41 can acquire sleep time information digitized on the basis of a past sleep performance of the bather P1. The control unit 41 executes deep part body temperature estimation processing with the bathing information and the sleep time information as parameters.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to a deep body temperature estimation device.

Background Art

[0002] Patent Document 1 discloses an example of a conventional deep body temperature estimation device. This deep body temperature estimation device includes a bathroom remote control installed in a bathroom and second physical condition information acquisition means.

[0003] In step S112 shown in FIG. 3 of Patent Document 1, the second physical condition information acquisition means acquires the physical condition information of a bather taking a bath in a bathtub, such as blood pressure, heart rate, blood flow rate, etc.

[0004] Based on the physical condition information acquired by the second physical condition information acquisition means, the second control unit of the bathroom remote control executes a deep body temperature estimation process for estimating the deep body temperature of the bather taking a bath in step S113 shown in FIG. 3 of Patent Document 1.

[0005] This deep body temperature estimation device uses the estimated deep body temperature to determine the bath exit timing and bedtime so that it is easier for the bather to obtain good-quality sleep after leaving the bath, and notifies the bather.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] By the way, in order for the bather to lead a healthy daily life, it has been considered to provide the bather with highly convenient information based on the deep body temperature of the bather taking a bath. For this reason, this deep body temperature estimation device is required to accurately estimate the deep body temperature.

[0008] The present invention has been made in view of the above-described conventional circumstances, and an object thereof is to provide a deep body temperature estimation device capable of accurately estimating the deep body temperature of a bather during bathing.

Means for Solving the Problems

[0009] The deep body temperature estimation device of the present invention includes a control unit that executes a deep body temperature estimation process for estimating the deep body temperature of a bather during bathing in a bathtub, detection means for detecting the bathing information regarding the bather during bathing and digitizing the detected bathing information, and is a deep body temperature estimation device provided with the detection means includes biological information detection means for detecting biological information including at least the vital state of the bather as the bathing information, bathing condition information detection means for detecting bathing condition information regarding the conditions during bathing of the bather as the bathing information, and has at least one of the control unit can acquire sleep time information digitized based on the past sleep record of the bather, the control unit is characterized in that the deep body temperature estimation process is executed using the bathing information and the sleep time information as parameters.

[0010] In the deep body temperature estimation device of the present invention, the detection means has at least one of biological information detection means and bathing condition information detection means.

[0011] The biological information of the bather detected by the biological information detection means as the bathing information includes at least the vital state of the bather. The vital state is, for example, the bather's heart rate, blood flow rate, sweating amount, etc.

[0012] The bathing condition information detected by the bathing condition information detection means as the bathing information is, for example, the temperature of the hot water stored in the bathtub, the elapsed time since the bather started bathing, the temperature of the bathroom where the bathtub is installed, etc.

[0013] The sleep time information acquired by the control unit is, for example, the average value or median value of the sleep time in a past predetermined period, the number of days with a sleep time of a predetermined time or more in the past predetermined period, and the like.

[0014] The control unit executes deep body temperature estimation processing using such bathing information and sleep time information as parameters.

[0015] Here, when comparing the case where the daily sleep time tends to be long and the case where it tends to be short, it is considered that in the case where it tends to be long, the autonomic nervous system (the balance between the sympathetic nerve and the parasympathetic nerve) is more likely to be adjusted, and the adjustment of blood flow volume can be performed smoothly. For this reason, a person whose daily sleep time tends to be long tends to have a large blood flow volume in a hot environment.

[0016] In addition, since the hot water in the bathtub in which the bather soaking during bathing has a temperature higher than the body temperature of the bather, when a person whose daily sleep time tends to be long takes a bath, it is considered that the blood flow volume of the bather is likely to increase. And, since the hot water stored in the bathtub has a larger heat capacity than air, the heat exchange between the body surface of the bather and the hot water is promoted, and the heat transmitted from the hot water to the blood in the body via the body surface is likely to move to the deep part of the body by blood circulation. As a result, it is considered that the deep body temperature is likely to rise.

[0017] As described above, since there is considered to be a correlation between the deep body temperature during bathing and the sleep time, the sleep time information is suitable as a parameter for estimating the deep body temperature during bathing.

[0018] Therefore, the deep body temperature estimation device of the present invention can accurately estimate the deep body temperature of the bather during bathing.

[0019] The sleep time information is preferably information based on one of the average value of the sleep time in a past predetermined period, the median value of the sleep time in a past predetermined period, and the number of days with a sleep time of a predetermined time or more in the past predetermined period.

[0020] By using such specific sleep time information, the deep body temperature of the bather during bathing can be estimated with higher accuracy.

[0021] It is desirable that the deep body temperature estimation device of the present invention further includes a communication unit capable of communicating with at least one of a sleep time information terminal that stores the bathing person's sleep record and calculates sleep time information, and an external server that acquires the sleep time information from the sleep time information terminal through network communication. And it is desirable that the control unit acquires the sleep time information via the communication unit.

[0022] In this case, the control unit can use the sleep time information of the bather updated by the sleep time information terminal as a parameter for the deep body temperature estimation process. As a result, the control unit can execute the deep body temperature estimation process without the bather performing a special operation for the control unit to acquire the sleep time information, such as an input operation of the sleep record, etc., so that an improvement in convenience can be realized.

[0023] It is desirable that the sleep time information terminal detects the sleep time of one day by dividing it into non-REM sleep time and REM sleep time, and stores at least the non-REM sleep time or the REM sleep time among the sleep time, non-REM sleep time, and REM sleep time as a sleep record and calculates the sleep time information.

[0024] Non-REM sleep means sleep in a state where one is sleepy but in a deep state, and REM sleep means sleep in a state where one is sleepy but in a shallow state. It is considered that there is a correlation between the non-REM sleep time, which is the time of non-REM sleep, or the REM sleep time, which is the time of REM sleep, and the degree to which the autonomic nervous system is adjusted (the degree to which the balance between the sympathetic nerve and the parasympathetic nerve is adjusted). Therefore, by configuring the sleep time information terminal as described above, sleep time information effective for estimating the deep body temperature during bathing can be calculated.

[0025] It is desirable that the sleep time information terminal stores the non-REM sleep time as a sleep record and calculates the sleep time information.

[0026] In this case, when the daily sleep time is divided into non-REM sleep time and REM sleep time, it is considered that when the non-REM sleep time tends to be long (when the REM sleep time tends to be short), the autonomic nervous system (the balance between the sympathetic nerve and the parasympathetic nerve) is more likely to be adjusted, and the blood flow can be smoothly adjusted. Therefore, by the sleep time information terminal storing the non-REM sleep time as the sleep record and calculating the sleep time information, the deep body temperature of the bather during the bath can be estimated with higher accuracy.

[0027] The detection means preferably has at least a biological information detection means. And the biological information detection means preferably has at least one of a heart rate detection means for detecting the heart rate of the bather as a vital state, a blood flow detection means for detecting the blood flow of the bather as a vital state, and a sweating amount detection means for detecting the sweating amount of the bather as a vital state.

[0028] In this case, since at least one of the heart rate, blood flow, and sweating amount of the bather is included in the biological information used as a parameter for the control unit to estimate the deep body temperature, the deep body temperature of the bather during the bath can be estimated with higher accuracy.

[0029] The detection means preferably has at least a bath condition information detection means. And the bath condition information detection means preferably has at least one of a hot water temperature detection means for detecting the temperature of the hot water stored in the bathtub, a bath elapsed time detection means for detecting the elapsed time since the bather started bathing, and a bathroom temperature detection means for detecting the temperature of the bathroom where the bathtub is installed.

[0030] In this case, since at least one of the temperature of the hot water stored in the bathtub, the bath elapsed time, and the temperature of the bathroom is included in the bath condition information used as a parameter for the control unit to estimate the deep body temperature, the deep body temperature of the bather during the bath can be estimated with higher accuracy.

[0031] The control unit desirably can acquire quantified exercise habit information based on the past exercise achievements of the bather. Then, it is desirable that the control unit execute deep body temperature estimation processing using the bathing information, sleep time information, and exercise habit information as parameters.

[0032] The exercise habit information acquired by the control unit is, for example, the frequency of exercise, the required time for exercise, the intensity of exercise, etc. People with a daily exercise habit tend to have a higher vasodilation function and a greater tendency for increased blood flow compared to those without an exercise habit in order to dissipate the heat of the body increased by exercise or the like. For this reason, people with a daily exercise habit tend to have an increased blood flow in a hot environment. Since the hot water in the bathtub in which the bather is immersed during bathing has a temperature higher than the body temperature of the bather, when a person with an exercise habit takes a bath, it is considered that the blood flow of the bather is likely to increase. And, since the hot water stored in the bathtub has a larger heat capacity than air, the heat exchange between the body surface of the bather and the hot water is promoted, and the heat transmitted from the hot water to the blood in the body via the body surface is likely to move to the deep part of the body by blood circulation. As a result, it is considered that the deep body temperature is likely to rise. Thus, since there is considered to be a correlation between the deep body temperature during bathing and the exercise habit, the exercise habit information is suitable as a parameter for estimating the deep body temperature during bathing. Therefore, this deep body temperature estimation device can more accurately estimate the deep body temperature of the bather during bathing.

[0033] The deep body temperature estimation device of the present invention preferably further includes a communication unit capable of communicating with at least one of a portable information terminal that is routinely carried by the bather and stores the exercise achievements of the bather to calculate exercise habit information, and an external server that acquires exercise habit information from the portable information terminal by network communication. Then, it is desirable that the control unit acquire the exercise habit information via the communication unit.

[0034] In this case, the control unit can use the exercise habit information of the bather updated by the portable information terminal as a parameter for the deep body temperature estimation process. As a result, the control unit can execute the deep body temperature estimation process without the bather performing a special operation for the control unit to acquire the exercise habit information, such as an input operation of exercise achievements, etc., on the deep body temperature estimation device, so that an improvement in convenience can be realized.

Effect of the Invention

[0035] According to the deep body temperature estimation device of the present invention, the deep body temperature of the bather during bathing can be accurately estimated.

Brief Description of the Drawings

[0036]

Figure 1

Figure 2

Figure 3

Figure 4

Modes for Carrying Out the Invention

[0037] Hereinafter, Examples 1 to 5 embodying the present invention will be described with reference to the drawings.

[0038] (Example 1) As shown in FIG. 1, the deep body temperature estimation device 1 of Example 1 is an example of a specific aspect of the deep body temperature estimation device of the present invention and is applied to a house H1. The house H1 is provided with a plurality of rooms such as a living room and a bedroom, and a bathroom R1 and a kitchen R2 are provided. In addition, a hot water supply device 90 is installed in the house H1.

[0039] In bathroom R1, there are installed a bathtub 3, a mixing faucet 5, a shower 5A, and a bathroom remote control 10. In kitchen R2, there is installed a kitchen remote control 20.

[0040] The bather P1 taking a bath in the bathtub 3 in bathroom R1 is an occupant of house H1, and lives by moving around each room of house H1 or going out of house H1. The bather P1 when not taking a bath is defined as the target person P1A (bather P1).

[0041] The target person P1A (bather P1) wears a smartwatch 30 on the wrist and carries it around daily. The smartwatch 30 is an example of the "portable information terminal" and "sleep time information terminal" of the present invention.

[0042] <Hot water supply device> Since the hot water supply device 90 has a well-known configuration, the description will be simplified. It has a gas burner, a heat exchanger, a circulation pump, etc. (not shown). The gas burner burns fuel gas such as city gas to generate high-temperature combustion gas. The hot water supply device 90 circulates the water supplied from a water supply such as a water pipe in the heat exchanger, and heats the water by performing heat exchange with the combustion gas generated by the gas burner, and discharges hot water.

[0043] The hot water supply device 90 supplies hot water to the mixing faucet 5 and the shower 5A via the pipe P5. Also, the hot water supply device 90 supplies hot water to the bathtub 3 via the bathtub supply pipe P3A. The hot water supply device 90 can execute an automatic water filling operation for automatically filling the bathtub 3 with water, a supplementary hot water operation for adding hot water to the bathtub 3, and a supplementary water operation for adding water to the bathtub 3.

[0044] Furthermore, the hot water supply device 90 can execute a reheating operation of heating with a gas burner while circulating hot water between the bathtub 3, the bathtub return pipe P3B, the heat exchanger, and the bathtub supply pipe P3A by a circulation pump (not shown).

[0045] As shown in FIGS. 1 and 2, the hot water supply device 90 has a hot water supply control unit 91. The hot water supply control unit 91 is an electronic circuit unit including a CPU (not shown), a storage unit 91M composed of storage elements such as a ROM and a RAM, an interface circuit, and the like. The hot water supply control unit 91 executes control processes related to the operations of the gas burner, heat exchanger, circulation pump, etc. of the hot water supply device 90.

[0046] The storage unit 91M stores various programs and setting information for operating the hot water supply device 90 and the like. Also, the storage unit 91M appropriately stores various information acquired by the hot water supply control unit 91 during the operation of the hot water supply device 90 and the like.

[0047] The hot water supply device 90 has a hot water temperature sensor 92 and a water level sensor 93.

[0048] The hot water temperature sensor 92 measures at least one of the temperature of the hot water sent from the hot water supply device 90 to the bathtub 3 and the temperature of the hot water returned from the bathtub 3 to the hot water supply device 90, and detects the temperature of the hot water stored in the bathtub 3. The hot water supply control unit 91 uses the detection result of the hot water temperature sensor 92 for control such as supplementary heating operation.

[0049] The deep body temperature estimation device 1 of the first embodiment does not use the hot water temperature sensor 92 to detect the temperature of the hot water stored in the bathtub 3, but in a modification described later, the deep body temperature estimation device may use the hot water temperature sensor 92.

[0050] The water level sensor 93 includes a pressure sensor that measures the pressure in the internal pipe connected to the bathtub 3 via the bathtub supply pipe P3A and the bathtub return pipe P3B. The water level sensor 93 detects the water level of the bathtub 3 based on the hydrostatic pressure measured by the pressure sensor when the water surface in the bathtub 3 is in a static state. The hot water supply control unit 91 uses the detection result of the water level sensor 93 for control such as automatic water filling operation.

[0051] When the bather P1 is in the bathtub 3, the water level is significantly higher than when the bather P1 is not in the bathtub 3. Based on such a change in the water level, the water level sensor 93 detects whether the bather P1 is in the bathtub 3 or not.

[0052] The deep body temperature estimation device 1 of the first embodiment does not use the water level sensor 93 to detect whether the bather P1 is in the bathtub 3 or not. However, in a modified example described later, the deep body temperature estimation device may use the water level sensor 93.

[0053] <Bathroom remote control> The bathroom remote control 10 is connected in a wired communication-capable manner to the hot water supply control unit 91 of the hot water supply device 90 and the remote control unit 21 of the kitchen remote control 20, which will be described later. As shown in FIG. 2, the bathroom remote control 10 has an input unit 12B, a display unit 12D, a remote control communication unit 19, and a remote control unit 11.

[0054] The bathroom remote control 10 transmits the operation performed by the bather P1 on the input unit 12B to the hot water supply device 90 to remotely control the hot water supply device 90. Further, the bathroom remote control 10 displays various information such as the operation status and setting information transmitted from the hot water supply device 90 on the display unit 12D.

[0055] Furthermore, the bathroom remote control 10 can also receive the input operation performed by the bather P1 on the deep body temperature estimation device 1 by the input unit 12B, or display various information about the deep body temperature estimation device 1 on the display unit 12D.

[0056] The remote control communication unit 19 performs wireless communication with the wireless router 7 installed in the house H1 by Wi-Fi (registered trademark) or the like. The remote control unit 11 is connected to the external network NW1 via the remote control communication unit 19 and the wireless router 7, and can perform network communication with an information processing terminal such as an external server 9 connected to the network NW1. The external server 9 is a support server for the deep body temperature estimation device 1.

[0057] Further, the remote control communication unit 19 can also directly perform wireless communication with the communication unit 49 (to be described later) of the deep body temperature estimation device 1 via Bluetooth (registered trademark). Note that if the bathroom remote control 10 is changed so as not to have the remote control communication unit 19, the remote control unit 11 can perform wireless communication with the communication unit 49 of the deep body temperature estimation device 1 via the remote control unit 21 of the kitchen remote control 20 and the wireless router 7, or perform network communication with the external server 9 or the like.

[0058] The remote control unit 11 is an electronic circuit unit including a CPU (not shown), a storage unit 11M composed of storage elements such as a ROM and a RAM, and an interface circuit or the like.

[0059] The remote control unit 11 controls the operations of the input unit 12B and the display unit 12D of the bathroom remote control 10. Further, the remote control unit 11 controls the wired communication between the bathroom remote control 10 and the hot water supply control unit 91 of the hot water supply device 90.

[0060] Furthermore, the remote control unit 11 receives an input operation performed by the bather P1 on the deep body temperature estimation device 1 through the input unit 12B, transmits the input operation to the deep body temperature estimation device 1 through the remote control communication unit 19, receives various information about the deep body temperature estimation device 1 through the remote control communication unit 19, and performs control to display the various information on the display unit 12D.

[0061] The storage unit 11M stores various programs and setting information for operating the bathroom remote control 10. Further, the storage unit 11M appropriately stores various information acquired by the remote control unit 11 during the operation of the bathroom remote control 10.

[0062] <Kitchen remote control> The kitchen remote control 20 is connected to be capable of wired communication with the hot water supply control unit 91 of the hot water supply device 90 and the remote control unit 11 of the bathroom remote control 10. The kitchen remote control 20 has an input unit 22B, a display unit 22D, a remote control communication unit 29, and a remote control unit 21.

[0063] The kitchen remote controller 20 transmits the operations performed by the resident in the kitchen R2 on the input unit 22B to the water heater 90 to remotely control the water heater 90. Further, the kitchen remote controller 20 displays various information such as the operation status and setting information transmitted from the water heater 90 on the display unit 22D.

[0064] The remote control communication unit 29 performs wireless communication with the wireless router 7. The remote control unit 21 is connected to an external network NW1 via the remote control communication unit 29 and the wireless router 7, and can perform network communication with an information processing terminal such as an external server 9 connected to the network NW1.

[0065] The remote control unit 21 is an electronic circuit unit including a CPU (not shown), a storage unit 21M composed of storage elements such as a ROM and a RAM, and an interface circuit or the like.

[0066] The remote control unit 21 controls the operations of the input unit 22B and the display unit 22D of the kitchen remote controller 20. Further, the remote control unit 21 controls the wired communication between the kitchen remote controller 20 and the hot water supply control unit 91 of the water heater 90.

[0067] The storage unit 21M stores various programs and setting information for operating the kitchen remote controller 20. Further, the storage unit 21M appropriately stores various information acquired by the remote control unit 21 during the operation of the kitchen remote controller 20.

[0068] <Smartwatch> As shown in FIG. 1, the smartwatch 30 is a wristwatch-type portable information terminal, and has wireless communication functions such as a telephone and network communication, a music playback function, a life log function, and the like. The life log function is a function for recording the number of steps, moving distance, amount of exercise, calorie consumption (basal metabolism + exercise consumption), sleep data (sleep time, depth of sleep, respiratory rate and body movement during sleep), etc. in a day.

[0069] As shown in FIG. 2, the smartwatch 30 has a terminal control unit 31, a touch panel 32, and a terminal communication unit 39.

[0070] The terminal control unit 31 is an electronic circuit unit including a CPU (not shown), a storage unit 31M composed of storage elements such as a ROM and a RAM, and an interface circuit and the like. The terminal control unit 31 executes control processing related to the operation of the smartwatch 30.

[0071] The storage unit 31M stores various programs and setting information for operating the smartwatch 30. Also, the storage unit 31M appropriately stores various information acquired by the terminal control unit 31. The various information stored in the storage unit 31M includes the life log of the person P1A (bather P1) who routinely carries the smartwatch 30. The terminal control unit 31 updates the life log of the person P1A (bather P1) stored in the storage unit 31M at a predetermined interval (for example, daily).

[0072] The touch panel 32 displays various information such as characters and images by a display unit such as a liquid crystal panel, and receives various inputs by an operation in which the user touches the display unit with a fingertip on an input unit that covers the display unit in a state where the various information displayed on the display unit is visible to the user.

[0073] The terminal communication unit 39 is capable of executing a call using the frequency band of a mobile phone. Also, the terminal communication unit 39 incorporates an electronic circuit that executes wireless communication such as Bluetooth (registered trademark) and Wi-Fi (registered trademark). The terminal communication unit 39 directly executes wireless communication with the deep body temperature estimation device 1 via Bluetooth (registered trademark). Also, the terminal communication unit 39 executes wireless communication with the deep body temperature estimation device 1 via a wireless router 7 or executes wireless communication with the remote control communication unit 19 of the bathroom remote control 10 via Wi-Fi (registered trademark) or the like.

[0074] The terminal control unit 31 executes network communication with the external server 9 via the terminal communication unit 39, and can upload the life log of the subject P1A (bather P1) to the external server 9 so that the amount of information in the life log of the subject P1A (bather P1) does not exceed the storage capacity of the storage unit 31M.

[0075] Also, the terminal control unit 31 can execute network communication with the external server 9 and download an information collection application program for assisting the deep body temperature estimation device 1 and store it in the storage unit 31M.

[0076] By executing the information collection application program, a part of the terminal control unit 31 functions as the exercise habit information calculation unit 31A, and another part functions as the sleep time information calculation unit 31B.

[0077] <Exercise habit information calculation unit> Based on the life log of the subject P1A (bather P1) stored in the storage unit 31M, the exercise habit information calculation unit 31A calculates quantified exercise habit information based on the past exercise performance of the subject P1A (bather P1).

[0078] As the exercise habit information, for example, the frequency of exercise, the required time of exercise, the intensity of exercise, etc. can be appropriately selected. In this embodiment, the exercise habit information is information based on the number of days in which exercise of a predetermined amount or more has been performed for a predetermined time or more in the past predetermined period.

[0079] The "past predetermined period" is preferably a long period such as in years or months because it relates to habits. In this embodiment, it is the past six months. The "predetermined amount of exercise or more" preferably has a numerically clear difference from daily activities without exercise. In this embodiment, it is an exercise intensity (strength of exercise) of 4.0 METs or more. The "predetermined time or more" preferably has a numerically clear difference from daily activities without exercise. In this embodiment, it is 30 minutes or more. Note that for the "predetermined amount of exercise or more", it may be defined as "exercise that causes sweating" without using the exercise intensity.

[0080] On the homepage of the Longevity Science Promotion Foundation, a public interest incorporated foundation, it is stated that "Exercise intensity (the intensity of exercise) is measured by the amount of oxygen taken into the body per 1 kg of body weight. Since the amount of oxygen is difficult to understand, the unit of MET (metabolic equivalent) is used. MET is a unit that indicates the exercise intensity by how many times the energy can be consumed during that exercise when the oxygen uptake at rest of 3.5 ml / kg / min is set to 1. For example, sitting and watching TV or riding in a car is 1.0 MET, sitting and talking, eating, or doing desk work is 1.5 MET, cooking, doing laundry, changing clothes, washing face, taking a shower, walking around the house, etc. are 2.0 MET, walking or cleaning is 3.0 MET, table tennis or the first part of calisthenics is 4.0 MET, badminton or golf is 4.3 MET, baseball or softball is 5.0 MET, weight training, swimming, or basketball is 6.0 MET...".

[0081] It is considered that when exercising at an exercise intensity of 4.0 MET or more for 30 minutes or more, vital states such as heart rate, blood flow volume, and sweating amount tend to increase compared to the vital states in daily activities without exercise.

[0082] When the subject P1A (bather P1) operates the touch panel 32 and routinely inputs records regarding the time and type of exercise performed at a specific date and time, the terminal control unit 31 associates the exercise amount and calorie consumption (basal metabolism + exercise consumption) at each time and stores them as a part of the life log in the storage unit 31M.

[0083] The exercise habit information calculation unit 31A refers to the information on the time and type of exercise performance input by the subject P1A (bather P1), extracts exercise performance records of performing exercise of a predetermined amount or more (exercise intensity of 4.0 MET or more) for a predetermined time or more (30 minutes or more) from the life log of a predetermined past period (the past six months) stored in the storage unit 31M, and calculates the exercise habit information of the subject P1A (bather P1) by obtaining the number of days with such exercise performance.

[0084] The exercise habit information calculated by the exercise habit information calculation unit 31A is stored in the storage unit 31M. The terminal control unit 31 updates the exercise habit information of the target person P1A (bather P1) stored in the storage unit 31M at a predetermined interval (for example, every day).

[0085] <Sleep time information calculation unit> The sleep time information calculation unit 31B calculates quantified sleep time information based on the past sleep performance of the target person P1A (bather P1) stored in the storage unit 31M.

[0086] As the sleep time information, for example, the average value or median of the sleep time in a past predetermined period, the number of days with a sleep time of a predetermined time or more in a past predetermined period, etc. can be appropriately selected. In this embodiment, the sleep time information is the average value of the sleep time in a past predetermined period. The "past predetermined period" is preferably a long period such as in years or months because it relates to habits. In this embodiment, it is the past six months.

[0087] It is considered that the more the average value of the sleep time in a past predetermined period increases, the easier it is for the autonomic nerves (the balance between the sympathetic nerves and the parasympathetic nerves) to be adjusted.

[0088] The sleep time information calculation unit 31B extracts the sleep time from the life log of a past predetermined period (the past six months) stored in the storage unit 31M, and calculates the average value of the sleep time as the sleep time information of the target person P1A (bather P1).

[0089] The sleep time information calculated by the sleep time information calculation unit 31B is stored in the storage unit 31M. The terminal control unit 31 updates the sleep time information of the target person P1A (bather P1) stored in the storage unit 31M at a predetermined interval (for example, every day).

[0090] <Deep body temperature estimation device> As shown in FIG. 1, the deep body temperature estimation device 1 is installed in the bathroom R1. The main body 40 of the deep body temperature estimation device 1 is attached to the side wall of the bathroom R1 so as to be exposed in the bathroom R1. Note that the main body 40 of the deep body temperature estimation device 1 may be installed on the ceiling back or the back side of the side wall of the bathroom R1 so as not to be exposed in the bathroom R1.

[0091] As shown in FIGS. 1 and 2, the deep body temperature estimation device 1 includes a control unit 41, a biological information detection means 50, and a bathing condition information detection means 60. The biological information detection means 50 and the bathing condition information detection means 60 are examples of the "detection means" of the present invention.

[0092] The control unit 41 is housed in the main body � of the deep body temperature estimation device 1. The control unit 41 is an electronic circuit unit including a CPU (not shown), a storage unit 41M constituted by storage elements such as a ROM and a RAM, and an interface circuit or the like. The control unit 41 executes control processing related to the operation of the deep body temperature estimation device 1.

[0093] The storage unit 41M stores various programs and setting information for operating the deep body temperature estimation device 1. Further, the storage unit 41M appropriately stores various information acquired by the control unit 41. The various information stored in the storage unit 41M includes the deep body temperature estimation program shown in FIGS. 3 and 4.

[0094] As will be described later, the control unit 41 executes a deep body temperature estimation process for estimating the deep body temperature of the bather P1 in the bathtub 3 during bathing by executing the deep body temperature estimation program shown in FIGS. 3 and 4.

[0095] Here, the deep body temperature is the temperature inside the main organs in the body, for example, the brain and internal organs. Therefore, in experiments and the like, in order to accurately measure the deep body temperature, a sensor is inserted into the esophagus or rectum to invasively measure the deep body temperature. However, in daily life, the deep body temperature is estimated based on the measured values obtained by non-invasive methods.

[0096] As shown in FIG. 1, the biological information detection means 50 and the bathing condition information detection means 60 are each provided outside the main body 40 of the deep body temperature estimation device 1 and are wired-connected to the control unit 41. The biological information detection means 50 and the bathing condition information detection means 60 detect bath information regarding the bather P1 during bathing, and digitized bath information.

[0097] Note that the biological information detection means 50 and the bathing condition information detection means 60 may be wirelessly connected to the control unit 41 by performing wireless communication such as Bluetooth (registered trademark) between the biological information detection means 50 and the bathing condition information detection means 60 and the communication unit 49.

[0098] The biological information detection means 50 is a wristband-type detection device that is worn on the wrist of the bather P1 when the bather P1 takes a bath in the bathtub 3. The biological information detection means 50 detects biological information including at least the vital state of the bather P1 as bath information. The vital state is, for example, the heart rate, blood flow volume, sweating amount, respiratory rate, blood pressure, etc. of the bather P1.

[0099] As shown in FIG. 2, the biological information detection means 50 includes a heart rate detection means 50A, a blood flow volume detection means 50B, and a sweating amount detection means 50C.

[0100] The heart rate detection means 50A detects the heart rate of the bather P1 as a vital state. Examples of the detection method of the heart rate detection means 50A include a method of measuring the expansion and contraction of the blood vessels in the wrist according to the heartbeat by a pressure sensor or an optical sensor and detecting the heart rate based on the cycle of the expansion and contraction.

[0101] The blood flow volume detection means 50B detects the blood flow volume of the bather P1 as a vital state. Examples of the detection method of the blood flow volume detection means 50B include a method of irradiating light on the skin of the wrist using an LED and measuring the change in the intensity of the reflected light caused by the blood flow with a photodiode.

[0102] The sweating amount detection means 50C detects the sweating amount of the bather P1 as a vital condition. As a detection method of the sweating amount detection means 50C, for example, there is a method of measuring the air humidity before passing through the skin of the wrist and the air humidity after passing through the skin (including the evaporated moisture of sweat) with two humidity sensors, and detecting the sweating amount from the difference therebetween.

[0103] The detection results of the heart rate detection means 50A, the blood flow rate detection means 50B, and the sweating amount detection means 50C are transmitted to the control unit 41.

[0104] As shown in FIG. 1, the bathing condition information detection means 60 detects bathing condition information regarding the conditions during the bath of the bather P1 as bathing information. The bathing condition information is, for example, the temperature and water level of the hot water stored in the bathtub 3, the elapsed time since the bather P1 started bathing, the temperature and humidity of the bathroom R1 in which the bathtub 3 is installed, and the like.

[0105] As shown in FIGS. 1 and 2, the bathing condition information detection means 60 includes a hot water temperature detection means 60A, a bathing elapsed time detection means 60B, and a bathroom temperature detection means 60C.

[0106] The hot water temperature detection means 60A has a hot water temperature sensor installed on the side wall surface of the bathtub 3. The hot water temperature detection means 60A directly detects the temperature of the hot water stored in the bathtub 3.

[0107] The bathing elapsed time detection means 60B has a pressure sensor and a timing means installed on the side wall surface of the bathtub 3. The pressure sensor detects whether or not the bather P1 has started bathing based on the pressure difference between the water level when the bather P1 is in the bathtub 3 and the water level when the bather P1 is not in the bathtub 3. The timing means starts timing when the pressure sensor detects the start of bathing by the bather P1. In this way, the bathing elapsed time detection means 60B detects the elapsed time since the bather P1 started bathing.

[0108] The bathroom temperature detection means 60C is a temperature sensor installed in the vicinity of the bathtub 3 on the side wall surface of the bathroom R1. The bathroom temperature detection means 60C detects the temperature of the bathroom R1.

[0109] The detection results of the bath water temperature detection means 60A, the bathing elapsed time detection means 60B, and the bathroom temperature detection means 60C are transmitted to the control unit 41.

[0110] As shown in FIG. 2, the deep body temperature estimation device 1 further includes a communication unit 49. The communication unit 49 incorporates an electronic circuit that performs wireless communication by Bluetooth (registered trademark), Wi-Fi (registered trademark), etc. The communication unit 49 directly performs wireless communication with the terminal communication unit 39 of the smart watch 30 by Bluetooth (registered trademark). Further, the communication unit 49 performs wireless communication with the terminal communication unit 39 of the smart watch 30 via the wireless router 7 by Wi-Fi (registered trademark) or the like, or performs wireless communication with the remote control communication unit 19 of the bathroom remote control 10.

[0111] When the control unit 41 executes the deep body temperature estimation process described later, it performs wireless communication with the terminal control unit 31 of the smart watch 30 via the terminal communication unit 39 and the communication unit 49. Then, the control unit 41 acquires the exercise habit information and sleep time information stored in the storage unit 31M via the terminal communication unit 39 and the communication unit 49.

[0112] <Deep body temperature estimation process> When the subject P1A (bather P1) starts preparations for bathing, the input unit 12B of the bathroom remote control 10 is operated in the bathroom R1, or the input unit 22B of the kitchen remote control 20 is operated in the kitchen R2 to cause the hot water supply device 90 to perform a hot water filling operation or a supplementary hot water operation, and the hot water stored in the bathtub 3 is made ready for bathing.

[0113] At this time, the control unit 41 of the deep body temperature estimation device 1 executes the deep body temperature estimation program shown in FIGS. 3 and 4, for example, to estimate the health state of the bather P1, estimate the health promotion effect of bathing, or propose bathing conditions for improving the quality of sleep after bathing.

[0114] First, in step S101 shown in FIG. 3, the control unit 41 acquires the exercise habit information and sleep time information of the target person P1A (bather P1) stored in the storage unit 31M of the smart watch 30. When the exercise habit information and sleep time information of the target person P1A (bather P1) are uploaded from the smart watch 30 to the external server 9, the control unit 41 can also acquire the exercise habit information and sleep time information of the target person P1A (bather P1) from the external server 9. When there are a plurality of residents living in the house H1, the number of target persons P1A (bathers P1) registered in this program can also be plural. When there are a plurality of target persons P1A (bathers P1), the exercise habit information and sleep time information of all of them are acquired. Then, the control unit 41 updates the exercise habit information and sleep time information of the target person P1A (bather P1) stored in the storage unit 41.

[0115] Next, the control unit 41 proceeds to step S102 and determines whether the biological information detection means 50 is worn on the arm of the bather P1. While the detection results are not transmitted from the heart rate detection means 50A, blood flow rate detection means 50B, and sweating amount detection means 50C to the control unit 41, it is "No", so the control unit 41 repeats step S102. Then, when the detection results are transmitted from the heart rate detection means 50A, blood flow rate detection means 50B, and sweating amount detection means 50C to the control unit 41, it becomes "Yes", so the control unit 41 determines that the biological information detection means 50 is worn and proceeds to step S103.

[0116] When the control unit 41 proceeds to step S103, it causes the display unit 12D of the bathroom remote control 10 to perform a notification prompting an input for selecting the target person for the deep body temperature estimation process. The bather P1 wearing the biological information detection means 50 will perform an input operation on the input unit 12B of the bathroom remote control 10 in response to the notification.

[0117] Next, the control unit 41 proceeds to step S104 and determines whether an input for selecting a target person has been made. The control unit 41 repeats step S104 while it is "No" in step S104. Then, when it becomes "Yes" in step S104, the control unit 41 proceeds to step S105.

[0118] When the control unit 41 proceeds to step S105, based on the input for selecting a subject, the bather P1 who is the subject of the current deep body temperature estimation process is selected from among the registered subjects P1A (bathers P1).

[0119] Next, the control unit 41 proceeds to step S106, reads out the exercise habit information and sleep time information of the bather P1 who is the processing target stored in the storage unit 41, and sets them as parameters of the estimation formula used in step S116 described later.

[0120] Here, the estimation formula for the deep body temperature used in this embodiment is a formula having eight parameters, [Estimated value Y of deep body temperature]=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8 where:

[0121] β0 is the intercept. β1 to β8 are regression coefficients. x1 to x8 are parameters. In step S106, the exercise habit information is set as the parameter x1, and the sleep time information is set as the parameter x2.

[0122] The estimation formula is constructed every time step S116 is executed based on a deep body temperature estimation algorithm obtained by previously conducting bathing tests on a plurality of subjects, using the exercise habit information and sleep time information of each subject, and the biological information (heart rate, blood flow rate, sweating amount) and bathing condition information (hot water temperature, bathing elapsed time, bathroom temperature) of each subject during bathing as input information, and using the deep body temperature (measured value of rectal temperature) as output information.

[0123] Note that the estimation formula for the deep body temperature is an example, and the number of parameters is not limited to eight. Although not listed as parameters in this embodiment, the height, weight, age, and gender of the bather P1 can also be used as parameters.

[0124] Next, the control unit 41 proceeds to step S107 and determines whether the bather P1 is in the bathtub 3. In this embodiment, the control unit 41 makes the determination based on the pressure sensor of the bathing elapsed time detection means 60B. The control unit 41 repeats step S107 while the determination in step S107 is "No". Then, when the determination in step S107 becomes "Yes", the control unit 41 proceeds to step S108.

[0125] When the control unit 41 proceeds to step S108, it starts the deep body temperature estimation process.

[0126] Next, the control unit 41 proceeds to step S111 shown in FIG. 4. Then, the control unit 41 controls the biological information detection means 50, and continuously detects biological information (heart rate, blood flow rate, sweating amount) as bathing information by the heart rate detection means 50A, the blood flow rate detection means 50B, and the sweating amount detection means 50C.

[0127] Next, the control unit 41 proceeds to step S112. Then, the control unit 41 controls the bathing condition information detection means 60, and continuously detects bathing condition information (hot water temperature, bathing elapsed time, bathroom temperature) as bathing information by the hot water temperature detection means 60A, the bathing elapsed time detection means 60B, and the bathroom temperature detection means 60C.

[0128] Next, the control unit 41 proceeds to step S113 and waits for a predetermined waiting time. The predetermined waiting time means the time interval of the deep body temperature estimation process in step S116. The predetermined waiting time is desirably short enough to suitably follow the physical condition change due to the bathing of the bather P1, and desirably 1 minute or less. Also, if the predetermined waiting time is too short, the calculation load becomes excessive, so it is desirably several seconds or more. In this embodiment, the predetermined waiting time is 20 seconds.

[0129] Next, the control unit 41 proceeds to step S114 and determines whether the bather P1 is in the bathtub 3. If the control unit 41 determines "Yes" in step S114, it proceeds to step S115. On the other hand, if the control unit 41 determines "No" in step S114, it determines that the bather P1 has finished bathing and proceeds to step S121. The processing after step S121 will be described later.

[0130] When the control unit 41 proceeds from step S114 to step S115, it sets the bathing information (biological information and bathing condition information) of the bather P1 detected in real time as parameters of the above-described estimation formula.

[0131] In step S115, regarding the biological information, the heart rate is set as parameter x3, the blood flow rate is set as parameter x4, and the sweating amount is set as parameter x5. Regarding the bathing condition information, the hot water temperature is set as parameter x6, the bathing elapsed time is set as parameter x7, and the bathroom temperature is set as parameter x8.

[0132] Next, the control unit 41 proceeds to step S116. Then, using the bathing information (biological information and bathing condition information), exercise habit information, and sleep time information as parameters, the control unit 41 constructs the above-described estimation formula and calculates [estimated value Y of the core body temperature].

[0133] Next, the control unit 41 proceeds to step S117 and stores the estimated core body temperature [estimated value Y of the core body temperature] in the storage unit 41M. The storage unit 41M stores [estimated value Y of the core body temperature] as time-series data at intervals of a predetermined standby time (20 seconds).

[0134] Note that the control unit 41 can execute network communication with the external server 9 via the communication unit 49 and upload the [estimated value Y of the core body temperature] of the subject P1A (bather P1) to the external server 9 so that the amount of information of the [estimated value Y of the core body temperature] of the subject P1A (bather P1) does not exceed the storage capacity of the storage unit 41M.

[0135] The control unit 41 can also upload bathing information (biometric information and bathing condition information) of the bather P1 to the external server 9. Furthermore, the control unit 41 can have the external server 9 execute some of the core body temperature estimation processing that requires a large computational load.

[0136] Next, the control unit 41 proceeds to step S118 and displays the estimated core body temperature, etc. on the display unit 12D of the bathroom remote control 10. At this time, the control unit 41 displays the detection results of the heart rate detection means 50A, blood flow detection means 50B, and sweat rate detection means 50C, as well as information obtained by analyzing them, on the display unit 12D of the bathroom remote control 10, according to the user's wishes.

[0137] Thereafter, the control unit 41 returns to step S113 and repeats steps S113 to S118.

[0138] When the process moves from step S114 to step S121, the control unit 41 ends the detection operations of the biological information detection means 50 and the bathing condition information detection means 60, and then ends this program.

[0139] <Action and effect> As shown in FIGS. 1 and 2, the deep body temperature estimating device 1 of the first embodiment includes a biological information detecting means 50 and a bathing condition information detecting means 60 as detecting means.

[0140] The biological information of the bather P1 detected as bathing information by the biological information detection means 50 is the bather P1's vital status, that is, the bather P1's heart rate, blood flow, and sweat rate.

[0141] The bathing condition information detected by the bathing condition information detection means 60 as bathing information is the temperature of the water stored in the bathtub 3, the time elapsed since the bather P1 started bathing, and the temperature of the bathroom R1 in which the bathtub 3 is installed.

[0142] The sleep time information acquired by the control unit 41 is the information calculated by the sleep time information calculation unit 31B of the smart watch 30. The sleep time information calculation unit 31B extracts the sleep time from the life log of a past predetermined period (the past six months) stored in the storage unit 31M, and calculates the average value of the sleep time as the sleep time information of the subject P1A (bather P1).

[0143] The control unit 41 executes the deep body temperature estimation program shown in FIGS. 3 and 4. In step S116, the above-described estimation formula is constructed using such bath information and sleep time information as parameters, and the [estimated value Y of the deep body temperature] is calculated.

[0144] Here, when comparing the case where the daily sleep time tends to be long and the case where it tends to be short, it is considered that the autonomic nervous system (the balance between the sympathetic nerve and the parasympathetic nerve) is more likely to be adjusted in the case where it tends to be long, and the blood flow rate can be adjusted smoothly. For this reason, a person whose daily sleep time tends to be long tends to have a large blood flow rate in a hot environment.

[0145] In addition, since the hot water in which the bather P1 soaking during the bath stored in the bathtub 3 has a temperature higher than the body temperature of the bather P1, when a person whose daily sleep time tends to be long takes a bath, it is considered that the blood flow rate of the bather P1 is likely to increase. And, since the hot water stored in the bathtub 3 has a larger heat capacity than air, the heat exchange between the body surface of the bather P1 and the hot water is promoted, and the heat transmitted from the hot water to the blood in the body via the body surface is likely to move to the deep part of the body by blood circulation. As a result, it is considered that the deep body temperature is likely to rise.

[0146] Thus, since there is considered to be a correlation between the deep body temperature during the bath and the sleep time, the sleep time information is suitable as a parameter for estimating the deep body temperature during the bath.

[0147] Therefore, the deep body temperature estimation device 1 of the first embodiment can accurately estimate the deep body temperature of the bather P1 during the bath.

[0148] In addition, in this deep body temperature estimation device 1, the sleep time information is information based on the average value of the sleep time in a past predetermined period (the past six months). By using such specific sleep time information, the deep body temperature of the bather P1 during bathing can be estimated with higher accuracy.

[0149] Furthermore, in this deep body temperature estimation device 1, the smartwatch 30 stores the sleep record of the subject P1A (bather P1) and calculates the sleep time information. Then, in step S101 shown in FIG. 3, the control unit 41 acquires the sleep time information of the subject P1A (bather P1) stored in the storage unit 31M of the smartwatch 30 from the smartwatch 30 via the communication unit 49, or acquires the sleep time information uploaded to the external server 9 from the external server 9 via the communication unit 49. With this configuration, the control unit 41 can use the sleep time information of the subject P1A (bather P1) updated by the smartwatch 30 as a parameter for the deep body temperature estimation process. As a result, even if the subject P1A (bather P1) does not perform a special operation for the control unit 41 to acquire the sleep time information, such as an input operation of the sleep record, etc., on the deep body temperature estimation device 1, the control unit 41 can execute the deep body temperature estimation process, thus realizing an improvement in convenience.

[0150] Also, in this deep body temperature estimation device 1, as shown in FIG. 2, the biological information detection means 50 includes a heart rate detection means 50A, a blood flow detection means 50B, and a sweating amount detection means 50C. With this configuration, since the biological information used as a parameter for the control unit 41 to estimate the deep body temperature includes the heart rate, blood flow, and sweating amount of the bather P1, the deep body temperature of the bather P1 during bathing can be estimated with higher accuracy.

[0151] 1 and 2, the bathing condition information detection means 60 includes a water temperature detection means 60A, a bathing time elapsed detection means 60B, and a bathroom temperature detection means 60C. With this configuration, the bathing condition information used as parameters by the control unit 41 to estimate the deep body temperature includes the temperature of the water stored in the bathtub 3, the bathing time elapsed, and the temperature of the bathroom R1, allowing for more accurate estimation of the deep body temperature of the bather P1 while bathing.

[0152] Furthermore, in this deep body temperature estimation device 1, the control unit 41 can acquire exercise habit information quantified based on the past exercise history of the subject P1A (bather P1). Then, in step S116 shown in FIG. 4, the control unit 41 executes a deep body temperature estimation process using bathing information, sleep time information, and exercise habit information as parameters. People who exercise regularly tend to have higher vasodilatory functions and higher blood flow rates than people who do not, because they dissipate body heat generated by exercise. Therefore, people who exercise regularly tend to have higher blood flow rates in hot environments. Because the temperature of the water stored in the bathtub 3 in which the bather P1 is immersed during bathing is higher than the body temperature of the bather P1, it is thought that the bather P1's blood flow is likely to be higher when bathing if the bather P1 exercises regularly. Furthermore, because the hot water stored in the bathtub 3 has a greater heat capacity than air, heat exchange between the bather P1's body surface and the hot water is promoted, and heat transferred from the hot water to the body's blood via the body surface is more likely to move deeper into the body through blood circulation, resulting in a rise in core body temperature. Thus, since there is a correlation between core body temperature during bathing and exercise habits, exercise habit information is suitable as a parameter for estimating core body temperature during bathing. Therefore, this core body temperature estimation device can more accurately estimate the bather's core body temperature while bathing.

[0153] Furthermore, in this deep body temperature estimation device 1, the smartwatch 30 stores the exercise achievements of the subject P1A (bather P1) and calculates exercise habit information. Then, in step S101 shown in FIG. 3, the control unit 41 acquires the exercise habit information of the subject P1A (bather P1) stored in the storage unit 31M of the smartwatch 30 from the smartwatch 30 via the communication unit 49, or acquires the exercise habit information uploaded to the external server 9 from the external server 9 via the communication unit 49. With this configuration, the control unit 41 can use the exercise habit information of the subject P1A (bather P1) updated by the smartwatch 30 as a parameter for the deep body temperature estimation process. Thereby, even if the subject P1A (bather P1) does not perform a special operation for causing the control unit 41 to acquire the exercise habit information, such as an input operation of exercise achievements, etc., on the deep body temperature estimation device 1, the control unit 41 can execute the deep body temperature estimation process, so that the convenience can be improved.

[0154] (Example 2) In the deep body temperature estimation device of Example 2, as part of the life log function, the smartwatch 30 detects the sleep time of one day by dividing it into non-REM sleep time and REM sleep time, and the storage unit 31M stores at least the non-REM sleep time among the sleep time, non-REM sleep time, and REM sleep time as the sleep achievement of the subject P1A (bather P1).

[0155] The non-REM sleep time is the time of non-REM sleep, which means being sleepy but in a deep state. The REM sleep time is the time of REM sleep, which means being sleepy but in a shallow state.

[0156] Then, the sleep time information calculation unit 31B of the smartwatch 30 extracts the non-REM sleep time from the life log of a past predetermined period (the past six months) stored in the storage unit 31M, and is changed to calculate the average value of the non-REM sleep time as the sleep time information of the subject P1A (bather P1).

[0157] As a result, the sleep time information set as the parameter x2 of the estimation formula used in step S116 is the average value of the non-REM sleep time in a past predetermined period (the past six months).

[0158] Other configurations of Example 2 are the same as those of Example 1.

[0159] The deep body temperature estimation device of Example 2 having such a configuration can accurately estimate the deep body temperature of the bather P1 during bathing, similarly to the deep body temperature estimation device 1 of Example 1.

[0160] Further, in this deep body temperature estimation device, the smartwatch 30 detects the sleep time of one day by dividing it into non-REM sleep time and REM sleep time, and the storage unit 31M stores at least the non-REM sleep time as the sleep record of the subject P1A (the bather P1). Then, the sleep time information calculation unit 31B calculates the average value of the non-REM sleep time as the sleep time information of the subject P1A (the bather P1). It is considered that there is a correlation between the non-REM sleep time and the degree to which the autonomic nerves are regulated (the balance between the sympathetic nerve and the parasympathetic nerve). Therefore, by configuring the smartwatch 30 as described above, sleep time information effective for estimating the deep body temperature during bathing can be calculated.

[0161] In particular, when the sleep time of one day is divided into non-REM sleep time and REM sleep time, it is considered that the autonomic nerves (the balance between the sympathetic nerve and the parasympathetic nerve) are more easily regulated when the non-REM sleep time tends to be long (when the REM sleep time tends to be short), and the adjustment of blood flow can be performed smoothly. Therefore, by the storage unit 31M of the smartwatch 30 storing the non-REM sleep time as the sleep record and the sleep time information calculation unit 31B calculating the average value of the non-REM sleep time as the sleep time information, the deep body temperature of the bather P1 during bathing can be estimated more accurately.

[0162] (Example 3) In the deep body temperature estimation device of Example 3, as part of its life log function, the smart watch 30 detects the daily sleep time by dividing it into non-REM sleep time and REM sleep time, and the memory unit 31M is modified to store at least the REM sleep time among the sleep time, non-REM sleep time and REM sleep time as the sleep history of the subject P1A (bather P1).

[0163] The sleep time information calculation unit 31B of the smart watch 30 is modified to extract REM sleep time from the life log for a predetermined period of time (the past six months) stored in the memory unit 31M, and calculate the average value of REM sleep time as the sleep time information of the subject P1A (bather P1).

[0164] As a result, the sleep time information set as parameter x2 of the estimation formula used in step S116 is the average value of the REM sleep time over the past predetermined period (past six months).

[0165] Other configurations of the third embodiment are the same as those of the first embodiment.

[0166] The deep body temperature estimation device of the third embodiment configured as described above can accurately estimate the deep body temperature of the bather P1 while bathing, similar to the deep body temperature estimation devices 1 of the first and second embodiments.

[0167] Furthermore, it is believed that there is a correlation between REM sleep time and the degree of autonomic nervous system regulation (the degree of balance between the sympathetic and parasympathetic nervous systems). Therefore, by configuring the smart watch 30 as described above, this deep body temperature estimation device can calculate sleep time information that is effective for estimating deep body temperature during bathing.

[0168] Example 4 In the deep body temperature estimation device of Example 4, as part of its life log function, the smart watch 30 detects and divides the daily sleep time into non-REM sleep time and REM sleep time, and the memory unit 31M is modified to store the daily sleep time, non-REM sleep time, and REM sleep time as the sleep history of the subject P1A (bather P1).

[0169] Then, the sleep time information calculation unit 31B of the smartwatch 30 extracts the sleep time and non-REM sleep time from the life log of a past predetermined period (the past six months) stored in the storage unit 31M, and calculates the average value of the ratio of the non-REM sleep time to the sleep time as the sleep time information of the subject P1A (bather P1).

[0170] Note that the sleep time information calculation unit 31B calculates the ratio of the non-REM sleep time to the sleep time for each day of a predetermined period (the past six months), and then calculates the average value of the ratio of the non-REM sleep time to the sleep time.

[0171] As a result, the sleep time information set as the parameter x2 of the estimation formula used in step S116 is the average value of the ratio of the non-REM sleep time to the sleep time in a past predetermined period (the past six months).

[0172] Note that a change in which the sleep time information calculation unit 31B extracts the non-REM sleep time and REM sleep time from the life log of a past predetermined period (the past six months) stored in the storage unit 31M and calculates the average value of the ratio of the non-REM sleep time to the REM sleep time is substantially synonymous with the above change in the sleep time information calculation unit 31B.

[0173] Other configurations of Example 4 are the same as those of Example 1.

[0174] The deep body temperature estimation device of Example 4 having such a configuration can accurately estimate the deep body temperature of the bather P1 during bathing, similar to the deep body temperature estimation device 1 of Examples 1 to 3.

[0175] Also, it is considered that there is a correlation between the non-REM sleep time and the degree to which the autonomic nerve is regulated (the degree to which the balance between the sympathetic nerve and the parasympathetic nerve is regulated). Therefore, by configuring the smartwatch 30 as described above, this deep body temperature estimation device can calculate sleep time information effective for estimating the deep body temperature during bathing.

[0176] (Example 5) In the deep body temperature estimation device of Example 5, the smartwatch 30, as part of the life log function, detects the sleep time of one day by dividing it into non-REM sleep time and REM sleep time, and the storage unit 31M stores at least the sleep time and non-REM sleep time among the sleep time, non-REM sleep time, and REM sleep time as the sleep record of the subject P1A (bather P1).

[0177] Then, the sleep time information calculation unit 31B of the smartwatch 30 extracts the sleep time and non-REM sleep time from the life log of a past predetermined period (the past six months) stored in the storage unit 31M, and calculates the average value of the sleep time and the average value of the non-REM sleep time as the sleep time information of the subject P1A (bather P1).

[0178] Furthermore, in Example 5, as shown below, a parameter is added to the deep body temperature estimation formula according to Example 1 [Estimated value Y of deep body temperature]=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8+β9x9 and it is changed to be used in step S116.

[0179] The settings of parameters x1 to x8 are the same as those in Example 1. That is, in step S106, the sleep time information set as parameter x2 is the average value of the sleep time in a past predetermined period (the past six months).

[0180] Furthermore, in step S106, the average value of the non-REM sleep time in a past predetermined period (the past six months) is set as another sleep time information to parameter x9.

[0181] The other configurations of Example 5 are the same as those of Example 1.

[0182] The deep body temperature estimation device of Example 5 with such a configuration can accurately estimate the deep body temperature of the bather P1 during bathing, similar to the deep body temperature estimation devices 1 of Examples 1 to 4.

[0183] Furthermore, it is believed that there is a correlation between non-REM sleep time and the degree of autonomic nervous system regulation (the degree of balance between the sympathetic and parasympathetic nervous systems). Therefore, by configuring the smart watch 30 as described above, this deep body temperature estimation device can calculate sleep time information that is effective for estimating deep body temperature during bathing, and by using an estimation formula with added parameters, it can estimate the deep body temperature of bather P1 while bathing with even greater accuracy.

[0184] The present invention has been described above in accordance with Examples 1 to 5, but it goes without saying that the present invention is not limited to the above Examples 1 to 5, and can be modified and applied as appropriate within the scope of the invention.

[0185] In Examples 1 to 5, the control unit 41 acquires the exercise habit information of the subject P1A (bathing person P1) and executes the deep body temperature estimation process using the bathing information, sleeping time information, and exercise habit information as parameters, but the present invention is not limited to this configuration. For example, the present invention also includes a configuration in which the control unit 41 does not acquire the exercise habit information of the subject P1A (bathing person P1) and executes the deep body temperature estimation process using the bathing information and sleeping time information as parameters.

[0186] In Example 1, the sleep time information is the average value of sleep time in a predetermined period of time in the past, and in Example 5, one of the two pieces of sleep time information is the average value of sleep time in a predetermined period of time in the past, but the present invention is not limited to this configuration. For example, the sleep time information may be information based on the median value of sleep time in a predetermined period of time in the past, or the number of days during which a person slept for a predetermined period of time or more in the predetermined period of time in the past.

[0187] In Example 2, the sleep time information is the average value of non-REM sleep time in a predetermined period of time in the past, and in Example 5, one of the two pieces of sleep time information is the average value of non-REM sleep time in a predetermined period of time in the past, but the present invention is not limited to this configuration. For example, the sleep time information may be information based on the median value of non-REM sleep time in a predetermined period of time in the past, or the number of days during which non-REM sleep time of a predetermined duration or more was spent in a predetermined period of time in the past.

[0188] In Example 3, the sleep time information is the average value of the REM sleep time in a past predetermined period, but the present invention is not limited to this configuration. For example, the sleep time information may be the median value of the REM sleep time in a past predetermined period, or information based on the number of days with a REM sleep time of a predetermined time or more in a past predetermined period.

[0189] In Examples 1 to 5, the deep body temperature estimation device 1 includes the biological information detection means 50 and the bathing condition information detection means 60 as detection means, but the present invention is not limited to this configuration. For example, a configuration in which the bathing condition information detection means 60 is eliminated from Examples 1 to 5, that is, a configuration in which the hot water temperature, the bathing elapsed time, and the bathroom temperature, which are bathing condition information, are excluded as parameters in the deep body temperature estimation formula, is also included in the present invention. Further, a configuration in which the biological information detection means 50 is eliminated from Examples 1 to 5, that is, a configuration in which the heart rate, blood flow rate, and sweating amount, which are biological information, are excluded as parameters in the deep body temperature estimation formula, is also included in the present invention.

[0190] In Examples 1 to 5, the biological information detection means 50 includes the heart rate detection means 50A, the blood flow rate detection means 50B, and the sweating amount detection means 50C, but the present invention is not limited to this configuration. For example, a configuration in which any one or two of the heart rate detection means 50A, the blood flow rate detection means 50B, and the sweating amount detection means 50C are eliminated from the biological information detection means 50 according to Examples 1 to 5, that is, a configuration in which any one or two of the heart rate, blood flow rate, and sweating amount are excluded as parameters in the deep body temperature estimation formula, is also included in the present invention.

[0191] In Examples 1 to 5, the bathing condition information detection means 60 includes a hot water temperature detection means 60A, a bathing elapsed time detection means 60B, and a bathroom temperature detection means 60C. However, the present invention is not limited to this configuration. For example, a configuration in which any one or two of the hot water temperature detection means 60A, the bathing elapsed time detection means 60B, and the bathroom temperature detection means 60C are eliminated from the bathing condition information detection means 60 according to Examples 1 to 5, that is, a configuration in which any one or two of the hot water temperature, the bathing elapsed time, and the bathroom temperature are excluded as parameters in the deep body temperature estimation formula is also included in the present invention.

[0192] In Examples 1 to 5, the input unit 12B and the display unit 12D of the bathroom remote control 10 also serve as the input unit and the display unit of the deep body temperature estimation device 1. However, the present invention is not limited to this configuration. For example, a configuration in which the deep body temperature estimation device 1 includes a dedicated input unit and a display unit and executes deep body temperature estimation processing independently of the bathroom remote control 10 and the hot water supply device 90 is also included in the present invention. In the deep body temperature estimation device 1 in this case, the control unit 41 acquires the sleep time information and the exercise habit information by inputting (manual input or data transfer via a communication cable) the sleep time information and the exercise habit information to the dedicated input unit, so that the deep body temperature estimation processing can be executed without cooperating with the smart watch 30.

[0193] The main body of the deep body temperature estimation device 1 in Examples 1 to 5 is eliminated, and the control unit 91 of the hot water supply device 90 has the same configuration as the control unit 41 of the deep body temperature estimation device 1. The storage unit 91M stores the deep body temperature estimation program shown in FIGS. 3 and 4. The detection results of the biological information detection means 50 and the bathing condition information detection means 60 are transmitted to the control unit 91 via the bathroom remote control 10, and the control unit 91 executes the deep body temperature estimation program, that is, a configuration in which the control unit 91 of the hot water supply device 90 also serves as the deep body temperature estimation device is also included in the present invention.

[0194] In Examples 1 to 5, the main body of the deep body temperature estimation device 1 is removed, the control unit 11 of the bathroom remote control 10 has the same configuration as the control unit 41 of the deep body temperature estimation device 1, the storage unit 11M stores the deep body temperature estimation program shown in FIGS. 3 and 4, the detection results of the biological information detection means 50 and the bathing condition information detection means 60 are transmitted to the control unit 11, and the control unit 11 executes the deep body temperature estimation program, that is, the configuration in which the control unit 11 of the bathroom remote control 10 also serves as the deep body temperature estimation device is also included in the present invention.

[0195] In Examples 1 to 5, the sleep time information terminal is the smart watch 30, but the present invention is not limited to this configuration. For example, the sleep time information terminal may be a sleep meter installed in the bedroom of the subject P1A (bather P1). As the sleep meter in this case, those laid on the bed mat, those wound around the head or arm, those arranged beside the head, etc. can be used.

[0196] In Examples 1 to 5, the portable information terminal is the smart watch 30, but the present invention is not limited to this configuration. For example, the portable information terminal may be a smart phone, a tablet, a sensor with a strap wound around the chest, etc.

[0197] In Examples 1 to 5, the biological information detection means 50 is a wristband type detection device, but the present invention is not limited to this configuration. For example, the biological information detection means may photograph an image including the face of the bather P1 with a camera and detect the respiration rate or other biological information based on the shape changes of the mouth and nose. Further, the biological information detection means may be a smart watch 30 with improved waterproofness and heat resistance, and may be worn on the arm of the bather P1 in the bathroom R1 instead of the biological information detection means 50.

[0198] In Examples 1 to 5, the hot water temperature detection means 60A has a hot water temperature sensor installed on the side wall surface of the bathtub 3, but the present invention is not limited to this configuration. For example, a configuration in which the hot water temperature detection means 60A is removed from Examples 1 to 5 and the hot water temperature sensor 92 of the hot water supply device 90 is used as the hot water temperature detection means is also included in the present invention.

[0199] In Examples 1 to 5, bathing elapsed time detection means 60B has a pressure sensor and a timing means installed on the side wall surface of bathtub 3, but the present invention is not limited to this configuration. For example, the present invention also includes a configuration in which bathing elapsed time detection means 60B is eliminated from Examples 1 to 5 and the water level sensor 93 and internal clock of water heater 90 are used as bathing elapsed time detection means.

[0200] In Examples 1 to 5, the control unit 41 receives an input to select a subject for the core body temperature estimation process in step S104 shown in Fig. 3, but the present invention is not limited to this configuration. For example, the control unit 41 may identify the bather P1 to be the subject of the current core body temperature estimation process from among the registered subjects P1A (bathers P1) by photographing the bather P1 with a camera installed in the bathroom R1 and performing image analysis. [Industrial Applicability]

[0201] The present invention can be used, for example, in homes, facilities, etc. that have bathrooms installed. [Explanation of symbols]

[0202] 1…Central body temperature estimation device 3...Bathtub P1: Bather 41...Control unit 50, 60...detection means (50...biological information detection means, 60...bathing condition information detection means) 30...Sleep time information terminal (smart watch) 9...External server 49…Communications Department 50A...Heart rate detection means 50B...Blood flow rate detection means 50C...Sweat amount detection means 60A...Water temperature detection means 60B...Bath elapsed time detection means 60C...Bathroom temperature detection means R1...Bathroom 30...Mobile information terminal (smart watch)

Claims

1. A control unit that executes a deep body temperature estimation process for estimating the deep body temperature of a bather taking a bath in a bathtub, Detection means for detecting the bathing information regarding the bather taking a bath, which is the digitized bathing information, A deep body temperature estimation device comprising: The detection means Biological information detection means for detecting biological information including at least the vital state of the bather as the bathing information, Bathing condition information detection means for detecting bathing condition information regarding the conditions during the bath of the bather as the bathing information, having at least one of The control unit is capable of acquiring sleep time information digitized based on the past sleep performance of the bather, The control unit executes the deep body temperature estimation process using the bathing information and the sleep time information as parameters. A deep body temperature estimation device characterized by this.

2. [[ID= ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ The deep body temperature estimation device according to any one of claims 1 to 5, having at least one of them.

7. The detection means includes at least the bathing condition information detection means, The bathing condition information detection means includes a hot water temperature detection means for detecting the temperature of the hot water stored in the bathtub, a bathing elapsed time detection means for detecting the elapsed time since the bather started bathing, a bathroom temperature detection means for detecting the temperature of the bathroom in which the bathtub is installed, The deep body temperature estimation device according to any one of claims 1 to 5, having at least one of them.

8. The control unit can acquire quantified exercise habit information based on the past exercise performance of the bather, The deep body temperature estimation device according to any one of claims 1 to 5, wherein the control unit executes the deep body temperature estimation process using the bathing information, the sleep time information, and the exercise habit information as parameters.

9. A portable information terminal that is routinely carried by the bather, the portable information terminal that stores the exercise performance of the bather and calculates the exercise habit information, and an external server that acquires the exercise habit information from the portable information terminal by network communication, further comprising a communication unit capable of communicating with at least one of them, The deep body temperature estimation device according to claim 8, wherein the control unit acquires the exercise habit information via the communication unit.

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