Estimation device, estimation method, and estimation program

Abstract

To provide an estimation device, an estimation method and an estimation program capable of estimating a body temperature with high accuracy.SOLUTION: An estimation device according to the present disclosure includes an acquisition unit that acquires a skin temperature measured by a sensor in a state of being in contact with skin and an external temperature that is a temperature on an outside air side, and a determination unit that determines whether or not the sensor is lifted from the skin on the basis of a reference at which the sensor is lifted from the skin, the reference being set on the basis of the acquired skin temperature and external temperature and a coefficient of thermal conductivity between the skin and a measurement device including the sensor. A removal unit configured to remove a skin temperature measured by the sensor determined to be floating from the skin and a floating temperature which is an external temperature corresponding to the skin temperature, and an estimation unit configured to estimate a deep body temperature based on the skin temperature from which the floating temperature is removed and the external temperature.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to an estimation device, an estimation method, and an estimation program. [Background technology]

[0002] Accurate estimation of body temperature enables accurate prediction of menstrual cycles and detection of illnesses based on changes in physical condition during sleep. Furthermore, accurate prediction of menstrual cycles and detection of illnesses respectively enable improved pregnancy success rates and appropriate physical condition management. For this reason, accurate estimation of body temperature is desirable.

[0003] One known technique for estimating body temperature is to correct the skin temperature and estimate the corrected skin temperature as the body temperature (see Patent Document 1 below). This technique changes the coefficient of thermal conductivity between the skin and a measuring device equipped with a sensor according to the amount of change in the temperature difference between the skin temperature measured by a sensor in contact with the skin and the external temperature, which is the temperature of the outside air. This technique then corrects the skin temperature based on the changed coefficient, and estimates the corrected skin temperature as the body temperature. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6957402 Summary of the Invention [Problem to be solved by the invention]

[0005] With conventional technology, if the sensor floats off the skin while measuring skin temperature, the thermal conductivity coefficient is changed according to the amount of change in the temperature difference between the skin temperature and the external temperature, and the skin temperature is corrected based on the changed coefficient, thereby enabling accurate estimation of body temperature.

[0006] On the other hand, it is desirable to be able to accurately estimate body temperature even when the temperature difference between the skin temperature and the external temperature does not change significantly even though the sensor is lifted from the skin. For example, it is desirable to be able to accurately estimate body temperature even when the sensor is lifted from the skin immediately after measuring the skin temperature, or when the temperature difference between the skin temperature and the external temperature remains stable while the sensor remains lifted from the skin. For this reason, there is room for improvement in the accuracy of body temperature estimation in conventional techniques.

[0007] Therefore, the present disclosure proposes an estimation device, an estimation method, and an estimation program that enable highly accurate estimation of body temperature. [Means for solving the problem]

[0008] In order to solve the above problems, the estimation device of the present disclosure is characterized by comprising an acquisition unit that acquires the skin temperature measured by the sensor while in contact with the skin and the external temperature, which is the temperature on the outside air side; a judgment unit that determines whether the sensor has floated off the skin based on the acquired skin temperature and external temperature and a criterion for the sensor to float off the skin, which is set based on the coefficient of thermal conductivity between the skin and the measuring device equipped with the sensor; a removal unit that removes the skin temperature measured by the sensor that has been determined to have floated off the skin and the floatation temperature, which is the external temperature corresponding to the skin temperature, from the acquired skin temperature and external temperature; and an estimation unit that estimates the core body temperature based on the skin temperature and external temperature from which the floatation temperature has been removed. [Effects of the Invention]

[0009] According to one aspect of the embodiment, it is possible to estimate body temperature with high accuracy. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 10 is a diagram illustrating a flow of an estimation process according to the embodiment. [Figure 2] FIG. 1 is a diagram illustrating an example of the configuration of an estimation device according to an embodiment. [Figure 3] FIG. 4 is a diagram illustrating an example of a representative temperature data storage unit according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a physiological event data storage unit according to the embodiment. [Figure 5] FIG. 10 is a diagram illustrating an example of a setting process according to the embodiment. [Figure 6] 1 is a graph showing an example of the relationship between skin temperature and external temperature. [Figure 7] 1 is a graph showing an example of the relationship between skin temperature and external temperature. [Figure 8] 1 is a graph showing an example of the relationship between skin temperature and external temperature. [Figure 9] 10 is a flowchart illustrating a procedure of a setting process according to the embodiment. [Figure 10] 10 is a flowchart illustrating a procedure of an estimation process according to the embodiment. [Figure 11] 10 is a flowchart illustrating a procedure of a removal process according to the embodiment. [Figure 12] FIG. 2 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the estimation device. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the following embodiments, the same components are designated by the same reference numerals, and redundant description will be omitted.

[0012] (1. Embodiment) (1-1. Overview of Estimation Processing According to the Embodiment) 1 is a diagram schematically illustrating the flow of an estimation process according to an embodiment. The estimation process according to the embodiment is executed by an estimation system 1. The estimation system 1 includes an estimation device 100, which is an example of an estimation device according to the present disclosure, a measuring instrument 10, and a user terminal 200. The devices included in the estimation system 1 can transmit and receive data to and from each other via wireless communication or the like.

[0013] The measuring device 10 is a device used to measure body temperature. The measuring device 10 is, for example, a wearable device. For example, the measuring device 10 is stored in clothing such as underwear of the user 20. The measuring device 10 is also placed so as to come into contact with the torso of the user 20.

[0014] The measuring device 10 can be realized as multiple devices depending on the data to be measured. For example, the measuring device 10 can be realized as multiple sensors that measure multiple pieces of data used by the estimation device 100 to estimate body temperature.

[0015] Specifically, the measuring device 10 is composed of a skin temperature sensor that measures the skin temperature used to estimate the core body temperature by the estimation device 100, and an external temperature sensor that measures the external temperature, which is the temperature of the outside air used for the estimation.

[0016] Core body temperature is the body temperature estimated based on the skin temperature and external temperature measured by these sensors. Core body temperature is also the body temperature used to calculate representative temperatures throughout the day, such as basal body temperature.

[0017] Basal body temperature is the body temperature when one is at rest and only consumes the minimum amount of energy necessary to sustain life. For example, basal body temperature refers to the temperature measured while lying down upon waking in the morning using a basal thermometer, which is commonly used to track women's menstrual cycles.

[0018] The representative temperature is a typical temperature for a day. For example, the representative temperature is a body temperature used to predict the day of ovulation, the day of menstruation, or the menstrual cycle. The external temperature is, for example, the temperature inside the clothing of the user 20 in which the measuring device 10 is stored.

[0019] To measure a stable body temperature, the skin temperature sensor continuously measures the skin temperature while in contact with the skin of the trunk, such as the chest, of the user 20, rather than the extremities, such as the fingers or arms. The skin temperature sensor also stores the measured skin temperature as skin temperature data.

[0020] The external temperature sensor continuously measures the external temperature and stores the measured external temperature as external temperature data.

[0021] The user terminal 200 is a terminal device used by the user 20. The user terminal 200 is, for example, a smartphone or a tablet terminal. An app for controlling the measuring device 10 is installed in the user terminal 200. By using the app running on the user terminal 200, the user 20 can control the measuring device 10 and refer to the skin temperature data and external temperature data measured by the measuring device 10.

[0022] The user terminal 200 acquires the skin temperature data and external temperature data measured by the measuring device 10 and transmits them to the estimation device 100. The user terminal 200 also receives the core body temperature, which is the estimation result by the estimation device 100, and the prediction results, such as the ovulation date, menstrual date, and menstrual cycle, from the estimation device 100. The user 20 references the estimation results and prediction results by the estimation device 100 via the user terminal 200.

[0023] The estimation device 100 is an information processing device that executes an estimation process to estimate core body temperature based on skin temperature data and external temperature data measured by the measuring device 10, and a prediction process to predict the ovulation date, menstrual date, menstrual cycle, etc. For example, the estimation device 100 is a cloud server wirelessly connected to the Internet.

[0024] 1, there may be many other users in addition to user 20 who use measuring device 10. Estimation device 100 continuously acquires skin temperature data and external temperature data from many users and statistically processes the data.

[0025] The estimation device 100 predicts the menstrual cycle by statistically processing the skin temperature data and the external temperature data. The menstrual cycle is cyclical (biphasic), in that it enters a high temperature period due to the influence of hormones secreted with ovulation, and then enters a low temperature period after the start of menstruation (monthly period). Therefore, the statistical processing described above makes it possible to predict the pattern of the menstrual cycle to some extent.

[0026] Basal body temperature is generally used to predict the menstrual cycle. However, measuring basal body temperature accurately can be difficult. For example, when measuring basal body temperature using a basal thermometer, a woman is required to remain motionless immediately after waking up. However, if the location or time of waking up varies greatly (for example, if a woman wakes up in the bathroom before dawn), or if there is an error in the measurement, basal body temperature will vary.

[0027] Therefore, it is conceivable to estimate the deep body temperature required for calculating the basal body temperature by measuring the skin temperature and external temperature of the woman while she sleeps using a wearable device such as measuring device 10 that is attached to her body. However, even in this case, if the skin temperature sensor of the wearable device floats above the skin due to the woman's sleeping position, the deep body temperature may be estimated to be lower than the actual temperature. Furthermore, incorporating a means to detect whether the skin temperature sensor is in contact with the skin or not increases costs.

[0028] For the above reasons, in order for the estimation device 100 to obtain a highly accurate basal body temperature, it is necessary for the estimation device 100 to estimate the core body temperature based on the skin temperature measured by a skin temperature sensor that is not floating above the skin. Therefore, the estimation device 100 according to the embodiment solves the problem of estimating a highly accurate body temperature by the following estimation process.

[0029] The estimation device 100 acquires the skin temperature measured by the skin temperature sensor while in contact with the skin, and the external temperature, which is the temperature on the outside air side. The estimation device 100 determines whether the skin temperature sensor has floated off the skin based on the acquired skin temperature and external temperature, and a criterion for the skin temperature sensor to float off the skin, which is set based on the coefficient of thermal conductivity between the skin and the measuring device 10. The estimation device 100 removes the skin temperature measured by the skin temperature sensor determined to have floated off the skin and the float temperature, which is the external temperature corresponding to that skin temperature, from the acquired skin temperature and external temperature. The estimation device 100 then estimates the core body temperature based on the skin temperature and external temperature from which the float temperature has been removed.

[0030] The criteria for whether the skin temperature sensor floats above the skin are not particularly limited, as long as they are set based on the coefficient of thermal conductivity between the skin and the measuring device 10. The criteria may be, for example, whether or not an inequality of a linear function that indicates the numerical relationship between the skin temperature and the external temperature, including the coefficient of thermal conductivity, is satisfied.

[0031] An example of the estimation process performed by the estimation device 100 will be described below with reference to Fig. 1. In step S1, the measuring device 10 continuously measures skin temperature data and external temperature data every day while in contact with the sleeping user 20. For example, the measuring device 10 continuously measures the skin temperature data and external temperature data every predetermined time (e.g., every minute) every day.

[0032] In step S2, the measuring device 10 sequentially or periodically transmits the measured skin temperature data and external temperature data to the user terminal 200. The user terminal 200 receives the skin temperature data and external temperature data measured by the measuring device 10 from the measuring device 10.

[0033] In step S3, the user terminal 200 transmits the skin temperature data and external temperature data measured by the measuring device 10 to the estimation device 100 sequentially or periodically.

[0034] In step S4, the estimation device 100 receives the skin temperature data and external temperature data measured by the measuring device 10 from the user terminal 200, thereby acquiring the skin temperature data and external temperature data measured by the measuring device 10.

[0035] The estimation device 100 stores the acquired skin temperature data and external temperature data (measurement data by the measuring device 10) in the database 50. The estimation device 100 also stores previously acquired skin temperature data and physiological information data related to past menstrual dates and menstrual cycles for each user in the database 50. The physiological information data includes various information that is expected to affect menstruation, such as the date of menstruation, the number of days in the menstrual cycle, the number of days in the low temperature period and the high temperature period, and events registered by the user 20 (drinking alcohol, catching a cold, exercising, etc.).

[0036] Next, the estimation device 100 determines whether the skin temperature sensor has floated off the skin based on the acquired skin temperature data and external temperature data, as well as a criterion for the skin temperature sensor to float off the skin, which is set based on the coefficient of thermal conductivity between the skin and the measuring device 10.

[0037] The estimation device 100 can also set a criterion for the skin temperature sensor to float from the skin in advance, for example, before steps S1 to S5. In this case, the estimation device 100 first calculates the coefficient K of thermal conductivity in the following equation (1) using the thermal resistance Rs from a deep part of the trunk, such as the chest, of the user 20 to the skin and the thermal resistance Rd of the entire measuring device 10.

[0038]

number

[0039] The estimation device 100 may calculate the coefficient K of thermal conductivity from the measurement results of each subject, or may set a predetermined value obtained in advance from the measurement results of multiple subjects as the initial value of the coefficient K of thermal conductivity.

[0040] After calculating the coefficient K of thermal conductivity, the estimation device 100 sets the criterion for the skin temperature sensor to float above the skin based on the following linear function equation (2) which shows the numerical relationship between the skin temperature Ts, the external temperature Ta, and the core body temperature Tb, including the coefficient K. Transforming equation (2) yields the following equation (3):

[0041]

number

[0042] For example, the estimation device 100 sets as a reference the inequality of the linear function in the following equation (4), in which the skin temperature Ts is equal to or less than the right-hand side when the slope (K / (K+1)) of the linear function in equation (3) is replaced with a slope α1 and the intercept Tb / (K+1) with an intercept b1 when Ta is the horizontal axis and Ts is the vertical axis. In this case, the appropriate slope α1 and intercept b1 of equation (4) vary depending on the thermal resistance Rd of the measuring device 10 and the contact area between the measuring device 10 and the skin, so the estimation device 100 sets the slope α1 and intercept b1 based on theory and prior evaluation.

[0043]

number

[0044] Return to step S4. For example, the estimation device 100 determines whether the skin temperature sensor has risen above the skin based on whether the acquired skin temperature and external temperature satisfy a relational expression such as an inequality of a linear function that shows the numerical relationship between the skin temperature and the external temperature, as in equation (4). Specifically, the estimation device 100 determines that the skin temperature sensor has risen above the skin when the acquired skin temperature and external temperature satisfy the inequality of equation (4).

[0045] Next, the estimation device 100 removes, from the acquired skin temperature data and external temperature data, the skin temperature data measured by the skin temperature sensor determined to have floated off the skin and the floating temperature data, which is the external temperature data corresponding to the skin temperature data. For example, the estimation device 100 removes, from the acquired skin temperature data and external temperature data, the floating temperature data that satisfies the above-mentioned formula (4).

[0046] Next, the estimation device 100 estimates the deep body temperature based on the skin temperature data and external temperature data from which the float temperature data has been removed. For example, the estimation device 100 estimates the deep body temperature Tb of the user 20 for each predetermined time period by substituting the skin temperature Ts from which the float temperature has been removed and the external temperature Ta corresponding to the skin temperature Ts, among the skin temperatures and external temperatures acquired for each predetermined time period, into the above-mentioned equation (2).

[0047] Next, the estimation device 100 calculates a representative temperature, which is a temperature treated as the deep body temperature (basal body temperature) for each date, based on the estimated deep body temperature. For example, the estimation device 100 calculates the average or median of the deep body temperature over a predetermined time range as the representative temperature data.

[0048] The specified time range may be, for example, six hours, which is the typical sleeping time, five minutes before and after the time period corresponding to the basal heart rate, which is the heart rate used to identify a stable state in the human body such as deep sleep, a range of up to one hour centered on the time corresponding to the basal heart rate, or any time in between.

[0049] The representative temperature data calculated by the estimation device 100 is shown as a waveform by plotting the body temperature value and the date of measurement, as in graph 30 in Fig. 1. If the user 20 is able to recognize the day of menstruation, the user 20 can include the date of menstruation in the representative temperature data by registering data indicating when the day of menstruation occurred.

[0050] Graph 30 represents temperature data for approximately one menstrual cycle of user 20. Graph 30 also represents data that exhibits a biphasic tendency, with the first half representing a low temperature period and the second half representing a high temperature period.

[0051] Next, the estimation device 100 predicts the ovulation date, menstrual date, menstrual cycle, etc. based on the calculated representative temperature and physiological item data. For example, the estimation device 100 predicts the next ovulation date, menstrual date, and menstrual cycle of the user 20 at a stage when it becomes possible to predict the next ovulation date, menstrual date, and menstrual cycle of the user 20 based on the representative temperature and physiological item data accumulated over a predetermined period.

[0052] In step S5, the estimation device 100 transmits the estimated core body temperature and the predicted results such as the ovulation date, menstrual date, and menstrual cycle to the user terminal 200. The user terminal 200 receives the estimated and predicted results from the estimation device 100.

[0053] As described above, the estimation device 100 determines whether the skin temperature sensor has floated off the skin based on the skin temperature, external temperature, and float criteria, removes the float temperature, and then estimates the deep body temperature based on the skin temperature and external temperature from which the float temperature has been removed. As a result, even if the sensor floats off the skin immediately after measuring the skin temperature, the estimation device 100 can accurately estimate the deep body temperature based on the skin temperature and external temperature from which the float temperature has been removed, and can therefore obtain the deep body temperature as a highly accurate basal body temperature.

[0054] (1-2. Configuration of Estimation Device According to Embodiment) Next, a description will be given of an example of the configuration of the estimation device 100 according to the embodiment. Fig. 2 is a diagram showing an example of the configuration of the estimation device 100 according to the embodiment.

[0055] 2, the estimation device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The estimation device 100 may also include an input unit (e.g., a keyboard, a mouse, etc.) that accepts various operations from an administrator or the like who manages the estimation device 100, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

[0056] The communication unit 110 is realized by, for example, a network interface controller, etc. The communication unit 110 is connected to a network N (for example, the Internet) by wire or wirelessly, and transmits and receives information to and from the measuring device 10, the user terminal 200, etc. via the network N. For example, the communication unit 110 transmits and receives information using a communication standard or communication technology such as Wi-Fi (registered trademark), SIM (Subscriber Identity Module), or LPWA (Low Power Wide Area).

[0057] The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a measurement data storage unit 121, a representative temperature data storage unit 122, and a physiological event data storage unit 123.

[0058] The measurement data storage unit 121 stores the measurement data measured by the measuring device 10. For example, the measurement data storage unit 121 stores skin temperature data and external temperature data in a time-related linked state.

[0059] Next, an example of the representative temperature data storage unit 122 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the representative temperature data storage unit according to an embodiment. As shown in Fig. 3, the representative temperature data storage unit 122 has items such as a user ID, measurement date and time, and representative temperature.

[0060] The user ID is identification information for identifying the user 20 or the measuring device 10 and user terminal 200 used by the user 20. The measurement date and time indicates the date and time when the body temperature of the user 20 was measured. The representative temperature indicates data of the representative temperature calculated on the date corresponding to the measurement date and time.

[0061] That is, the estimation device 100 calculates the representative temperature of the user 20 based on the information stored in the measurement data storage unit 121, and then stores the calculated representative temperature in the representative temperature data storage unit 122. The estimation device 100 can predict the date of ovulation, the date of the next menstruation, etc. by plotting the representative temperature for each date.

[0062] Next, an example of the physiological information data storage unit 123 will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the physiological information data storage unit according to the embodiment. As shown in Fig. 4, the physiological information data storage unit 123 has items such as a user ID, cycle data, average number of days in a low temperature period, and average number of days in a high temperature period. In Fig. 4, some of the data stored in the physiological information data storage unit 123 is conceptually shown as B01, etc. However, in reality, various data, which will be described later, is stored.

[0063] The user ID corresponds to the same item as in Fig. 3. The cycle data is data related to the menstrual cycle of the user 20. For example, the cycle data is various data such as the past menstrual dates and cycles of the user 20, and representative temperatures. The cycle data may be a prediction result by the estimation device 100, or may be data such as menstrual dates and cycles registered by the user 20 themselves.

[0064] The average number of days in the low temperature period indicates the average number of days in the low temperature period of the user 20 during the menstrual cycle. The average number of days in the high temperature period indicates the average number of days in the high temperature period of the user 20 during the menstrual cycle. When the date of the changeover point of the low temperature period or the high temperature period is determined by observing the representative temperature, the estimation device 100 can predict the next menstrual date or ovulation date using the average number of days in the low temperature period or the average number of days in the high temperature period of the user 20.

[0065] Returning to the explanation of Fig. 2, the control unit 130 is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), or the like executing a program stored inside the estimation device 100 using a RAM or the like as a work area. The control unit 130 is also a controller. The control unit 130 is realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0066] As shown in FIG. 2, the control unit 130 includes a setting unit 131, an acquisition unit 132, a determination unit 133, a removal unit 134, an estimation unit 135, a calculation unit 136, and a prediction unit 137.

[0067] The setting unit 131 sets a criterion for the skin temperature sensor to float from the skin based on the coefficient of thermal conductivity between the skin and the measuring device 10. An example of the setting process will be described below with reference to FIG. 5. FIG. 5 is a diagram for explaining an example of the setting process according to the embodiment. FIG. 5 is also a diagram schematically illustrating the torso of a user 20 wearing the measuring device 10, as viewed from the side.

[0068] First, the setting unit 131 calculates the coefficient of thermal conductivity between the skin and the measuring device 10. FIG. 5 shows variables used by the setting unit 131 when calculating this coefficient. The measurement mode 40 in FIG. 5 illustrates the relationship between the close contact of the measuring device 10 with the skin of the user 20, the position where the measuring device 10 measures the temperature, and the flow of the heat flow I from the deep part of the user 20 to the outside air, based on the idea of ​​a heat flow I from the deep part of the user 20 to the outside air. A schematic diagram 41 also shows the flow of the heat flow I shown in the measurement mode 40.

[0069] Heat flow I indicates the flow of heat that is transmitted to the skin temperature sensor in contact with the skin surface 45, then transmitted throughout the entire measuring device 10, and then exits from the measuring device 10 to the outside air. Heat flow I is expressed as in the following equation (5). Rs in equation (5) is the thermal resistance from the deep part of the trunk of the user 20, such as the chest, to the skin. Rd is the thermal resistance of the measuring device 10 itself. Furthermore, deep body temperature Tb is expressed as in the following equation (6), which is a modification of the above equation (5).

[0070]

number

[0071] The coefficient K of thermal conductivity in the above formula (6) is expressed as in the above formula (1). The setting unit 131 can determine the coefficient K of thermal conductivity by any means. In the case of thermal resistance Rs, the setting unit 131 can determine it from general values, such as a skin thickness of 5 mm and a thermal conductivity of skin of approximately 0.4 [W / m K]. However, the value of thermal resistance Rd varies greatly depending on the shape of the measuring device 10. Therefore, the setting unit 131 determines the coefficient K of thermal conductivity, for example, by experiment.

[0072] As one example, the setting unit 131 determines a predetermined value that takes into account individual differences as the coefficient K of thermal conductivity based on the measurement results of multiple subjects. As another example, the setting unit 131 determines the coefficient K of thermal conductivity through an experiment using a heater or the like that simulates skin. As another example, the coefficient K of thermal conductivity is determined based on measurement results for each subject over several days.

[0073] When determining the coefficient K of thermal conductivity based on the measurement results for each subject, the setting unit 131 uses, for example, the above-mentioned formula (2), which corresponds to the right-hand side of the above-mentioned formula (6). As described above, formula (2) can be transformed to the above-mentioned formula (3). In formula (3), when the horizontal axis is the external temperature Ta and the vertical axis is the skin temperature Ts, the slope of the linear function is as shown in the following formula (7). Furthermore, formula (7) can be transformed to the following formula (8).

[0074]

number

[0075] The setting unit 131 calculates the slope α1 of the above equation (7) from a graph in which the measurement results, external temperature Ta and skin temperature Ts, are plotted on the horizontal and vertical axes, respectively, and then substitutes the slope α1 into the above equation (8), thereby obtaining the thermal conductivity coefficient K.

[0076] As described above, when the setting unit 131 determines the coefficient K of thermal conductivity from actual data of a subject, such as measurement results for each subject, the setting unit 131 may determine the coefficient K of thermal conductivity that also includes the case where the skin temperature sensor is floating above the skin.

[0077] For this reason, the setting unit 131 can also remove irregular external temperatures Ta and skin temperatures Ts that deviate by a predetermined distance or more from a straight line derived from the temperature data plotted on a graph. Furthermore, the setting unit 131 can also calculate the coefficient K of thermal conductivity in a state where the skin temperature sensor is fixed with tape or the like so that it does not float off the skin.

[0078] Furthermore, when the change in the measured external temperatures Ta or skin temperatures Ts is smaller than a predetermined temperature, for example, less than 0.2°C, the setting unit 131 cannot determine the slope α1 of the straight line from the external temperatures Ta and skin temperatures Ts plotted on the graph, making it difficult to calculate the thermal conductivity coefficient K.

[0079] Therefore, the setting unit 131 can determine the coefficient K of thermal conductivity when the temperature changes more than a predetermined temperature, or can determine the coefficient K of thermal conductivity by changing the external temperature, for example.

[0080] However, when calculating the coefficient K of thermal conductivity for each measurement day of the subject, the setting unit 131 may calculate the coefficient K of thermal conductivity for that day as the coefficient K of thermal conductivity for the individual based on the skin temperature Ts measured by a skin temperature sensor floating above the skin. For this reason, when calculating the coefficient K of thermal conductivity every day, it is difficult to determine the true value of the coefficient K of thermal conductivity for the individual compared to when the average value of the coefficient K of thermal conductivity calculated every day for a predetermined period (for example, five days) or more is used as the coefficient K of thermal conductivity for the individual, and therefore the coefficient K cannot be calculated.

[0081] Next, the setting unit 131 sets a criterion for the skin temperature sensor to float from the skin based on the calculated coefficient K of thermal conductivity. If the skin temperature sensor floats from the skin, an air layer will be created between the skin and the measuring device 10, which will reduce the thermal conductivity between the skin and the measuring device 10. As a result, the coefficient K of thermal conductivity will appear to increase.

[0082] Therefore, the setting unit 131 sets the apparent thermal conductivity coefficient K' when the skin temperature sensor is floating above the skin based on the calculated thermal conductivity coefficient K. Since the thermal conductivity coefficient K when the skin temperature sensor is not floating above the skin differs depending on the thickness of the measuring device 10 and the skin, the setting unit 131 sets the apparent thermal conductivity coefficient K' to, for example, a value that is 1.5, 2, or 3 times the normal thermal conductivity coefficient K.

[0083] The setting unit 131 can set, depending on the purpose, how many times the apparent thermal conductivity coefficient K' should be made larger than the thermal conductivity coefficient K. However, when measurements are taken over a long period of time, the degree of contact of the skin temperature sensor changes due to changes in the external temperature and movements of the subject, such as turning over in bed, and so the temperatures measured by the skin temperature sensor and the external temperature sensor will vary.

[0084] Therefore, when priority is given to increasing the accuracy of the core body temperature estimated by the estimation unit 135, the setting unit 131 sets the coefficient K' of apparent thermal conductivity to be smaller than when this is not the case. However, the setting unit 131 sets the coefficient K' of apparent thermal conductivity to be equal to or greater than the coefficient K of thermal conductivity. On the other hand, when priority is given to observing the movement of body temperature changes over a long period of time over accuracy, the setting unit 131 sets the coefficient K' of apparent thermal conductivity to be larger than when this is not the case.

[0085] When setting the coefficient K' of apparent thermal conductivity based on the coefficients K of thermal conductivity of many subjects, the setting unit 131 sets the coefficient K' of apparent thermal conductivity based on individual differences as well.

[0086] Next, the setting unit 131 sets a criterion for the skin temperature sensor to float from the skin based on the coefficient K' of apparent thermal conductivity. An example of setting the criterion by the setting unit 131 will be described below with reference to FIG. 6. FIG. 6 is a graph showing an example of the relationship between skin temperature and external temperature. FIG. 6 is a graph in which the horizontal axis represents the external temperature Ta in the above-mentioned equation (3) obtained by modifying the above-mentioned equation (2), and the vertical axis represents the skin temperature Ts.

[0087] In FIG. 6, for example, if a standard is set to uniformly determine that the skin temperature sensor has floated, there is a risk that data that is not actually floating will be determined to be floating.

[0088] Specifically, if the standard is line (i), which determines that a skin temperature Ts below 35.0°C indicates that the skin temperature sensor has floated, the temperature data of the first subject (contact) whose skin temperature sensor has come into contact with the skin of the first subject will also be determined to be floating.

[0089] Similarly, if the standard is line (ii), which determines that an external temperature Ta of 35.0°C or higher indicates that the skin temperature sensor has floated, the temperature data of a second subject whose skin temperature sensor has come into contact with the skin of the second subject (contact) will also be determined to be floating.Furthermore, if the standard is line (ii), the temperature data of a first subject whose skin temperature sensor has floated from the skin of the first subject will also be determined to be contact.

[0090] Therefore, instead of setting a predetermined skin temperature Ts or a predetermined external temperature Ta as the reference, the setting unit 131 sets the reference as the relationship between the skin temperature Ts and the external temperature Ta, which changes depending on whether the skin temperature sensor is floating above the skin.

[0091] As a change in the above relationship, when the skin temperature sensor is in contact with the skin, even if the external temperature Ta drops due to the influence of the outside air temperature, the skin temperature Ts does not drop by more than a certain temperature because it is in contact with the skin. As a result, the difference Ts - Ta between the skin temperature Ts and the external temperature Ta becomes larger.

[0092] On the other hand, when the skin temperature sensor is lifted from the skin, the skin temperature Ts becomes more susceptible to the influence of the outside air temperature and approaches the external temperature Ta, thereby reducing the difference between the skin temperature Ts and the external temperature Ta.

[0093] In this way, the value of the linear function that shows the numerical relationship between the skin temperature Ts and the external temperature Ta, such as the difference between the skin temperature Ts and the external temperature Ta, changes depending on whether the skin temperature sensor is lifted from the skin. Therefore, the setting unit 131 sets the relationship between the skin temperature Ts and the external temperature Ta, particularly whether the linear function inequality is satisfied, as the criterion for whether the skin temperature sensor is lifted from the skin, thereby improving the accuracy of the determination by the determination unit 133.

[0094] In Figure 6, the setting unit 131 sets the inequality of the linear function of equation (4) as the basis, where the skin temperature Ts is equal to or less than the right-hand side of equation (3) after replacing the slope (K / (K+1)) of the linear function with slope α1 and the intercept Tb / (K+1) with intercept b1 when Ta is the horizontal axis and Ts is the vertical axis.

[0095] In this case, the slope of the line derived from the temperature data in Figure 6 is given by the above-mentioned formula (7). Furthermore, by transforming formula (7), the above-mentioned formula (8) is obtained. The setting unit 131 calculates K = 0.43 / (1 - 0.43) ≒ 0.75 by substituting the slope α1 = 0.43 of the line segment (iii) derived from the temperature data of the first subject (contact) into formula (8).

[0096] The setting unit 131 calculates K=0.39 / (1-0.39)≒0.64 by substituting the slope α1=0.39 of the line segment (iv) derived from the temperature data of the second subject (contact) into equation (8). The setting unit 131 determines the average value of these values, 0.7, as the coefficient K of thermal conductivity.

[0097] Next, the setting unit 131 sets the apparent thermal conductivity coefficient K' based on a prior evaluation of the temperature data plotted on the graph in Figure 6. In Figure 6, when the skin temperature sensor is in contact with the skin, even if the external temperature Ta drops, the skin temperature Ts does not drop as much as the external temperature Ta because the skin is in contact with the skin, so the slope α1 is small. On the other hand, when the skin temperature sensor is floating from the skin, the skin temperature Ts also drops when the external temperature Ta drops, so the slope α1 is large. Also, in Figure 6, when K appears to be three times or more, the skin temperature sensor tends to float from the skin.

[0098] Therefore, the setting unit 131 sets K' = 3 × K = 3 × 0.7 = 2.1. Next, the setting unit 131 substitutes the set K' for K in the above-mentioned formula (4) to calculate the slope α1 as α1 = (K / (K+1)) = 2.1 / 3.1 = 0.68. Furthermore, the setting unit 131 sets the value of the intercept b1 of formula (4) based on a prior evaluation of the temperature data plotted on the graph in FIG. 6.

[0099] The setting unit 131 calculates the criterion for the skin temperature sensor to float above the skin as a linear function of the line (v) in Figure 6 by substituting the calculated values ​​for the slope α1 and intercept b1 of equation (4).

[0100] Next, another example of setting a reference by setting unit 131 will be described with reference to Fig. 7. Fig. 7 is a graph showing an example of the relationship between skin temperature and external temperature. Fig. 7 is a graph in which Ta in the following equation (9), obtained by modifying the above equation (2), is on the horizontal axis and Ts-Ta is on the vertical axis.

[0101]

number

[0102] The setting unit 131 replaces the slope -1 / (1+K) of the linear function in equation (9) with the slope α2 and the intercept Tb / (K+1) with the intercept b2 when the horizontal axis is Ta and the vertical axis is Ts-Ta in equation (9), and then sets the inequality of the linear function in equation (10) below, in which Ts-Ta is equal to or less than the right-hand side, as the basis.

[0103]

number

[0104] The slope of the line derived from the temperature data in Figure 7 is given by the following equation (11): Transforming equation (11) yields the following equation (12): The setting unit 131 substitutes the slope α2 = -0.57 of the line segment (vi) derived from the temperature data of the first subject (contact) into equation (12), thereby calculating K = -(1 - 0.57) / (-0.57) ≒ 0.75.

[0105]

number

[0106] The setting unit 131 calculates K=-(1-0.61) / (-0.61)≒0.64 by substituting the slope α=-0.61 of the line segment (vii) derived from the temperature data of the second subject (contact) into the above formula (12). The setting unit 131 determines the average value of these values, 0.7, as the coefficient K of thermal conductivity.

[0107] Next, the setting unit 131 sets the apparent thermal conductivity coefficient K' based on a prior evaluation of the temperature data plotted on the graph in Figure 7. In Figure 7, when the skin temperature sensor is in contact with the skin, even if the external temperature Ta drops, the skin temperature Ts is still in contact with the skin, so Ts - Ta is large. In this case, the absolute value of the slope α2 is large. On the other hand, when the skin temperature sensor is floating from the skin, as the external temperature Ta drops, the skin temperature Ts also drops, so Ts - Ta becomes smaller. In this case, the absolute value of the slope α2 also becomes smaller. Also, in Figure 7, when K appears to be three times or more, the skin temperature sensor tends to float from the skin.

[0108] Therefore, the setting unit 131 sets K' = 3 × K = 3 × 0.7 = 2.1. Next, the setting unit 131 substitutes the set K' for K in the above-mentioned equation (11) to calculate the slope α2 as α2 = -1 / (1 + K) = -1 / (1 + 2.1) = -1 / 3.1 = -0.32. In addition, the setting unit 131 sets the value of the intercept b2 of equation (10) based on a prior evaluation of the temperature data plotted on the graph in FIG. 7.

[0109] The setting unit 131 calculates the criterion for the skin temperature sensor to float above the skin as a linear function of the straight line (viii) in Figure 7 by substituting the calculated and set values ​​for the slope α2 and intercept b2 of equation (10).

[0110] Next, another example of setting a reference by setting unit 131 will be described with reference to Fig. 8. Fig. 8 is a graph showing an example of the relationship between skin temperature and external temperature. Fig. 8 is a graph in which Ts in the following equation (13), obtained by modifying the above equation (2), is plotted on the horizontal axis and Ts-Ta on the vertical axis.

[0111]

number

[0112] The setting unit 131 replaces the slope (-1 / K) of the linear function in the above equation (13) with Ts on the horizontal axis and Ts-Ta on the vertical axis as the slope α3 and the intercept Tb / K with the intercept b3, and then sets the inequality of the linear function in the following equation (14) where Ts-Ta is equal to or less than the right-hand side as the basis.

[0113]

number

[0114] In this case, the slope of the straight line derived from the temperature data in FIG. 8 is given by the following formula (15). Transforming formula (15) yields the following formula (16). Therefore, the setting unit 131 calculates two Ks by substituting the slope of the line segment (ix) derived from the temperature data of the first subject (contact) and the slope of the line segment (x) derived from the temperature data of the second subject (contact) into formula (16). The setting unit 131 calculates the average value of these as the coefficient K of thermal conductivity.

[0115]

number

[0116] Next, the setting unit 131 sets the coefficient K' of apparent thermal conductivity based on a prior evaluation of the temperature data plotted on the graph in Fig. 8. In Fig. 8, when the skin temperature sensor is in contact with the skin, even if the external temperature Ta drops, the skin temperature Ts is still in contact with the skin, so Ts - Ta is greater than the drop in skin temperature Ts. In this case, the absolute value of the slope α3 is large.

[0117] On the other hand, if the skin temperature sensor is lifted off the skin, a drop in the external temperature Ta will also cause the skin temperature Ts to drop, so the drop in skin temperature Ts will be close to Ts - Ta. In this case, the absolute value of the slope α3 is small. Also, in Figure 8, when K appears to be three times or more, the skin temperature sensor tends to lift off the skin.

[0118] Therefore, the setting unit 131 sets K' based on the above-mentioned preliminary evaluation and the apparent magnitude of K when the skin temperature sensor tends to float from the skin. Next, the setting unit 131 calculates the slope α3 by substituting the set K' for K in the above-mentioned equation (15). In addition, the setting unit 131 sets the value of the intercept b3 of equation (14) based on the preliminary evaluation of the temperature data plotted on the graph in FIG. 8.

[0119] The setting unit 131 calculates the criterion for the skin temperature sensor to float above the skin as a linear function of the line (xi) in Figure 8 by substituting the calculated and set values ​​for the slope α3 and intercept b3 of equation (14).

[0120] Returning to the explanation of Figure 2, the acquisition unit 132 acquires various types of information. For example, the acquisition unit 132 acquires skin temperature data measured by the measuring device 10, which is a wearable device attached to the torso of the user 20. Specifically, the acquisition unit 132 controls an application program installed in the user terminal 200 to acquire the skin temperature data from the user terminal 200 when the skin temperature data is transmitted from the measuring device 10 to the user terminal 200, at a fixed time each day, etc.

[0121] The acquiring unit 132 also acquires external temperature data. For example, the acquiring unit 132 may acquire external temperature data measured by the measuring device 10, or may acquire external temperature data measured by a device other than the measuring device 10.

[0122] Furthermore, the acquisition unit 132 is not limited to acquiring the temperature data described above, and may also acquire physiological data and any other physical information that is expected to affect the menstrual cycle, such as the age, height, and weight of each user. The acquisition unit 132 acquires this information by accepting input from the user 20, for example, through user registration in an app. Furthermore, if the measuring device 10 is capable of measuring biological information other than body temperature, the acquisition unit 132 may acquire the biological information together with the temperature data.

[0123] The acquiring unit 132 may also acquire body movement data of the user 20 using various sensors provided in the measuring device 10 or the user terminal 200. The body movement data is information indicating the movement of the body of the user 20, such as information indicating that the user 20 is awake or lying down (asleep). The body movement data may be detected by combining, for example, an acceleration sensor, an inertial sensor, a geomagnetic sensor, and the like.

[0124] The determination unit 133 determines whether the skin temperature sensor has floated off the skin based on the acquired skin temperature and external temperature, and a criterion for the skin temperature sensor to float off the skin, which is set based on the coefficient of thermal conductivity between the skin and the measuring device 10. For example, the determination unit 133 determines whether the skin temperature sensor has floated off the skin based on whether the acquired skin temperature and external temperature satisfy an inequality of a linear function that indicates the numerical relationship between the skin temperature and the external temperature as a criterion.

[0125] Specifically, when the setting unit 131 sets the above-mentioned formula (4) as a criterion, the determination unit 133 determines whether the skin temperature sensor has risen above the skin based on whether formula (4) is satisfied. In this case, when the acquired skin temperature and external temperature satisfy the inequality of formula (4), the determination unit 133 determines that the skin temperature sensor has risen above the skin.

[0126] The determination unit 133 can also determine that the skin temperature sensor has risen from the skin based on a graph of the relationship between the skin temperature and the external temperature. For example, in Figure 6, the criterion of the above-mentioned equation (4) is shown as a straight line (v), so the determination unit 133 determines that the skin temperature sensor has risen from the skin when the temperature data is plotted below the straight line (v) on the graph in Figure 6.

[0127] The calculation unit 136 calculates the representative temperature based on the deep body temperature estimated by the estimation unit 135. For example, the calculation unit 136 calculates the average or median value of the deep body temperature of the user 20 over a predetermined time range, such as a time period corresponding to the basal heart rate, as the representative temperature for the user 20 for one day.

[0128] The prediction unit 137 predicts information about the menstrual cycle of the user 20, such as the ovulation date, menstrual date, and menstrual cycle, based on the calculated representative temperature and physiological item data. For example, the prediction unit 137 identifies the transition date from the low temperature period to the high temperature period, or from the high temperature period to the low temperature period, based on the transition of the representative temperature calculated over several days. The prediction unit 137 then predicts the next menstrual date or ovulation date of the user 20 by adding the average number of days in the low temperature period or the average number of days in the high temperature period for the general public to the identified date.

[0129] Furthermore, the prediction unit 137 may display the prediction result on the user terminal 200. For example, the prediction unit 137 causes the communication unit 110 to transmit the prediction result, such as the date of the next ovulation day or menstruation day, to the user terminal 200. The user terminal 200 displays the received prediction result.

[0130] Furthermore, when a difference between the information about the next menstrual cycle, which is the prediction result, and information about multiple past menstrual cycles of user 20 satisfies a predetermined condition, prediction unit 137 may cause user terminal 200 to display information about the difference. For example, when the next menstrual day statistically calculated based on multiple past menstrual cycle results differs from the predicted next menstrual day by seven days or more, prediction unit 137 causes communication unit 110 to transmit a message to that effect to user terminal 200. User terminal 200 displays the message.

[0131] User 20 can obtain various information based on the display on user terminal 200. For example, if user 20 experiences a significant or continuing deviation in her menstrual cycle, she can understand that something is wrong with her physical condition. Furthermore, by linking the abnormality in her physical condition with changes in her lifestyle or physical condition that occurred during her current menstrual cycle, user 20 can consider the cause of the deviation (such as catching a cold, being under a lot of stress, or nothing in particular).

[0132] In this way, the prediction unit 137 allows the user 20 to judge for himself whether his physical condition is normal and he can go about his day with peace of mind, or whether something unusual is happening and he should be careful.

[0133] The prediction unit 137 may provide various information other than the predicted menstrual period date to the user 20 via the user terminal 200. When a sign of an approaching menstrual period or the like is observed in the prediction result, the prediction unit 137 may display (notify) the user 20 with more emphasis than usual. The prediction unit 137 may also provide advice to the user 20 based on the predicted menstrual cycle. For example, the prediction unit 137 may provide the user 20 with advice regarding the user's physical condition, possibility of pregnancy, premenstrual syndrome, best time to lose weight, menopausal symptoms, etc., estimated from the menstrual cycle.

[0134] (1-3. Processing Procedure According to the Embodiment) The processing flow of the estimation device 100 will be described with reference to Fig. 9 to Fig. 11. First, the setting processing, which is processing performed as a preliminary examination before steps S1 to S5 in Fig. 1, will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the procedure of the setting processing according to the embodiment.

[0135] In step S11, the setting unit 131 calculates the coefficient K of thermal conductivity. For example, the setting unit 131 calculates the coefficient K of thermal conductivity by substituting the gradient α1 of the temperature data of the subject plotted on the graph of FIG. 6 as a preliminary experiment into equation (8).

[0136] In step S12, the setting unit 131 sets the apparent thermal conductivity coefficient K', which is the basis for the floating criterion, based on the calculated thermal conductivity coefficient K. For example, if the temperature data plotted on the graph in Fig. 6 indicates that K appears to be three times or more, indicating that the skin temperature sensor tends to float from the skin, the setting unit 131 sets the apparent thermal conductivity coefficient K' to a value three times the thermal conductivity coefficient K.

[0137] In step S13, the setting unit 131 sets a criterion for the skin temperature sensor to float from the skin based on the calculated coefficient K' of apparent thermal conductivity. For example, the setting unit 131 calculates the value of the slope α1 by substituting the set K' for K in the above-mentioned equation (7). In addition, the setting unit 131 sets the value of the intercept b1 in equation (4) based on a prior evaluation of the temperature data plotted on the graph in FIG. 6.

[0138] The setting unit 131 calculates the criterion for the skin temperature sensor to float above the skin as a linear function of the straight line in FIG. 6 by substituting the calculated and set values ​​for the slope α1 and intercept b1 of equation (4).

[0139] Next, the estimation process corresponding to steps S1 to S5 in Fig. 1 will be described with reference to Fig. 10. Fig. 10 is a flowchart showing the procedure of the estimation process according to the embodiment.

[0140] In step S21, the acquisition unit 132 acquires the skin temperature Ts and the external temperature Ta measured by the skin temperature sensor. For example, the acquisition unit 132 continuously acquires the skin temperature Ts and the external temperature Ta measured by the skin temperature sensor at regular intervals.

[0141] In step S22, the removal unit 134 removes the floating temperature, which is the skin temperature measured by the skin temperature sensor that is determined to have floated off the skin, from the acquired skin temperature Ts. For example, the removal unit 134 removes the temperature data plotted below the line (v) in the graph of FIG. 6 as floating temperature data.

[0142] In step S23, the estimation unit 135 estimates the deep body temperature Tb based on the skin temperature Ts from which the floating temperature has been removed and the external temperature Ta. For example, the estimation unit 135 estimates the deep body temperature Tb of the user 20 for each predetermined time period by substituting the skin temperature Ts from which the floating temperature has been removed and the external temperature Ta corresponding to the skin temperature Ts into the above-mentioned equation (2).

[0143] In step S24, the calculation unit 136 calculates the representative temperature based on the deep body temperature Tb estimated by the estimation unit 135. For example, the calculation unit 136 calculates the average value of the deep body temperature Tb over a predetermined time range, such as a time period corresponding to the basal heart rate, as the representative temperature for the day.

[0144] Next, an example of the removal process in step S22 of Fig. 10 will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the procedure of the removal process according to the embodiment.

[0145] In step S31, the judgment unit 133 judges whether the skin temperature sensor has floated off the skin based on the acquired skin temperature Ts and external temperature Ta, as well as a criterion for the skin temperature sensor to float off the skin, which is set based on the coefficient K of thermal conductivity between the skin and the measuring device 10.

[0146] For example, the judgment unit 133 judges whether the skin temperature sensor has floated above the skin based on whether the acquired skin temperature Ts and external temperature Ta satisfy an inequality of a linear function that indicates the numerical relationship between the skin temperature Ts and the external temperature Ta as a criterion.

[0147] Specifically, the determination unit 133 determines whether the skin temperature sensor has risen from the skin based on whether the acquired skin temperature Ts and external temperature Ta satisfy the above-mentioned formula (4). More specifically, the determination unit 133 determines that the skin temperature sensor has risen from the skin if the acquired skin temperature Ts and external temperature Ta satisfy the above-mentioned formula (4). In this case, the determination unit 133 can also determine that the skin temperature sensor has risen from the skin if the temperature data is plotted below the straight line (v) on the graph in Figure 6, which indicates the criterion for the above-mentioned formula (4).

[0148] If the determination unit 133 determines that the skin temperature sensor has floated off the skin (step S31; Yes), the removal unit 134 removes the floating temperature from the acquired skin temperature and external temperature in step S32. For example, the removal unit 134 removes the temperature data plotted below the line (v) in the graph of FIG. 6 as floating temperature data.

[0149] If the judgment unit 133 judges that the skin temperature sensor is in contact with the skin (step S31; No), the removal unit 134 does not remove the floating temperature from the acquired skin temperature and external temperature, and terminates the removal process.

[0150] (2. Modifications of the embodiment) The above-described embodiment may be modified in various ways. For example, in the above-described embodiment, an example was shown in which the user terminal 200 according to the present disclosure acquires skin temperature data and external temperature data measured by the measuring device 10. However, the user terminal 200 does not necessarily acquire the skin temperature data, external temperature data, etc. from the measuring device 10. For example, the measuring device 10 may transmit these data directly to the estimation device 100 without going through the user terminal 200.

[0151] In addition, in the embodiment, an example has been shown in which the user terminal 200 transmits data to the estimation device 100, and the estimation device 100 then predicts the menstrual period, etc. However, if the user terminal 200 has a processing unit corresponding to the control unit 130, the user terminal 200 may predict the menstrual period, etc. based on temperature data measured by the measuring device 10, etc. In other words, the processing performed by the estimation device 100 in the embodiment may be performed by the user terminal 200.

[0152] In the embodiment, the estimation device 100 has a storage unit 120 and stores measurement data, physiological data, and the like. However, this information may be stored in an external storage device (such as cloud storage) other than the estimation device 100. The measuring device 10 is not limited to the embodiment shown in FIG. 1 and may be a wearable device such as a smart watch. The skin temperature sensor and the external temperature sensor do not necessarily have to be installed in the same device.

[0153] FIG. 1 also shows body temperatures measured on consecutive days during a menstrual cycle. However, such body temperature data may be sufficient if it covers a predetermined number of days, such as one menstrual cycle. Such body temperature data is not necessarily limited to data acquired from the user on a daily basis. For example, even if there is a missing portion due to the user forgetting to measure or making a measurement error, the estimation device 100 may interpolate the missing portion using data from before and after the missing portion and acquire the interpolated data as data corresponding to the missing date. Furthermore, temperature data may be subject to various modifications during information processing, such as converting the measured numerical value into an arbitrary value and performing calculations or statistics.

[0154] In the embodiment, the setting unit 131 of the estimation device 100 sets a criterion for the skin temperature sensor to float from the skin based on the coefficient of thermal conductivity between the skin and the measuring device 10. However, the criterion does not necessarily have to be set by the estimation device 100. For example, the criterion may be set by another device. In this case, the acquisition unit 132 of the estimation device 100 acquires the criterion set by the other device via the communication unit 110.

[0155] (3. Other embodiments) The processing according to the above-described embodiment may be implemented in various different forms other than the above embodiment.

[0156] For example, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. Furthermore, the process procedures, specific names, and information including various data and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information. For example, the order of steps S22 and S23 in FIG. 10 may be reversed.

[0157] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0158] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0159] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0160] (4. Effects of the Estimation Device According to the Present Disclosure) As described above, the estimation device (estimation device 100 in the embodiment) according to the present disclosure includes an acquisition unit (acquisition unit 132 in the embodiment), a determination unit (determination unit 133 in the embodiment), a removal unit (removal unit 134 in the embodiment), and an estimation unit (estimation unit 135 in the embodiment).

[0161] The acquisition unit acquires the skin temperature measured by the sensor while in contact with the skin, and the external temperature, which is the temperature on the outside air side. The determination unit determines whether the sensor has floated off the skin based on the acquired skin temperature and external temperature, and a sensor float criterion set based on the coefficient of thermal conductivity between the skin and a measuring device (measuring device 10 in this embodiment) equipped with the sensor. The removal unit removes, from the acquired skin temperature and external temperature, the skin temperature measured by the sensor determined to have floated off the skin and the float temperature, which is the external temperature corresponding to that skin temperature. The estimation unit estimates the core body temperature based on the skin temperature and external temperature from which the float temperature has been removed.

[0162] In this way, the estimation device determines whether the skin temperature sensor has floated off the skin and removes the floatation temperature based on the acquired skin temperature and external temperature and the criteria for the skin temperature sensor to float, and then estimates the deep body temperature based on the skin temperature and external temperature from which the floatation temperature has been removed. This allows the estimation device to accurately estimate the deep body temperature based on the skin temperature and external temperature from which the floatation temperature has been removed, even if the sensor floats off the skin immediately after measuring the skin temperature.

[0163] The determination unit determines whether the sensor has floated above the skin based on whether the acquired skin temperature and external temperature satisfy an inequality of a linear function that indicates the numerical relationship between the skin temperature and the external temperature as a reference. Whether the sensor has floated above the skin changes the value of the linear function that indicates the numerical relationship between the skin temperature and the external temperature, such as the difference between the skin temperature and the external temperature. Therefore, the estimation device determines whether the sensor has floated above the skin based on whether the inequality of the linear function is satisfied, allowing the determination to be made accurately even for different subjects.

[0164] The determination unit determines whether the sensor has been lifted from the skin based on whether the acquired skin temperature and external temperature satisfy a linear function inequality that includes a coefficient of thermal conductivity as an inequality of a linear function. The estimation device can set the linear function inequality that includes a coefficient of thermal conductivity as a criterion simply by modifying the linear function that shows the numerical relationship between the skin temperature and the external temperature, which includes a coefficient of thermal conductivity. Therefore, the estimation device can easily set a criterion that can accurately determine whether the sensor has been lifted from the skin even for different subjects.

[0165] (5. Hardware Configuration) The estimation device 100, user terminal 200, measuring device 10, and other information devices according to the above-described embodiments are realized by a computer 1000 configured as shown in FIG. 12, for example. The estimation device 100 according to the embodiment will be described below as an example. FIG. 12 is a hardware configuration diagram showing an example of the computer 1000 that realizes the functions of the estimation device 100. The computer 1000 has a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. The components of the computer 1000 are connected by a bus 1050.

[0166] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs.

[0167] The ROM 1300 stores boot programs such as a basic input output system (BIOS) executed by the CPU 1100 when the computer 1000 is started, and programs that depend on the hardware of the computer 1000 .

[0168] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records a program that executes the estimation process according to the present disclosure, which is an example of program data 1450.

[0169] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (e.g., the Internet). For example, the CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0170] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to an output device such as a display, a speaker, or a printer via the input / output interface 1600. The input / output interface 1600 may also function as a media interface for reading programs and the like recorded on a predetermined recording medium. Examples of media include optical recording media such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disk), magneto-optical recording media such as an MO (Magneto-Optical disk), tape media, magnetic recording media, and semiconductor memories.

[0171] For example, when the computer 1000 functions as the estimation device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes an estimation program loaded onto the RAM 1200, thereby realizing functions of the control unit 130 and the like. The HDD 1400 stores the estimation program according to the present disclosure and data in the storage unit 120. The CPU 1100 reads and executes program data 1450 from the HDD 1400, but as another example, the CPU 1100 may obtain these programs from another device via an external network 1550.

[0172] The above describes the embodiments of the present application in detail based on the drawings, but these are merely examples, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0173] 100 Estimator 110 Communications Department 120 Storage section 121 Measurement data storage unit 122 Representative temperature data storage section 123 Physiological data storage unit 130 Control Unit 131 Setting section 132 Acquisition Department 133 Judgment section 134 Removal section 135 Estimation part 136 Calculation Unit 137 Prediction Department

Claims

1. an acquisition unit that acquires a skin temperature measured by the sensor while the sensor is in contact with the skin and an external temperature, which is the temperature of the outside air; a determination unit that determines whether the sensor has been lifted from the skin based on a criterion for the sensor to be lifted from the skin, which is set based on the acquired skin temperature and external temperature, and a coefficient of thermal conductivity between the skin and a measuring device equipped with the sensor; and a removal unit that removes the skin temperature measured by the sensor that has been determined to have floated off the skin and the floating temperature, which is the external temperature corresponding to the skin temperature, from the acquired skin temperature and external temperature; an estimation unit that estimates a deep body temperature based on the skin temperature and the external temperature from which the floating temperature has been removed; An estimation device comprising:

2. the determination unit determines whether the sensor is lifted from the skin based on whether the acquired skin temperature and external temperature satisfy an inequality of a linear function that indicates a numerical relationship between the skin temperature and the external temperature as the criterion.

2. The estimation device according to claim 1 .

3. the determination unit determines whether the sensor is lifted from the skin based on whether the acquired skin temperature and external temperature satisfy an inequality of a linear function including a coefficient of the thermal conductivity as the inequality of the linear function.

3. The estimation device according to claim 2.

4. The computer The skin temperature measured by the sensor while in contact with the skin and the external temperature, which is the temperature of the outside air, are acquired. determining whether the sensor has been lifted from the skin based on a criterion for the sensor to be lifted from the skin, which is set based on the acquired skin temperature and external temperature, and a coefficient of thermal conductivity between the skin and a measuring device equipped with the sensor; The skin temperature measured by the sensor determined to have floated off the skin and the floating temperature, which is the external temperature corresponding to the skin temperature, are removed from the acquired skin temperature and external temperature; A deep body temperature is estimated based on the skin temperature and the external temperature from which the floating temperature has been removed. An estimation method characterized by:

5. Computer, an acquisition unit that acquires a skin temperature measured by the sensor while the sensor is in contact with the skin and an external temperature, which is the temperature of the outside air; a determination unit that determines whether the sensor has been lifted from the skin based on a criterion for the sensor to be lifted from the skin, which is set based on the acquired skin temperature and external temperature, and a coefficient of thermal conductivity between the skin and a measuring device equipped with the sensor; and a removal unit that removes the skin temperature measured by the sensor that has been determined to have floated off the skin and the floating temperature, which is the external temperature corresponding to the skin temperature, from the acquired skin temperature and external temperature; an estimation unit that estimates a deep body temperature based on the skin temperature and the external temperature from which the floating temperature has been removed; An estimation program characterized by causing the program to function as an estimation device comprising:

Images