Temperature detection device, temperature detection method and recording medium

Abstract

The present invention relates to a temperature detection device, a temperature detection method and a recording medium. The temperature detection device comprises: a first measuring unit (11) for measuring a first temperature of a first part of an object having a heat source; a second measuring unit (12) for measuring a second temperature of a second part farther from the heat source than the first part; and a CPU (13). The CPU (13) obtains estimated temperature data related to a group of a first temperature and a second temperature measured under a state satisfying a predetermined condition corresponding to a certain change state of the temperature of the heat source, obtains a single thermal resistance parameter in a thermal model based on the obtained estimated temperature data, and estimates the temperature of the heat source as the deep temperature of the object based on the obtained thermal resistance parameter, the measured first temperature and the second temperature.

Description

Technical Field

[0001] The invention relates to a temperature detection device, a temperature detection method and a recording medium. Background Art

[0002] There are temperature detection devices that detect the temperature of a portion that cannot be directly measured (detection target portion). As such a temperature detection device, it is known to use a thermal model based on the measurement results of the temperature at the measurable portion and the heat flow to calculate the temperature of the detection target portion from the heat flow between the detection target portion and the temperature measurement-capable portion.

[0003] As a thermal model, there is a thermal model that determines the heat flow based on the temperature difference and thermal resistance between two points and does not consider the capacity component. In Japanese Patent Publication No. 2019-97819, the following technology is disclosed: the temperature between two points with known thermal resistance is measured twice on the premise that the temperature of the detection object part does not change, and the thermal resistance between one of the above two points and the detection object part is estimated, and the temperature of the estimated detection object part can be calculated. Summary of the invention

[0004] In order to achieve the above-mentioned object, the present invention comprises: a first temperature sensor for measuring a first temperature of a first part of an object having a heat source; a second temperature sensor for measuring a second temperature of a second part which is farther away from the heat source than the first part; and a processing unit, the processing unit performing the following processing: obtaining estimated temperature data related to the first temperature measured by the first temperature sensor in a state where a certain condition corresponding to a certain change state of the temperature of the heat source is satisfied, and the second temperature measured by the second temperature sensor in a state where the certain condition is satisfied, based on A thermal resistance parameter characterizing the relationship between a first thermal resistance and a second thermal resistance in a thermal model is obtained based on the obtained estimated temperature data, wherein the thermal model includes the heat source, the first part, and the second part, the relationship between the temperature and the heat flow between the heat source and the first part is characterized by the first thermal resistance, and the relationship between the temperature and the heat flow between the first part and the second part is characterized by the second thermal resistance, and the temperature of the heat source is estimated based on the obtained thermal resistance parameter and the measured first temperature and the second temperature as the deep temperature of the object. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1A It is a diagram for explaining the temperature detection device according to this embodiment.

[0006] Figure 1B This is a block diagram of the temperature detection device according to this embodiment.

[0007] Figure 2A It is a diagram for explaining a method of estimating deep temperature.

[0008] Figure 2B This is a diagram for explaining the amount of change in the temperatures T1(t) and T2(t) measured in response to a change in the ambient temperature.

[0009] Figure 3 : is a flowchart showing the control procedure of the deep temperature estimation process.

[0010] Figure 4 This is a flowchart showing Modification 1 of the deep temperature estimation process.

[0011] Figure 5 This is a flowchart showing a third modification of the deep temperature estimation process.

[0012] Fig. 6A It is a diagram showing a fourth modification of the temperature detection device.

[0013] Figure 6B It is a diagram showing a fifth modification of the temperature detection device. DETAILED DESCRIPTION

[0014] Embodiments of the present invention will be described below based on the drawings.

[0015] Figure 1A , Figure 1B It is a diagram for explaining the temperature detection device 1 according to the present embodiment.

[0016] The temperature detection device 1 is used to detect the deep temperature (deep body temperature) of a living body (an object having a heat source, here, a human body) by estimating the temperature based on the measurement result of the temperature of a measurable part, for example, by inserting it into the ear hole of the living body to measure the temperature of two places, the inside of the ear and near the entrance. The so-called deep body temperature generally refers to the temperature of a heat-generating part (heat source) in the body, such as an internal organ or a brain.

[0017] like Figure 1A As shown, the temperature detection device 1 is divided into an insertion portion 10a and a main body 10b, and the insertion portion 10a is inserted into the ear hole. The first measuring portion 11 (first temperature sensor) and the second measuring portion 12 (second temperature sensor) are located in the insertion portion 10a, and the CPU 13 (processing unit. Central Processing Unit, central processing unit), storage unit 14 and communication unit 15 are located in the main body 10b.

[0018] The first measuring unit 11 measures (detects) the temperature (first temperature) of the first portion (a portion close to the heat generating portion) on the inner side of the ear, and outputs the measurement result to the CPU 13. The first measuring unit 11 is, for example, an infrared temperature sensor, and measures the temperature of the eardrum or the like near the insertion side end portion of the temperature detecting device 1 toward the inner side of the ear.

[0019] The second measuring unit 12 measures the temperature (second temperature) of a second portion (a portion farther from the heat generating portion than the first portion) on the ear opening side, and outputs the measurement result to the CPU 13. The second measuring unit 12 is a contact temperature sensor using a thermistor, for example.

[0020] The CPU 13 is a hardware processor that performs calculation processing to comprehensively control the operation of the temperature detection device 1 , and includes a dedicated microcomputer (microcomputer module), an IC chip, and the like.

[0021] The storage unit 14 includes a RAM (Random Access Memory) and a nonvolatile memory. The RAM provides a memory space for the CPU 13 to work and stores temporary data. The nonvolatile memory stores control programs, setting data, etc. executed by the CPU 13. The nonvolatile memory is, for example, a flash memory.

[0022] like Figure 1B As shown in the block diagram of , a program 141 and temperature data 142 are stored in the storage unit 14. The program 141 is stored in a non-volatile memory and is used to perform control actions of the temperature detection device 1. The temperature data 142 includes data on the measurement results of the first measuring unit 11 and the second measuring unit 12, (estimated) operation parameters obtained based on the measurement results, and deep temperatures (deep body temperatures) at various times estimated (calculated) based on these measurement results and operation parameters. These data can be stored in a RAM or in a non-volatile memory. For example, if the temperature detection device 1 always performs a temperature measurement action and it is appropriate to send the measurement results to an external device through the communication unit 15, the temperature data 142 can also be stored only in the RAM. In the case of switching the on and off of the switching action such as a switch operation, and in the case where data that is not sent to an external device needs to be maintained during the off period, the temperature data 142 is stored in a non-volatile memory.

[0023] The communication unit 15 performs wireless communication with an external device. The wireless communication standard may be, for example, UWB (Ultra Wide Band) that complies with BAN (Body Area Network), or Low Energy standard of Bluetooth (registered trademark), but the intensity of radio waves is specified so as not to cause adverse effects on the human body. When the measurement data of the first measurement unit 11 and the second measurement unit 12 can be displayed in real time in an external device through an application, the communication unit 15 can send the measurement results to the external device in real time through the control of the CPU 13.

[0024] The CPU 13, the storage unit 14, and the communication unit 15 need to ensure that the heat generated by these actions does not affect the measurement temperature of each of the first measuring unit 11 and the second measuring unit 12. For example, the temperature detection device 1 may have a heat insulating layer between the portion of the main body 10b where the CPU 13, the storage unit 14, and the communication unit 15 are provided, and the portion where the first measuring unit 11 and the second measuring unit 12 are provided. The heat insulating layer may be of a size, position, and orientation that does not hinder the heat dissipation from the vicinity of the ear of the organism. On the other hand, if the main body 10b is large, the wearing feeling will be reduced, especially the interference with sleep, etc., so it may be of a small (thin) shape.

[0025] In addition to the above, the temperature detection device 1 can also have a built-in storage battery, and receive power supply from the storage battery to operate each part. The storage battery can be a dry cell, etc., and can be appropriately disassembled and replaced. Alternatively, the storage battery can be a rechargeable secondary battery.

[0026] In addition, the temperature detection device 1 may have a switch (not shown) and perform the temperature measurement only during the period when the switch is turned on, or may have no switch and always continue the temperature measurement. Alternatively, the temperature detection device 1 may have a contact sensor, etc., to change the temperature measurement and the measurement interval based on the result of determining whether the device itself is inserted into the ear. In addition, the duration from the start of the temperature measurement to the end of the measurement, the time period for the temperature measurement (start time and end time) may be set, and the measurement may be automatically ended (started). In this case, the temperature detection device 1 counts the current date and time, and determines the start timing and / or end timing of the temperature measurement based on the current date and time.

[0027] Furthermore, the temperature detection device 1 may include a display unit. The display unit may be any device as long as it can display the measured temperature, or display the above-mentioned current date and time, duration, and time period.

[0028] Next, a method of estimating the deep temperature as a temperature detection method according to the present embodiment will be described.

[0029] In the temperature detection device 1 of the present embodiment, the CPU 13 obtains the first temperature of the first part and the second temperature of the second part from the first measuring unit 11 and the second measuring unit 12 at appropriate time intervals. After accumulating the data of these measured temperatures, the data satisfying the conditions described below are extracted, and the parameters required for estimating the deep temperature are obtained according to the conditions. If the parameters are obtained, the estimated deep temperature can be calculated based on the parameters and the temperature of the first part and the temperature of the second part.

[0030] Figure 2A , Figure 2B It is a diagram for explaining a method of estimating deep temperature.

[0031] like Figure 2A As shown in FIG. 1 , in the temperature detection device 1, a heat flow in a direction perpendicular to the body surface between a heat source in the body (a measurement site Pb of a deep temperature), a first site P1, a second site P2, and an external environment is considered by a thermal model represented by a thermal circuit having thermal resistance (capacitance component is not considered; quasi-static). Here, the eardrum Ed isolated from the insertion part 10a inserted into the ear E is set as the first site P1, and the distance between the first site P1 and the second site P2 is set to be larger than the length of the insertion part 10a.

[0032] The heat flow I(t) at each time t between each part is determined by the thermal resistance R and the temperature difference dT(t) at that time t between each part. That is, if the thermal resistance Rb (first thermal resistance) is set between the measurement part Pb (deep temperature Tb(t)) and the first part P1 (temperature T1(t)) (the surface including it), the relationship between the outward heat flow Ib(t) at the time t in this part, the temperature T1(t), and the deep temperature Tb(t) is given by formula (1).

[0033] Ib(t)=(Tb(t)-T1(t)) / Rb...(1)

[0034] In addition, if the thermal resistance Rs (second thermal resistance) is set between the first part P1 (the surface including it) and the second part P2 (temperature T2(t)) (the surface including it), the relationship between the outward heat flow Is(t) at the time t in this part and the temperatures T1(t) and T2(t) is given by formula (2).

[0035] Is(t)=(T1(t)-T2(t)) / Rs...(2)

[0036] In this thermal model, the heat from the heat source flows without any loss to the heat absorber, which is the external atmosphere. Therefore, Ib(t)=Is(t) holds. Therefore, the deep temperature Tb(t) can be determined by measuring the temperatures T1(t) and T2(t) using the thermal resistances Rs and Rb as parameters. However, in the temperature detection device 1 of this embodiment, the thermal resistances Rs and Rb are unknown parameters.

[0037] By arranging equations (1) and (2) for the balance of heat flow, the following equation (3) can be obtained.

[0038] Tb(t)=(Rb / Rs)×(T1(t)-T2(t))+T1(t)...(3)

[0039] Therefore, even if the thermal resistances Rs and Rb cannot be determined separately, as long as the thermal resistance ratio a=Rb / Rs can be obtained as a single thermal resistance parameter representing the relationship between the thermal resistances Rs and Rb, the deep temperature Tb(t) can be calculated based on the thermal resistance ratio a and the measured temperatures T1(t) and T2(t).

[0040] Here, in the above-mentioned formula (3), it is not easy to measure and determine the deep temperature Tb(t) as the estimation object. In addition, the thermal resistances Rb and Rs are also parameters that change according to the measurement object or the wearing state of the temperature detection device 1, and cannot be determined uniformly in advance. Therefore, if the time variation of formula (3) is taken, it becomes formula (4) and (5).

[0041] dTb(t)=a×dTdf(t)+dT1(t)...(4)

[0042] dTdf(t)=dT1(t)-dT2(t)...(5)

[0043] In the formulas (4) and (5), if the temperature difference change dTdf(t) that satisfies the specific temperature change dTb(t) of the deep temperature Tb(t), that is, the temperature T1(t) change dT1(t) (the first change) and the temperature T2(t) change dT2(t) (the second change) are obtained, the thermal resistance ratio a can be obtained.

[0044] The temperature change dTb(t) of the deep temperature Tb(t) often becomes zero (a certain change state of the temperature of the heat source). Especially during sleep, large changes in the deep temperature Tb(t) are suppressed, and the gentle increase and decrease within the daily change cycle becomes the main change. In a short time interval, the temperature change dTb(t) often becomes zero. At this time, the deep temperature Tb(t) and the ambient temperature Te(t) are different from the usual situation, so a heat flow I(t) is generated between the two, and the temperature T1(t) and the temperature T2(t) change in accordance with the change of the ambient temperature Te(t). During sleep, the ambient temperature Te(t) is affected not only by the surrounding temperature (room temperature), but also by the positional relationship between the bedding, pillow and ears, etc.

[0045] like Figure 2B As shown in FIG. 1 , under the premise of such a change in the ambient temperature Te(t), when the temperature change dTb(t) of the deep temperature Tb(t) is zero in the thermal model of this embodiment, several constraints are generated in the changes dT1(t) and dT2(t) of the measured temperatures T1(t) and T2(t). That is, when the ambient temperature Te(t) changes relative to the deep temperature Tb(t):

[0046] (Condition A) The temperatures T1(t) and T2(t) measured between these heat sources and the external environment also change (the absolute values ​​of the changes dT1(t) and dT2(t) are greater than a certain first threshold value; first condition).

[0047] (Condition B) In this case, the changes in temperature T1(t) and T2(t) are in the same direction as the change in ambient temperature Te(t) (the changes dT1(t) and dT2(t) have the same sign). Furthermore, the change is determined by the ratio of the thermal resistances of each section, and the absolute value of the change dT2(t) of temperature T2(t) measured on the outside, which is easily affected by the change in ambient temperature Te(t), becomes larger than the absolute value of the change dT1(t) of temperature T1(t) measured on the inside (Condition 2).

[0048] These conditions A and B (conditions corresponding to the above-mentioned change state) are not sufficient conditions for the temperature change dTb(t) to be zero, but they are necessary conditions for making the temperature change dTb(t) smaller than the change in the ambient temperature Te(t) and for the assumption of the thermal model not to collapse (that is, no heat capacity or other heat sources are generated between the measurement site Pb and the external environment) under the premise that the ambient temperature Te(t) changes. These conditions A and B are determined in advance, and it is determined whether each group of temperature data satisfies the conditions A and B (conditions A and B are established), and the temperature changes dT1(t) and dT2(t) of multiple groups that satisfy the conditions A and B are extracted. Then, by using the extracted changes dT1(t) and dT2(t), a relatively reasonable parameter (thermal resistance ratio a) is statistically obtained (estimated) for dTb(t)=0, thereby calculating and estimating the deep temperature Tb(t).

[0049] In addition, the first threshold value related to the condition is appropriately determined within a range that allows the amounts of change dT1(t) and dT2(t) to be measured.

[0050] Specifically, the N-dimensional vectors v(dT1(ts)), v(dT2(ts)) obtained by arranging the components of the extracted N groups of temperature change data (change dTl(t), change dT2(t)), and the N-dimensional vector v(dTb(ts)) of the temperature change dTb(t) of the deep temperature Tb(t) (where v refers to a vector) are represented by the following formula (6) according to formula (4).

[0051] v(dTb(ts))=a×v(dTdf(ts))+v(dT1(ts))...(6)

[0052] The thermal resistance ratio a when the magnitude of v(dTb(ts))|v(dTb(ts))| takes a minimum value, that is, when the partial differential of a based on the sum of the squares of the components on the right side of equation (6) becomes 0, becomes a relatively reasonable value of the thermal resistance ratio a. The result of the equation operation is represented by the following equation (7).

[0053] a=-v(T1(ts))·v(dTdf(ts)) / |v(dTdf(ts))| 2 ...(7)

[0054] Here, "·" in the formula (7) is the inner product of the preceding and following vectors.

[0055] The extracted changes dT1(ts) and dT2(ts) can be the difference between the measurement results that are adjacent in time. However, due to the noise, the small deviation in the measurement accuracy (resolution of the digital conversion value), and the large number of changes, the time changes of the temperature T1(t) and T2(t) can also be smoothed. As a smoothing method, it can be the difference of the moving average (or weighted average) of the measurement results in a certain period relative to the target time. The frequency components that are easy to be generated as noise in the measurement can be discarded by frequency filtering of multiple measurement data in a certain period. After smoothing the time changes, the difference values ​​of each smoothed time data can be obtained. In addition, the local changes in a certain period can be approximated by a polynomial, and the above-mentioned changes dT1(t), dT2(t), etc. can be determined based on the output of the Savitzky-Golay differential filter using the polynomial approximation. Furthermore, immediately after the ambient temperature Te(t) changes, the temperature distribution and heat flow also become dynamic states that deviate from the thermal model, and therefore it is better to smooth them using frequency characteristics such as temperature changes at time intervals that reflect the degree to which such dynamic changes subside.

[0056] As described above, in the temperature detection device 1 of the present embodiment, the thermal resistance ratio a as a parameter is estimated after obtaining a plurality of measurement data. In particular, statistically, the greater the number of groups of measurement data extracted within the range where the thermal resistance ratio a does not change, the higher the accuracy. Therefore, the estimation of the deep temperature Tb(t) using the obtained thermal resistance ratio a is not performed in real time, but after the data is summarized within a certain measurement time. For example, the measurement data can be continuously obtained during sleep time, and the thermal resistance ratio a and the deep temperature Tb(t) at each moment can be estimated by summarizing the measurement data or time information of other sensors after the measurement is completed.

[0057] Figure 3 This is a flowchart showing a control procedure performed by the CPU 13 in the deep temperature estimation process executed by the temperature detection device 1 of the present embodiment.

[0058] When the deep temperature estimation process continues to be performed from the start of the operation of the temperature detection device 1 until a predetermined time has elapsed, the CPU 13 starts by reading the program 141 from a nonvolatile memory or the like at the start of the operation.

[0059] When the deep temperature estimation process is started, the CPU 13 performs initial settings such as starting the first measuring unit 11 and the second measuring unit 12 and setting the storage area, and starts the temperature measurement of the first measuring unit 11 and the second measuring unit 12 (step S101). The measurement timing of the first measuring unit 11 and the measurement timing of the second measuring unit 12 may be simultaneous, or may be slightly offset by a time scale shorter than the heat conduction between the first part P1 and the second part P2. In addition, even in the case of simultaneous measurement, the timing of sending the measurement data to the CPU 13 may be staggered.

[0060] CPU13 determines whether the measured temperature is in a quasi-static state (step S102). As a state that is not quasi-static, here, first, it is assumed that when the temperature detection device 1 is worn on the ear, there is a large difference between the temperature of the second part P2 and the temperature of the temperature detection device 1 itself, resulting in a sharp change in temperature T2(t). For example, after the measurement starts, the temperature rises rapidly to the assumed body temperature level (36-40°C), and until the temperature change becomes below the reference value or the temperature reaches a maximum value, CPU13 determines that it is not quasi-static. In addition, after the temperature acquisition starts, in the case of a sharp temperature change even during continuous acquisition, it will become a dynamic heat flow state (temperature distribution), which can also be distinguished in the deep temperature estimation process, or it can be set as a separate process in the subsequent analysis, and no distinction is made when the data is acquired but all acquired.

[0061] When it is determined that the measured temperature is quasi-static ("Yes" in step S102), CPU13 obtains the measurement data from the first measuring unit 11 and the second measuring unit 12 respectively and stores them in the storage unit 14 (step S103). The stored data is maintained so that the measurement date and time can be determined. That is, in the storage unit 14, the measurement date and time and the measured temperature can be respectively established in correspondence, or only the storage start date and time and the measurement interval can be determined as initial information, and only the measured temperature is stored in sequence thereafter. In this case, when a measurement error occurs after the acquisition of quasi-static data begins, the measurement data is not skipped, but the data showing the error is stored and maintained. CPU13 can also send the measurement data stored in the storage unit 14 to an external device through the communication unit 15. Then, the processing of CPU13 is transferred to step S104. When it is determined that the measured temperature is not quasi-static ("No" in step S102), the processing of CPU13 is transferred to step S104.

[0062] If the process is transferred to step S104, the CPU 13 determines whether the measurement is completed (in addition to the end of the measurement time and the measurement period, it can also include the manual disconnection of the switch, the removal of the temperature detection device 1 from the ear, etc.) (step S104). If it is determined that the measurement is not completed ("No" in step S104), the CPU 13 returns to step S102.

[0063] When it is determined that the measurement is finished ("Yes" in step S104), CPU13 excludes the data of outliers from the arrangement of the stored measured temperatures (step S105). Regarding outliers, for example, the standard deviation of multiple measured temperatures within a certain period can be used to detect the measured temperature with a deviation larger than the deviation of the benchmark, and the median absolute deviation (Hampel identifier, etc.) can also be used to detect the measured temperature with a deviation larger than the benchmark. The above-mentioned erroneous data is also included in the outliers. The measured value of the detected outlier is replaced with the interpolation value of the previous and next data (it may not be 1 data before and after each data), the neighborhood median, etc.

[0064] CPU13 calculates the change dT1(t) of temperature T1(t) and the change dT2(t) of temperature T2(t) from the arrangement of the measured data (step S106). CPU13 determines whether each of the obtained groups of time-varying data satisfies the above-mentioned conditions A and B, and extracts the data of the group of changes that satisfy conditions A and B (dT1(ts), dT2(ts)) (collectively referred to as estimated temperature data) (step S107; acquisition unit, acquisition step). CPU13 calculates and acquires the thermal resistance ratio a based on the extracted data through formula (6) (step S108; parameter acquisition unit, parameter acquisition step).

[0065] The CPU 13 uses the obtained thermal resistance ratio a to calculate the estimated value of the deep temperature Tb(t) at the measurement timing of each set of temperatures T1(t) and T2(t) by equation (3) (step S109; temperature estimating means, temperature estimating step). Then, the CPU 13 ends the deep temperature estimation process.

[0066] In addition, CPU 13 may send historical record data of a group of output temperatures T1(t), T2(t) and deep temperature Tb(t) to an external device via the communication unit 15 before the end of the processing, or may send the output historical record data only when there is a communication command through manual operation, etc.

[0067] In this way, the thermal resistance ratio a is statistically obtained as a single parameter based on the historical record data of the detected and obtained temperatures T1(t) and T2(t), and the deep temperature Tb(t) corresponding to the group of temperatures T1(t) and T2(t) is also obtained through the thermal resistance ratio a.

[0068] [Modifications]

[0069] As a variation 1 of the deep temperature estimation processing of the present embodiment, regardless of the above, even in the middle of the measurement time, the thermal resistance ratio a and the deep temperature Tb(t) at each moment when the temperatures T1(t) and T2(t) have been obtained so far are summarized and estimated, and the deep temperature Tb(t) is then estimated in real time together with the temperatures T1(t) and T2(t).

[0070] Figure 4 This is a flowchart showing Modification 1 of the deep temperature estimation process.

[0071] The deep temperature estimation process of this modification example 1 is executed in real time within a range in which the deep temperature can be estimated. Therefore, this deep temperature estimation process is preferably used when the measurement result is output to an external device in real time and displayed.

[0072] The deep temperature estimation process of this modification example is Figure 3 The deep temperature estimation process of the illustrated embodiment adds steps S111 to S114. The other processes are the same, and the same reference numerals are used for the same process contents, and detailed descriptions thereof are omitted.

[0073] When the measured data is stored in the storage unit 14 in the process of step S103, the CPU 13 determines whether the reference number of measurement data is stored in the storage unit 14 (step S111). If it is determined that the reference number of measurement data is not stored (No in step S111), the CPU 13 proceeds to step S104.

[0074] When it is determined that the storage unit 14 stores measurement data of the reference number (or more) ("Yes" in step S111), CPU13 determines whether the thermal resistance ratio a has been calculated (step S112). When it is determined that the thermal resistance ratio a has not been calculated ("No" in step S112), the processing of CPU13 is transferred to step S105. When it is determined that the thermal resistance ratio a has been calculated ("Yes" in step S112), CPU13 calculates the estimated value of the deep temperature Tb(t) based on the thermal resistance ratio a and the measurement data stored in the most recent step S103 (step S113). As described above, CPU13 can transmit the estimated value of the deep temperature Tb(t) to an external device through the communication unit 15. Then, the processing of CPU13 is transferred to step S114.

[0075] After executing the processes of steps S105 to S109 , the process of the CPU 13 also shifts to step S114 .

[0076] If the process moves to step S114, the CPU 13 determines again whether the measurement (measurement time, measurement time period) is completed (step S114). If it is determined that the measurement is not completed ("No" in step S114), the CPU 13 returns to step S102. If it is determined that the measurement is completed ("Yes" in step S114), the CPU 13 ends the deep temperature estimation process.

[0077] In addition, as a variation 2 of the deep temperature estimation processing of the present embodiment, instead of calculating a more reasonable thermal resistance ratio a based on the overall extracted temperature changes dT1(ts) and dT2(ts), the thermal resistance ratio a(t) can be calculated based on each temperature change dT1(t) and dT2(t), and a representative value of the obtained multiple (the number of groups of extracted temperature changes) thermal resistance ratios a(t) is determined as the thermal resistance ratio a.

[0078] In this case, as a representative value of the thermal resistance ratio a, for example, an average value or a median value may be set as the thermal resistance ratio a.

[0079] Alternatively, as a third variation of the deep temperature estimation process of the present embodiment, the statistical calculation method of the above embodiment may utilize the results of the individual calculations of the above variation 2. For example, the thermal resistance ratio a(t) is individually calculated based on the temperature changes dT1(t) and dT2(t) of the groups satisfying conditions A and B (the estimated temperature data of each of the multiple groups), and the vectors v(dT1(tsm)) and v(dT2(tsm)) of the temperature changes dT1(t) and dT2(t) of a(t) are calculated as outliers, thereby obtaining a statistically reasonable thermal resistance ratio a. The outliers may be identified by, for example, the standard deviation, the Hampel identifier, etc.

[0080] Figure 5 : is a flowchart showing the control procedure of the deep temperature estimation process according to the third modification.

[0081] The deep temperature estimation process of this modification example 3 is only in the deep temperature estimation process ( Figure 3 ) is different in that steps S121 and S122 are added. The other processes are the same as those in the deep temperature estimation process of the above-mentioned embodiment, and are indicated by the same reference numerals, and detailed descriptions are omitted.

[0082] If data satisfying conditions A and B related to deep temperature changes are extracted in the process of step S107 (step S107), CPU13 sets dTb(t)=0 for each temperature dT1(t) and dT2(t) to calculate the thermal resistance ratio a(t) (step S121). CPU13 determines the outlier of the obtained thermal resistance ratio a(t), and excludes the temperature dT1(t) and dT2(t) groups of the thermal resistance ratio a(t) for which the determined outlier is calculated from the data extracted in step S107 (step S122). Then, the process of CPU13 shifts to step S108.

[0083] Fig. 6A , Figure 6B It is a diagram showing a modification of the temperature detection device 1 .

[0084] like Fig. 6A As shown in FIG. 1 , the first measuring portion 11a of the temperature detection device 1a of the modification 4 can be a contact type thermometer similar to the second measuring portion 12, and can measure the temperature by contacting the wall surface of the ear. Since the measurement accuracy will be reduced if the measurement positions of the first measuring portion 11a and the second measuring portion 12 are too close in the depth direction of the ear hole, the insertion portion 10a is set to an appropriate length. In addition, the insertion portion 10a can be a shape having a thickness that allows the first measuring portion 11a near the front end to easily contact the wall surface of the ear.

[0085] Alternatively, you can Figure 6B As shown, the temperature detection device 1b of the modification 5 does not necessarily have to be inserted into the ear to measure the temperature. Here, the first measuring part 11b, the second measuring part 12b, the CPU 13, the storage part 14 and the communication part 15 are located inside or on the surface of a thin structure 10c attached to the body surface K (for example, the forehead, etc.). The measurement site (first site) of the first measuring part 11b is located at a position in contact with or close to the body surface K, and the measurement site (second site) of the second measuring part 12b is set farther from the body surface K (that is, the measurement site of the deep temperature) than the measurement site (first site) of the first measuring part 11b.

[0086] Even with such a temperature detection device 1b, the deep temperature can be estimated by calculating the thermal resistance ratio a to the thermal resistance Rb inside the body without determining the thermal resistance Rs of the structure 10c which changes due to the influence of the external environment, body temperature, sweat, etc.

[0087] As described above, the temperature detection device 1 of the present embodiment comprises: a first measuring unit 11, which measures the temperature T1(t) of a first part P1 of a biological body as an object having a heat source; a second measuring unit 12, which measures the temperature T2(t) of a second part P2 farther from the heat source than the first part P1; and a CPU 13. The CPU 13 obtains a thermal resistance parameter (thermal resistance ratio a) representing the relationship (ratio) between the thermal resistance Rb and the thermal resistance Rs in a thermal model (excluding the capacity component), based on the temperature T1(t) and the temperature T2(t) obtained under a state satisfying a certain condition A, B (the condition mentioned here does not mean being limited to a single condition) corresponding to a certain change state of the temperature of the heat source, here dTb(t)=0, (step S107), wherein the thermal model includes the heat source, the first part P1 and the second part P2, and the thermal resistance Rb and the thermal resistance Rs are measured. The relationship between the temperature (Tb(t), T1(t)) and the heat flow (Ib(t)) between the source and the first part P1 is represented by the thermal resistance Rb, and the relationship between the temperature (T1(t), T2(t)) and the heat flow (Is(t)) between the first part P1 and the second part P2 is represented by the thermal resistance Rs (step S108), and the temperature of the heat source is estimated based on the obtained thermal resistance parameters, temperature T1(t) and temperature T2(t) as the deep temperature Tb(t) of the object (organism) (step S109).

[0088] In this way, the thermal resistance ratio a as a single parameter can be estimated based on the measurement data under a certain condition without determining any of the thermal resistances Rs and Rb, and the deep temperature Tb(t) can be estimated based on the temperature measurement of two points. Therefore, it is not necessary to increase the number of measurement points, and the deep temperature Tb(t) can be obtained more easily. In addition, by obtaining the thermal resistance ratio a appropriately for each measurement period, etc. and continuously updating it, it is possible to easily cope with changes in the measurement conditions of the thermal resistances Rs and Rb, and it is easy to estimate a more accurate deep temperature Tb(t) corresponding to the changes in the measurement conditions.

[0089] In addition, the above-mentioned certain conditions include: condition A, the absolute value of the change amount dT1(t) of the detected temperature T1(t) is greater than a certain first threshold value; and condition B, the change amount dT2(t) of the detected temperature T2(t) has the same sign as the change amount dT1(t), and the absolute value of the change amount dT2(t) is greater than the absolute value of the change amount dT1(t). That is, in the thermal model of the present embodiment that does not consider heat capacity and only characterizes the thermal circuit by thermal resistance, if the ambient temperature Te(t) changes during the period when the temperature change amount dTb(t) of the deep temperature Tb(t) is zero, the influence corresponding to the change is transmitted to both the first part P1 and the second part P2 as the same tendency, and the influence on the temperature T2(t) of the second part P2 closer to the external environment is greater. In this way, by using measurement data that satisfies the temperature conditions produced when the temperature change dTb(t) = 0 and the change in ambient temperature Te(t) is non-zero, the thermal resistance ratio a, which is an unknown parameter, can be obtained with a certain degree of accuracy without directly measuring the deep temperature Tb(t).

[0090] In addition, a certain change state of the temperature of the heat source may be a state without a time change of the temperature of the heat source (dTb(t)=0). By determining the above conditions A and B as necessary conditions for this condition and extracting the measurement data, the thermal resistance ratio a can be easily calculated as the only unknown number.

[0091] In particular, CPU 13 can obtain estimated temperature data (temperature changes dT1(t) and dT2(t)) related to a plurality of groups of temperature T1(t) and temperature T2(t), and obtain a more reasonable thermal resistance ratio a based on the estimated temperature data (step S108). As mentioned above, since the extraction conditions of the group of temperatures T1(t) and T2(t) are not sufficient conditions for dTb(t)=0, data in which the temperature change dTb(t) deviates from zero to some extent will also be mixed in. Even with such an incomplete group of data, the accuracy of the more reasonable thermal resistance ratio a can be appropriately improved by aggregating a plurality of them and processing them statistically, so that the deep temperature Tb(t) can be estimated with good accuracy.

[0092] Alternatively, CPU 13 obtains a thermal resistance ratio a(t) (step S121) for each of the estimated temperature data, i.e., the temperature change dT1(t), dT2(t) for multiple groups satisfying conditions A and B, and obtains a more reasonable thermal resistance ratio a (step S108) based on the estimated temperature data excluding the groups of the temperature change dT1(t), dT2(t) of the estimated thermal resistance ratio a(t) that do not satisfy a predetermined benchmark and are set as outliers among the multiple thermal resistance ratios a(t) obtained (step S122).

[0093] In this way, by excluding the groups of changes dT1(t) and dT2(t) of the thermal resistance ratio a(t) that are estimated to satisfy the conditions A and B but are significantly different from the other groups of changes dT1(t) and dT2(t), a statistically reasonable thermal resistance ratio a is obtained again, and the thermal resistance ratio a can be obtained more accurately. As a result, the deep temperature Tb(t) can be estimated with better accuracy in the temperature detection device 1b.

[0094] In addition, the thermal resistance parameter is the ratio of the thermal resistance Rb to the thermal resistance Rs, that is, the thermal resistance ratio a. As shown in the above formula (3), the thermal resistance ratio a is the best parameter to express the relationship between the thermal resistance Rb and the thermal resistance Rs. By obtaining the thermal resistance ratio a as shown in formula (7), the deep temperature Tb(t) can be easily estimated and calculated.

[0095] In addition, the temperature detection method of the present embodiment is a temperature detection method performed by a first measuring unit 11 that measures a temperature T1(t) of a first part P1 of an object (biological body) having a heat source and a second measuring unit 12 that measures a temperature T2(t) of a second part P2 farther from the heat source than the first part P1, and obtains a characteristic (excluding the characteristic) temperature T1(t) and a temperature T2(t) (step S107) obtained under a state satisfying a certain condition A and a certain change state (dTb(t)=0) of the temperature (deep temperature Tb(t)) of the heat source based on the temperature T1(t) and the temperature T2(t) obtained under a state satisfying a certain condition A and a certain condition B corresponding to a certain change state (dTb(t)=0) of the temperature (deep temperature Tb(t)) of the heat source. A thermal resistance parameter (thermal resistance ratio a) of the relationship between the thermal resistance Rb and the thermal resistance Rs in a thermal model (capacity component), wherein the thermal model includes a heat source, a first part P1 and the second part, the heat flow between the position of the heat source (measurement part Pb) and the first part P1 is characterized by the thermal resistance Rb, and the heat flow between the first part P1 and the second part P2 is characterized by the thermal resistance Rs (step S108), and the temperature of the heat source is estimated based on the estimated thermal resistance parameter, temperature T1(t) and temperature T2(t), as the deep temperature Tb(t) of the object (step S109).

[0096] Thus, in the temperature detection method of the present embodiment, the deep temperature Tb(t) can be estimated by appropriately obtaining a parameter indicating the correlation between the thermal resistances Rs and Rb in each measurement period without determining the thermal resistances Rs and Rb. Therefore, in this temperature detection method, the deep temperature Tb(t) which cannot be directly measured can be obtained more stably.

[0097] In addition, by installing the program 141 of this embodiment on a computer that can receive (acquire) data from the first measuring unit 11 and the second measuring unit 12, so that it executes the various processes involved in the above-mentioned temperature detection method, the deep temperature Tb(t) can be estimated more easily and stably based on the measured temperatures at two points without using special hardware.

[0098] In addition, the present invention is not limited to the above-mentioned embodiment, and various modifications can be made.

[0099] For example, in the above-mentioned embodiment, as necessary conditions for the temperature change dTb(t) of the deep temperature Tb(t) to become zero, two conditions A and B are exemplified, but other necessary conditions may be added on this basis or in place of it. It is also possible to determine the conditions within the range of a group of measurement data that can be extracted to obtain the thermal resistance ratio a in continuous temperature measurement, so that the temperature change dTb(t) becomes zero more reliably. In addition, it is also possible not to be a simple necessary condition, but to set the condition so that it becomes a necessary and sufficient condition. In addition, in the case where the measurement results of a biological measuring device other than the temperature detection device 1 can be obtained, for example, in the case of pulse (heart rate) measurement, the condition of dTb(t) = 0 can be further added to the condition that the pulse is less than a given reference value (non-rapid eye movement sleep, etc.) independently of the above-mentioned conditions A and B.

[0100] On the other hand, as long as the temperature change amount dTb(t) is more reliably zero, the number of sets of measurement data used to obtain the thermal resistance ratio a can be reduced (at least one set).

[0101] In the above embodiment, the state requirement of satisfying dTb(t)=0 is determined, but the above method involving estimation of the thermal resistance ratio a and the deep temperature Tb(t) can also be applied in principle by using the requirement of satisfying other states.

[0102] In addition, in the above-mentioned embodiment, after obtaining the measurement data successively, it is summarized to determine whether the conditions A and B are satisfied, and the thermal resistance ratio a is obtained by the data satisfying these conditions A and B, but the present invention is not limited to this. It is also possible to determine whether each condition A and B are satisfied when obtaining the measurement data and set a flag, etc., and extract and use the data used to obtain the thermal resistance ratio a based on the flag at the stage where the required data can be obtained.

[0103] Furthermore, although the thermal resistance ratio a is described as a single parameter to be obtained, any parameter may be used as long as the relationship between the thermal resistances Rs and Rb can be determined, and the present invention is not limited to this.

[0104] In addition, in the above-mentioned embodiment, it is described that the estimation of the thermal resistance ratio a and the deep temperature Tb(t) are all performed in the temperature detection device 1, but it is not limited to this. After the measurement data of the temperatures T1(t) and T2(t) are forwarded to an external device, the thermal resistance ratio a and the deep temperature Tb(t) can be estimated in the external device or the like. In this case, the deep temperature Tb(t) can be confirmed in the external device, or it can be returned to the temperature detection device 1 again via the communication unit 15 and confirmed in the temperature detection device 1.

[0105] In addition, in the above description, as a computer-readable medium storing the program 141 involved in the control of the deep temperature estimation process of the present invention, the storage unit 14 composed of a non-volatile memory such as a flash memory is cited as an example for description, but it is not limited to this. As other computer-readable media, other non-volatile memories such as MRAM, HDD (Hard Disk Drive), CD-ROM, DVD disk and other removable storage media can be used. In addition, as a medium for providing the data of the program involved in the present invention via a communication line, a carrier wave is also used in the present invention.

[0106] In addition, the specific structures, contents of processing operations, steps, etc. shown in the above-mentioned embodiments can be appropriately changed within the scope not departing from the gist of the present invention.

[0107] Although several embodiments of the present invention have been described, the scope of the present invention is not limited to the above-described embodiments, but includes the scope of the invention described in the claims and the scope equivalent thereto.

[0108] The inventions described in the claims first attached to the application form of the present application are described in the appendix below. The numbers of the claims described in the appendix are the same as those in the claims first attached to the application form of the present application.

Claims

1. A temperature detection device, characterized in that: have: a first temperature sensor for measuring a first temperature of a first portion of an object having a heat source; a second temperature sensor for measuring a second temperature of a second portion farther from the heat source than the first portion; and Processing Department, The processing unit performs the following processing: acquiring estimated temperature data for a plurality of groups including the first temperature measured by the first temperature sensor in a state where a predetermined condition corresponding to a certain change state of the temperature of the heat source is satisfied, and the second temperature measured by the second temperature sensor in a state where the certain condition is satisfied, Based on the estimated temperature data respectively involved in the obtained plurality of groups, relatively reasonable thermal resistance parameters representing the relationship between the first thermal resistance and the second thermal resistance in the thermal model are respectively obtained, wherein the thermal model includes the heat source, the first part, and the second part, the relationship between the temperature and the heat flow between the heat source and the first part is represented by the first thermal resistance, and the relationship between the temperature and the heat flow between the first part and the second part is represented by the second thermal resistance, Based on the estimated temperature data related to the plurality of groups excluding the group estimated to have a value that does not satisfy a predetermined reference among the plurality of thermal resistance parameters obtained, relatively reasonable thermal resistance parameters are obtained, Based on the acquired thermal resistance parameter and the measured first and second temperatures, the temperature of the heat source is estimated as the deep temperature of the object.

2. The temperature detection device according to claim 1, characterized in that: The certain condition includes: a first condition, that the absolute value of the measured first change in the first temperature is above a first threshold value; and a second condition, that the measured second change in the second temperature has the same sign as the first change, and the absolute value of the second change is greater than the absolute value of the first change.

3. The temperature detection device according to claim 2, characterized in that: The certain change state of the temperature of the heat source is a state in which there is no temporal change in the temperature of the heat source.

4. The temperature detection device according to claim 1, characterized in that: The thermal resistance parameter is a ratio of the first thermal resistance to the second thermal resistance.

5. A temperature detection method, comprising: using a first temperature sensor for measuring a first temperature of a first portion of an object having a heat source and using a second temperature sensor for measuring a second temperature of a second portion farther from the heat source than the first portion, wherein: acquiring estimated temperature data for a plurality of groups including the first temperature measured by the first temperature sensor in a state where a predetermined condition corresponding to a certain change state of the temperature of the heat source is satisfied, and the second temperature measured by the second temperature sensor in a state where the certain condition is satisfied, Based on the estimated temperature data associated with each of the plurality of groups, relatively reasonable thermal resistance parameters representing the relationship between the first thermal resistance and the second thermal resistance in the thermal model are obtained, respectively. The thermal model includes the heat source, the first portion, and the second portion, the relationship between the temperature and the heat flow between the heat source and the first portion is characterized by the first thermal resistance, and the relationship between the temperature and the heat flow between the first portion and the second portion is characterized by the second thermal resistance, Based on the estimated temperature data related to the plurality of groups excluding the group estimated to have a value that does not satisfy a predetermined reference among the plurality of thermal resistance parameters obtained, relatively reasonable thermal resistance parameters are obtained, The temperature of the heat source is estimated as the deep temperature of the object based on the acquired thermal resistance parameter and the measured first temperature and second temperature.

6. The temperature detection method according to claim 5, characterized in that: The certain condition includes: a first condition, that the absolute value of the measured first change in the first temperature is above a first threshold value; and a second condition, that the measured second change in the second temperature has the same sign as the first change, and the absolute value of the second change is greater than the absolute value of the first change.

7. The temperature detection method according to claim 6, characterized in that: The certain change state of the temperature of the heat source is a state in which there is no temporal change in the temperature of the heat source.

8. The temperature detection method according to claim 5, characterized in that: The thermal resistance parameter is a ratio of the first thermal resistance to the second thermal resistance.

9. A recording medium having a program recorded thereon, the program causing a computer that obtains measurement results from a first temperature sensor that measures a first temperature of a first portion of an object having a heat source and a second temperature sensor that measures a second temperature of a second portion farther from the heat source than the first portion to function as the following means: an acquisition unit that acquires a plurality of sets of estimated temperature data related to the first temperature measured by the first temperature sensor in a state where a predetermined condition corresponding to a certain change state of the temperature of the heat source is satisfied, and the second temperature measured by the second temperature sensor in a state where the certain condition is satisfied; A parameter acquisition unit acquires relatively reasonable thermal resistance parameters representing the relationship between the first thermal resistance and the second thermal resistance in the thermal model based on the estimated temperature data associated with each of the plurality of groups acquired, wherein: The thermal model includes the heat source, the first part, and the second part, the relationship between the temperature and the heat flow between the heat source and the first part is represented by the first thermal resistance, and the relationship between the temperature and the heat flow between the first part and the second part is represented by the second thermal resistance, and relatively reasonable thermal resistance parameters are obtained based on the estimated temperature data related to the plurality of groups excluding the group in which the values ​​not satisfying a predetermined reference are estimated among the plurality of thermal resistance parameters obtained; and The temperature estimating unit estimates the temperature of the heat source as the deep temperature of the object based on the acquired thermal resistance parameter, the measured first temperature, and the second temperature.

Images