JP6675552B2 - Deep body temperature estimation device, method and program - Google Patents

Description

  The present invention relates to a technique for estimating a core body temperature under a load-varying exercise.

  BACKGROUND ART In recent years, the occurrence of heat stroke indoors of elderly people and heat stroke during sports such as club activities has become a problem. The body temperature (deep body temperature) in the deep part of a person's body rises due to the heat environment or heavy exercise, but the body usually uses heat from body temperature regulation functions such as sweating and blood supply to the skin to prevent the body temperature from rising excessively. Release to the body. However, those who are not accustomed to the heat, such as early summer, and the elderly often have insufficient thermoregulatory functions, which increases the risk of developing heat stroke. Therefore, grasping the core body temperature, adjusting the air conditioning and supplying water are effective in preventing heat stroke. Generally, in order to measure the core body temperature, it is necessary to measure the temperature of a site such as the rectum and the eardrum, but it is difficult to continuously measure the temperature in daily life and during exercise. For this reason, it is common to measure the axillary temperature and the oral cavity temperature as a reference value of the core body temperature, but resting conditions are indispensable for the temperature measurement of these sites.

  On the other hand, as products for measuring (estimating) deep body temperature other than rectum and eardrum temperature, there are an oral capsule-type deep body temperature sensor, a head-mounted type forehead temperature sensor, a forehead pasting-type deep body temperature sensor, and the like. However, the cost associated with the receiver and the disposable capsule is a problem with the oral sensor, and the estimation accuracy of the core body temperature under the influence of wind and solar radiation may be reduced with the head-mounted sensor. In addition, the forehead type sensor is for medical use and is not intended for use during exercise. As described above, there is no low-cost core temperature sensor available during exercise.

  Incidentally, for example, a two-node model of Gagge is known as a device for estimating the core body temperature during exercise (Non-Patent Document 1). That is, the body temperature during exercise is estimated by applying the measured values obtained from the wearable sensor that can be worn during exercise and the sensor installed in the environment to the two-node model of Gagge. Gagge's two-node model can physically simulate the production of heat, heat transfer inside the body, and heat release outside the body. Furthermore, a method of applying six types of individual difference parameters to improve the accuracy of the two-node model has been proposed (Non-Patent Document 2). In this method, the estimation accuracy of the model is estimated by parameterizing the two thermoregulatory functions of sweating and increasing skin blood flow, and optimizing the individual difference parameters based on the measurements of seven points of skin temperature and rectal temperature for 120 minutes. Improvements have been reported. However, a 120-minute resting state is required for adjusting the parameters, which imposes a heavy burden on the user. Further, it is difficult to measure a total of eight temperatures during exercise, and the applicable range is limited to a resting state.

  In addition to Non-Patent Document 1, several models that physically formulate heat production in the body, heat transfer in the body, and heat exchange with the outside of the body have been proposed so far in order to evaluate changes in human body temperature. (Non-Patent Documents 3 and 4). Non-Patent Document 3 describes a 25-node model in which a human body is divided into six parts, that is, left and right arms, left and right legs, a torso, and a head, and each part is further divided into four layers: deep, muscle, fat, and skin. The heat calculation is performed at 25 sites obtained by adding the blood flow to the 24 sites. Non-Patent Document 4 is a 65-node model, and it is possible to simulate a detailed body temperature change for each part by more detailed division. In a model with a large number of divisions, while a more accurate biological reaction can be reproduced, there is a problem that there is much information to be input.

  In order to solve this problem, the inventors have proposed a method of adjusting individual difference parameters that can be applied during exercise using a wearable sensor (Non-Patent Documents 5 and 6). In this proposed method, for the simulation of body surface temperature with 3,200 individual difference parameters, the parameters are adjusted by finding the parameter that best matches the measured body surface temperature obtained from the wearable sensor. It has been confirmed that this method can improve the estimation accuracy of the model by monitoring the body surface temperature for about 40 minutes.

Gagge, A .: An effective temperature scale based on a simple model of human physiological regulatory response, ASHRAETRANSACTIONS, Vol. 77, No. 2192, pp. 247-262 (1971). Takada, S., Kobayashi, H. and Matsushita, T .: Thermal model of human body fitted with individualcharacteristics of body temperature regulation, Building and Environment, Vol. 44, No. 3, pp. 463-470 (2009). Stolwijk, J.A .: A mathematical model of physiological temperature regulation in man, Technical Report CR-1855, National Aeronautics and Space Administration (NASA) (1971). Tanabe, S., Kobayashi, K., Nakano, J., Ozeki, Y. and Konishi, M .: Evaluation of thermal comfort using combinedmulti-node thermoregulation (65MN) and radiation models and computational fluiddynamics (CFD), Energy and Buildings , Vol. 34, No. 6, pp. 637-646 (2002). Takashi Hamaya, Akira Uchiyama, Teruo Higashino, "Modeling of solar radiation and proposal of parameter adjustment method in biological thermal model using wearable sensor, multimedia, dispersion, coordination and mobile" (DICOMO2015) Symposium, Information Processing Society, pp.381-391 (2015). Takashi Hamaya, Akira Uchiyama, Teruo Higashino, "A Method of Deep Body Temperature Estimation in Hot Environment Using Wearable Sensor and Biothermal Model", Transactions of Information Processing Society of Japan, Vol. 56, No. 10, pp. 2033- 2043 (2015).

  However, it has been clarified that the parameter adjustment method based on body surface temperature shown in Non-Patent Documents 5 and 6 makes it difficult to adjust parameters for a load-varying exercise.

  Therefore, the present inventors assumed a real sports environment and the like, and based on the fragmentary measurement of the core body temperature related to warm-up, a method of selecting a parameter set applicable to exercise with load fluctuations. Devised. In addition, the model was applied to the model, including a delay parameter that considers the delay of heat transfer, which reproduces the response of sweating and blood flow in the transition period such as immediately after the start of exercise or immediately after the start of a break.

  Accordingly, the present invention provides a deep body temperature estimating device, a method and a program thereof, which can improve the accuracy of estimating the deep body temperature even for exercise in which the load fluctuates including a break.

  The deep body temperature estimating apparatus according to the present invention uses a living body heat model, and calculates heat transfer in the body and heat exchange with the outside of the body by a heat balance calculation formula from heat production in the deep part obtained based on the measurement by the sensor. In the deep body temperature estimating apparatus for estimating the deep body temperature during exercise by repeatedly calculating, individual difference parameters in which a plurality of types and a plurality of values are prepared for each, and a plurality of individual difference parameters for the heat transfer and the heat exchange, respectively. The delay value is set to the prepared delay parameter, and the measured initial core body temperature and the initial skin temperature are input, and a combination of all the parameters is applied to the heat balance calculation formula in the warm-up stage in parallel calculation. Warm-up parallel operation means to be performed, and compare the deep body temperature measured at the end of the warm-up with each result of the parallel calculation to determine an optimal parameter set. And selecting means for output, by applying the parameter set of the optimal running the heat balance equation, in which a monitor calculation means for calculating the core temperature.

  Further, the method for estimating a core body temperature according to the present invention uses a body temperature model to calculate heat transfer in the body and heat exchange with the outside of the body from heat production in the deep part obtained based on the measurement by the sensor. By repeatedly calculating in the formula, in the core body temperature estimation method of estimating the core body temperature during exercise, a plurality of types and individual difference parameters prepared for a plurality of values for each, for the heat transfer and the heat exchange A plurality of delay values each set a prepared delay parameter, and input the measured initial core body temperature and the initial skin temperature, apply the combination of all parameters to the heat balance calculation formula in the warm-up stage. The warming-up parallel operation step for performing the parallel calculation, and comparing the deep body temperature measured at the end of the warming-up with each result of the parallel calculation to determine the optimal parameters. A selection step of selecting a set, by applying the parameter set of the optimal running the heat balance equation, in which a monitor calculating step of calculating the core temperature.

  The program according to the present invention uses a living body heat model, and by a computer, repeatedly calculates heat transfer in the body and heat exchange with the outside from the heat production in the deep part obtained based on the measurement by the sensor using a heat balance calculation formula. By doing so, in a program for estimating core body temperature during exercise, a plurality of types of individual difference parameters are prepared for each, and a plurality of delay values are prepared for the heat transfer and the heat exchange. Warming-up parallel computing means for setting the set delay parameters and inputting the measured initial core body temperature and initial skin temperature, and applying a combination of all parameters to the heat balance calculation formula in the warming-up stage to perform parallel calculations. , Comparing the core body temperature measured at the end of the warm-up with each result of the parallel calculation and the optimal parameters Selecting means for selecting a and the by applying the optimal set of parameters to perform the heat balance equation, a monitor calculation means for calculating the core temperature, in which causes the computer to function as.

  According to these inventions, by using the warm-up period, the estimated value and the true value of the core body temperature are compared and evaluated, and the optimal parameter set is selected from a combination of a large number of individual difference parameters including the delay parameter. Therefore, in the subsequent estimation of the core body temperature during exercise, each situation at the time of exercise is reflected, and the estimation accuracy is improved. In addition, since the delay parameter is adopted and each situation at the time of the exercise is similarly reflected, the estimation accuracy is further improved even when the exercise or the rest with the load fluctuation is included.

  In addition, there is provided calculation means for calculating heat production information from heart rate information, wherein the sensor is mounted on a human body, measures a heart rate, and outputs the measured heart rate to the calculation means. According to this configuration, since the user only needs to wear the sensor for measuring the heart rate, there is little hindrance to the exercise.

  Further, the sensor transmits the measurement result to the calculation means wirelessly. According to this configuration, since the sensor is used separately from the deep body temperature estimation device, it does not affect the user's exercise.

Further, the core body temperature that is calculated by the monitor Starring Santé stage, if it exceeds a predetermined level, characterized by comprising a warning unit for issuing a warning. According to this configuration, since the parameter set closest to the actual measurement is selected to reproduce the actual reaction, a highly accurate prediction warning can be performed.

  ADVANTAGE OF THE INVENTION According to this invention, the estimation accuracy of core body temperature can be improved also with respect to the exercise | movement whose load fluctuates including a break.

It is a figure which shows the 2-node model of Gagge which is one Embodiment of the living body thermal model applied to the core body temperature estimation apparatus which concerns on this invention. It is a figure explaining consideration of solar radiation. It is a figure explaining a measured value of core body temperature and a simulation. 5A and 5B are diagrams showing distributions of optimal delay parameters, wherein FIG. 5A shows a delay parameter β1, and FIG. 5B shows a delay parameter β2. It is a functional block diagram showing one embodiment of a core temperature estimation device concerning the present invention. It is a figure explaining the outline of deep body temperature estimation concerning the present invention. It is a flowchart of a core body temperature estimation process. 7A and 7B are diagrams illustrating an experiment schedule, in which FIG. 7A is a running schedule diagram, and FIG. 7B is a walking schedule diagram. It is a chart which shows the average estimation error in walking. It is a chart which shows the average estimation error in running. It is a chart which shows transition of the average estimation error in walking. 5 is a chart showing a transition of an average estimation error in running. It is a table | surface which shows the average estimation error in exercise bike (trademark) exercise. 7A and 7B are diagrams illustrating a tennis experiment schedule, in which FIG. 7A is a schedule diagram on a first day, and FIG. It is a table | surface which shows the average estimation error on the 1st day of tennis. It is a table | surface which shows the average estimation error on the 2nd day of tennis. It is a table | surface which shows the example of a deep body temperature estimation on the 1st day. It is a table | surface which shows the example of a deep body temperature estimation on the 2nd day.

(1. Outline of the proposed method)
First, a body temperature simulation using a body temperature model will be described.

  FIG. 1 shows an outline of a two-node model of Gagge applied as an embodiment of a living body thermal model. In the two-node model, the human body is regarded as a sphere, and the human body is represented by two layers, an inner deep layer core and an outer skin layer skin. In the two-node model, heat transfer between the deep layer core, the skin layer skin, and the outside air is sequentially calculated, and the deep body temperature (the temperature of the deep layer core) and the skin temperature (the temperature of the skin layer skin) are simulated. In the simulation, the deep body temperature and skin temperature measured before the start of exercise (before warming up) are set as initial values, and thereafter, the heart rate measured by the wearable sensor 20 (see FIG. 5) that can be worn on the human body, and the environmental sensor The temperature, humidity, and the like measured in Step 12 (see FIG. 5) are input to the model, and heat calculation per unit time is repeated to estimate at least the temporal change of at least the core temperature among the core body temperature and skin temperature. Things. In this calculation, it is preferable to give the weight, the total skin area, the exercise type, and the thermal resistance of the clothes.

  Although the two-node model is the simplest biological heat model, it is possible to estimate the core body temperature with high accuracy by optimizing six types of individual difference parameters (Non-Patent Document 2). That is, in the two-node model, the amount of sweating and the amount of skin blood flow increase in accordance with the rise in body temperature from the reference core body temperature and skin temperature, but the amount of sweating and the amount of increase in skin blood flow also take into account individual differences. A coefficient that represents the degree of increase or decrease in is incorporated into the model formula. It is reported that the individual difference parameter improves the estimation accuracy of the core body temperature as compared with the case where the conventional two-node model is used.

  Similarly, the deep body temperature estimating apparatus 1 (see FIG. 5) according to the present invention improves the deep body temperature estimation accuracy by adjusting the individual difference parameter based on the actually measured deep body temperature. The deep body temperature estimating apparatus 1 estimates the deep body temperature of a user (body to be monitored for body temperature) during exercise (in addition to various sports, various daily and on-site work may be included). The metabolic rate is acquired from the wearable sensor 20 that can be worn on the body, and the temperature and humidity (at least the temperature) are acquired from the environment sensor 12 installed in the environment, and are sequentially input to the model. Further, since the energy released as work out of the body among the metabolic amounts varies depending on the type of exercise, the ratio of the energy used for work (external power) via the input unit 11 (see FIG. 5) is determined as the exercise type. Give each. Further, since the generation and release of heat increase and decrease in proportion to the body weight of the user's body and the total area of the skin, the user's weight and the total skin area are given to the model via the input unit 11 before the start of exercise. In addition, as the initial values for estimating the core body temperature and skin temperature, actual measurement of the core body temperature obtained from the core body temperature sensor 31 (see FIG. 5), which is an infrared type eardrum thermometer, and the skin temperature obtained from the wearable sensor 20 Give the value to the two-node model. Note that, in a mode in which the wearable sensor 20 is equipped with a thermometer, a mode in which the skin temperature is measured using the thermometer and the measurement result is transmitted to the model via the communication unit 201 (see FIG. 5) may be used. Alternatively, a mode in which the temperature is measured by a thermometer and input from the input unit 11 may be used.

  Further, for adjusting individual difference parameters, the core body temperature sensor 31 measures the core body temperature at the end of the warm-up or at the time of a break. For the measured core body temperature, a parameter set that minimizes the error between the actual measurement and the simulation is selected. With this method, it is possible to determine parameters that reflect individual differences and the condition of the day without having to constantly measure the core body temperature during exercise.

  Hereinafter, Chapter 2 describes a two-node model of Gagge and an individual difference parameter adjustment method, and Chapter 3 specifically describes an input method to a model and an individual difference parameter adjustment method in an assumed scenario. In Chapter 4, the functional configuration and processing of the core body temperature estimation device 1 will be described with reference to FIGS. Chapter 5 describes the improvement of the model formula that was made to adapt the two-node model to the sports environment. Then, in Chapter 6, an experiment is performed and an evaluation based on the data is performed.

(2. Gagge 2-node model)
(Overview)
The temperature change from time t to t + 1 is a two-node model, core temperature T _core ^t at time t, the input value from the wearable sensor 20 obtained at the skin temperature T _skin ^t, and time t, the layers with the outside air by calculating the heat exchange between obtain core temperature T _core ^t at time t, skin temperature T _skin ^t of variation [Delta] T _core ^t, the [Delta] T _skin ^t respectively. Based on the obtained change amount, the body temperature at time t + 1 is calculated by the following equation (A).

By repeating the above calculation from time 0 to time t−1, the _core body temperature and skin layer estimated series T _core ^t and T _skin ^{t up} to time ^t are obtained. As the initial values T _core ⁰ and T _skin ⁰ , actual measured values measured before exercise are input. Hereinafter, t representing time is omitted to explain the heat balance calculation method at time t.

(Heat calculation in deep layer)

Table 1 shows a thermal calculation formula in the deep layer. In the deeper layer, heat is generated by metabolism, the heat is transmitted to the skin layer by conduction and blood flow, and the process of being released outside the body by respiration and exercise (work) is reproduced by the formulas in the table. The _core body temperature change amount ΔT _core is calculated by the equation (a) in Table 1 using the heat balance Q _core , the deep layer mass m _core , and the specific heat c _core . Further, as shown in equation (b), the heat balance Q _core of the deep layer is determined by the total metabolism M, the external work W, the heat loss q _res due to respiration, the heat conduction q _cond to the skin layer, and the blood flow to the skin. Calculated as the sum of the heat q _blo delivered to the layer. Normally, during exercise, the metabolic amount exceeds heat release, so Q _core has a positive value. However, at the time of a break or the like, the _core value has a negative value, and the body temperature decreases from the equation (a). The details of the respective heat calculation formulas represented by the formulas (c) to (f) are known (Non-Patent Document 1). Each calculation formula uses the constants shown in Table 2.

(Calculation of heat in the skin layer)
Table 3 shows the formula for calculating the heat of the skin layer. Unlike the deep layer, no heat is generated in the skin layer, and the process of releasing the heat received from the deep layer is calculated. The heat q _cond and q _blo transmitted directly from the deep layer and transmitted by the blood flow are released into the air by sweating q _{rsw on} the skin surface, water evaporation q _diff , heat exchange with air q _conv , and heat radiation q _rad . Is done. The above heat balance Q _skin is obtained by equation (h) in Table 3. Based on the heat balance Q _skin in the skin layer, the skin temperature change amount ΔT _skin is obtained from the skin layer mass m _skin and the specific heat c _core (equation (g)). Note that individual calculation formulas are known (Non-Patent Document 1).

(Consideration of individual differences using variable parameters)
In the two-node model, as the core body temperature and skin temperature rise, the blood flow and the amount of perspiration are increased to release heat outside the body (Equation (f) in Table 1 and Equation (k) in Table 3). These thermoregulatory responses increase or decrease due to deviation from the reference body temperature. This reproduces the human body's reaction that the amount of perspiration and the amount of skin blood flow increase as the body temperature rises. By incorporating parameters that reflect individual differences into the body temperature regulation reaction equation, more accurate changes in body temperature can be reproduced. Formula (B) shows a formula for calculating the amount of perspiration and the amount of skin blood flow incorporating the individual difference parameter.

Wherein ^{_{(B), V blo t,}} m rsw t the skin blood flow at time t, respectively, represent the total amount of sweat, the α4 from α1 is an individual difference parameter, respectively. α1 and α2 represent the initial value of the skin blood flow and the rate of increase due to an increase in body temperature, respectively, and α3 and α4 represent the perspiration coefficient specific to exercise and the common perspiration coefficient during exercise and at rest, respectively. α3 is a parameter that is uniquely incorporated with reference to the equation proposed by Gagge's two-node model. Further, in the two-node model Gagge, but defines a body temperature as a reference uniformly, where considered difference due individual difference and physical condition exists for the reference body temperature, body temperature T _core ⁰ motion before the start of the actual measurement of the day , T _skin ⁰ are given to the formula as reference body temperatures. Table 4 shows the ranges of the parameters determined so far for the individual difference parameters described above. That is, there are a total of 3,200 combinations for the four parameters.

(3. Method for estimating core body temperature during exercise)
(How to get input)

  For the body temperature simulation using the two-node model described in the previous chapter, four types of inputs are required: (1) user information such as weight, (2) initial body temperature, (3) metabolic rate, and (4) environmental conditions. . Table 5 shows all necessary input contents. Here, a method of applying these input values to the heat calculation formula in the model will be described. Table 6 shows equations used for conversion of each input.

User information such as height and weight is used for estimating the masses m _core and m _skin of the deep layer / skin layer and the total skin area A _body . It is known that the mass ratio of each layer is 95: 5 (Non-Patent Document 4), which is determined by the equations (1) and (2), respectively. The total skin area is estimated from formula (3) using height and weight. Further, the initial _core body temperature T _core ⁰ and the initial skin temperature T _skin ⁰ are respectively provided by an infrared radiation type eardrum thermometer MC-510 (manufactured by OMRON) as the core body temperature sensor 31 and a wristwatch-type wearable sensor Basis Peak (manufactured by Basis). Measurement is performed using the wearable sensor 20.

The total metabolic heat M in the model is estimated based on oxygen consumption. As shown by equation (4), the metabolic heat is considered as the sum of resting metabolic heat (basal metabolism) M _rest and metabolic heat M _ex due to exercise. Each metabolic heat [W] is calculated by equations (5) and (6). The terms 3.5 · weight and (VO2−3.5) · weight in these equations represent the resting state and oxygen consumption by exercise [ml / min], respectively. Assuming that 5 kilocalories of energy are generated per liter of oxygen consumption, multiply the oxygen consumption by 5/1000 to calculate the energy by metabolism. In addition, kilocalories per minute is converted to joules per second to convert to watts used in the two-node model.

  The oxygen consumption VO2 during exercise is estimated by equation (7). In equation (7), the oxygen consumption is calculated by multiplying the maximum oxygen consumption VO2max per kg of the user's body weight by the oxygen consumption% VO2R (what percentage of the oxygen intake capacity is used) with respect to the reserve oxygen intake. Find consumption. VO2max is obtained from the user's maximum heart rate maxHR and resting heart rate restHR (Equation (8)). Further, in order to estimate% VO2R from the heart rate, the conversion is performed by the equation (9) using the current heart rate% HRR with respect to the reserve heart rate (how much the maximum heart rate is increased). % HRR is calculated by the Carbonene method shown in equation (10). The maximum heart rate is estimated based on the age of the user (Equation (11)). With the above method, metabolic heat M is estimated based on the user's heart rate, resting heart rate, weight, and age.

  Further, the energy loss W due to external work is obtained by multiplying the exercise metabolism by a coefficient Δeff, as shown in Expression (12). Δeff is known for basic exercise types such as walking, running, cycling and the like. Therefore, Δeff = 0.40 (during walking), Δeff = 0.44 to 0.54 (during running), and Δeff = 0.23 (during cycling), respectively. During traveling, the energy efficiency changes depending on the speed, so a value of 0.44 to 0.54 is given according to the speed of the user. The environmental conditions are measured using a sensor WBGT-203B (manufactured by Kyoto Electronics Manufacturing Co) installed in the environment as the environment sensor 12.

(Parameter adjustment based on warm-up)
The inventors have proposed an adjustment method of an individual difference parameter using a body surface temperature that can be measured during exercise (Non-Patent Document 6). While this method can collect body surface temperature in real time, it requires a minimum of 40 minutes of observation, and it is difficult to estimate deep body temperature for exercises that fluctuate in load, and the accuracy improvement has been limited to a maximum of 12%. . In the present invention, the individual difference parameter is adjusted based on the fragmentary measured body temperature using the core body temperature sensor 31. In the assumed environment, sensor data from time 0 to t is obtained, and as an estimated series (C) of deep body temperature up to time t under the individual difference parameter group θi,

Get. Time tw is the time when the user has finished the warm-up exercise and measured the core body temperature with the core body temperature sensor 31. At this time, the true value of the core body temperature measured at time tw
Using T _core ^∧tw , an optimum individual difference parameter ^θopt is obtained by the following equation (D).

By giving ^θopt and T _core ^∧tw obtained by the above equation (D) as inputs, and performing simulation again from time tw + 1 onward, the _core body temperature estimation result T _core ^t (θopt ).

(4. Improvement of node model)
This chapter describes four types of model improvements made to apply the two-node model of Gagge to actual exercise, for example, a sports environment. Here, for example, the effects of solar radiation, wind, and drinking water, which are considered to have a large effect on changes in body temperature in the outdoor environment in the summer, are considered. Take into account the differences in response. Table 7 shows the above model improvement formula.

(Considering solar heat)
In an outdoor environment, the sun receives strong thermal energy from the sun and the skin temperature rises. However, the conventional two-node model does not consider the influence of solar radiation heat. Here, as shown in FIG. 2, the measured value of the pyranometer included in the environment sensor 12 installed in the environment, the total skin area of the user, and clothing are input, and the direct solar radiation qdn received by the user is estimated. Was incorporated into the heat balance calculation. qdn is obtained by the following equation (E).

  a represents the solar absorptivity of the skin, Ap represents the projected area of a person, and Jdn represents the observation value of the solar radiation sensor. Here, a = 0.6. In the second formula, the projected area of the person in the direction perpendicular to the solar radiation is obtained by multiplying the area Abody of the person, the uncovered ratio fcl by clothing, the effective radiation area coefficient feff, and the projection area coefficient fp. The coefficient values at the time of standing are defined as fp = 0.85 and feff = 0.725, respectively. Further, fcl can adopt a value determined as a clothing condition, for example, 0.4. By incorporating the above contents into the two-node model, solar radiation heat is considered.

(Considering wind)
In an outdoor environment, the wind increases the amount of air that touches the skin per unit time due to the wind, and as a result, the efficiency of heat dissipation due to perspiration increases. In the two-node model, the increase in the amount of perspiration due to the rise in body temperature can be reproduced, but the improvement in perspiration efficiency due to the wind cannot be reproduced. Sweat that did not evaporate in the model runs off and is considered not to contribute to heat release (ineffective sweating). We performed outdoor walking and jogging exercises and performed simulations using the collected data. As a result, it was confirmed that invalid sweating occurred in 1727 minutes of the exercise time of 3571 minutes. However, in reality, there is a natural wind and a relative wind generated by running, so that the actual sweating efficiency should be even better. On the other hand, in an outdoor environment, it is difficult to accurately observe a natural wind received by a user due to an influence of a building or the like. Therefore, here, the amount of heat loss due to perspiration is more accurately estimated by incorporating the relative wind generated when the user is moving into the model.

Since the convective heat transfer coefficient h _conv in the model increases and decreases depending on the speed of the person, this time the following equation (F)

Thus, the increase in the sweating efficiency due to the movement of the user is considered. In the equation, v represents the speed [m / s]. Since the walking speed and running speed in this exercise were 1.4 [m / s] and 2.5 [m / s], respectively, by applying to the above equation (F), h _conv was set to the standard value (4.3), respectively. In comparison, it is revised to 386% and 547%. As a result, the total amount of heat E _max (formulas (l) and l: L in Table 3) that can be released from the body by perspiration increases, and the heat loss due to perspiration increases.

(Deep body temperature decrease due to water intake)
It has been shown that there is a difference in body temperature change between when water is taken during exercise and when it is not, and water intake is indispensable for safety. Hydration, in addition to the effect of lowering body temperature due to the coldness of water itself, has a role in maintaining normal sweating function. In the two-node model, since the calculation is performed on the assumption that the sweating function always functions normally, it is unnecessary to consider the latter function. On the other hand, when a large amount of water lower than the body temperature is taken, a significant effect of lowering the body temperature can be expected. Therefore, heat transfer due to the temperature difference between moisture and the human body is added to the calculation formula of the model.

The water taken into the body will eventually rise to the same temperature as the human body. Accordingly, the core body temperature ΔT _water that decreases due to water intake is calculated based on the respective temperatures, masses, and specific heats of _water and the core, as represented by equation (16). By applying the equation (16) to the point of time when hydration is performed, a decrease in core body temperature due to moisture is considered.

(Consideration of thermal delay)
The inventors have found that there is a case where a difference may occur between the actual core body temperature and the simulation of the core body temperature by simulation from data collected by walking and running including a break. An example is shown in FIG. In the model, since the metabolic rate is calculated based on the heart rate measured by the wearable sensor 20, the heart temperature rises immediately after the start of exercise, so that the core body temperature also rises. Further, when a break is started, the heart rate decreases, and the core body temperature also decreases accordingly. On the other hand, from FIG. 3, the reaction of the actual core body temperature is delayed with respect to the simulation, and there is a tendency that the body temperature does not immediately rise even when the exercise is started, and the body temperature does not decrease for a while even after the break is started. .

  FIG. 3 also shows that immediately after the start of exercise, the core body temperature is lower than the initial value. Similar phenomena have been reported elsewhere. Although the cause is still unclear, the temperature of the blood accumulated in the muscle before the start of exercise may be lower than the temperature of the blood at the core, and the blood may circulate immediately after the start of exercise, which may lower the average blood temperature. It is said that there is. On the other hand, many of the collected samples did not show an initial drop in core body temperature. Therefore, it is difficult to define the initial drop in core body temperature physiologically and incorporate it into the model. Therefore, here, the initial drop of the core body temperature and the delay for the simulation of the core body temperature described above were considered by using a simple delay parameter.

  In this delay method, the heat calculation in the two-node model is delayed by, for example, two types of delay parameters β1 and β2. By controlling the propagation speed of heat M generated by metabolism, β1 takes into account the initial drop in core body temperature and further delays the decrease in body temperature during a break. β1 is incorporated in the model as the size of the sliding window, and the larger the value, the lower the speed of metabolic heat propagation. β2 represents a delay in the thermoregulatory response to an increase in body temperature. In the conventional model, sweating and blood flow immediately respond to an increase in body temperature, but the actual response is considered to be delayed. For example, sweat may come out without stopping for a while after intense exercise. Therefore, the body temperature regulation reaction is delayed by β2 minutes by using the body temperature before β2 minutes instead of the current body temperature in the calculation of perspiration and blood flow. By incorporating the above delay parameters into the model as shown in equations (b '), (17), (f'), and (k ') in Table 7, the estimation performance of the core body temperature is improved.

  Furthermore, since these delay parameters β1 and β2 are different for each user and are considered to be affected by physical condition, it is necessary to perform parameter adjustment in each exercise similarly to the individual difference parameter. Therefore, in order to determine an appropriate parameter range, first, [1,15] was set for β1 and [0,10] for β2, and parameter adjustment was performed based on actual measurements for a total of 165 combinations. At this time, the individual difference parameters α1 to α4 were fixed at standard values. As a result, optimal distributions of β1 and β2 were obtained for 34 samples, respectively, as shown in FIG.

  Since the parameter search range can be expected to have higher accuracy when it is wider, the number of parameter combinations becomes enormous when combined with the individual difference parameter. Therefore, it is necessary to determine the parameter range in consideration of the calculation time. Therefore, based on this distribution, [1,12] is set for β1 and [0,5] for β2 as sections covering the optimal solution of 80% or more. As described in section 3 (parameter adjustment based on warm-up), two delay parameters are adjusted simultaneously with the four individual difference parameters for the 72 combinations.

  Apply the improved equation described in Chapter 4 to the model. Specifically, the equations (a), (b), (f), (h), and (k) in the two-node model of Gagge are converted into the equations (a ′), (b ′), and (f) shown in Table 7. '), (H'), and (k '). In the evaluation in the following six chapters, the improved model is called an improved model, and a normal two-node model is called a normal model.

  As described above, in this improved method, the core body temperature before the start of exercise is given to the model as an initial value, and the core body temperature, temperature, humidity, and other temporal changes are input to the model to estimate the core body temperature. Furthermore, simulation results of deep body temperature during warming-up are generated comprehensively (by parallel calculation) for 230,400 individual difference parameter sets including delay parameters, and deep body temperature obtained by an infrared tympanic thermometer etc. after warming-up is completed. The actual response is reproduced by selecting the parameter set closest to the actual measurement by comparing the measured value of the above with the estimated value of the core body temperature in each parameter. In addition, in order to apply the two-node model to the sports environment, heat transfer by solar radiation, wind, and drinking water, and delay parameters that reproduce the response of sweating and blood flow during transitional periods, such as immediately after starting exercise and immediately after taking a break, must be set. Incorporated in a two-node model.

(5. Example)
FIG. 5 is a functional configuration diagram showing one embodiment of a deep body temperature estimating apparatus according to the present invention, FIG. 6 is a diagram for explaining an outline of deep body temperature estimation according to the present invention, and FIG. It is a flowchart of an estimation process.

  The deep body temperature estimation device 1 includes a control unit 10 configured by a computer, and stores an input unit 11, an environment sensor 12, an output unit 13, a RAM 14 as a work area, a control program, and the like connected to the control unit 10. The storage unit 15 is provided. The input unit 11 includes a numeric keypad, a mouse, a touch panel, and the like to which various kinds of information can be input from the outside. The output unit 13 is provided as needed, and is, for example, a display for displaying an image, a sound generator, or the like. The output unit 13 may be used for a warning. The environment sensor 12 is connected to the control unit 10 wirelessly or by wire, and periodically transmits various types of measurement information to the control unit 10 via the communication unit 121. The storage unit 15 stores, in addition to the control program, information on a biological temperature model to be used, various information applied to the model, various heat balance calculation formulas for temperature estimation, and various types of information necessary for execution of the control program. Parameters are stored.

  Further, a deep body temperature sensor 31 is connected to the control unit 10 as necessary. The core temperature sensor 31 may be wired or wireless, and is connected when the measurement result is taken into the control unit 10. Alternatively, the measurement result may be manually input via the input unit 11. Wearable sensor 20 continuously measures the user's heart rate during exercise, and wirelessly transmits to control unit 10 via communication unit 201. Note that in a mode in which the deep body temperature estimation device 1 is directly worn by a user, a wired type may be used. In addition, the wearable sensor 20 may output the heart rate, or may output the heart rate to the control unit 10 in the form of a metabolic rate.

  The deep body temperature estimating apparatus 1 performs heat transfer in the body and heat exchange with the outside of the body from heat production (metabolism) in the deep part calculated based on the measurement of the heart rate by the wearable sensor 20 using the body temperature model. The body temperature during exercise is estimated by repeatedly calculating the heat balance formula. The control unit 10 reads out the control program into the RAM 14 and executes the control program, whereby the input reception unit 101, the warm-up parallel operation unit 102, the parameter set selection unit 103, the monitor operation unit 104, and the warning processing unit 105 provided as necessary Function as

  The input receiving unit 101 receives measurement information from the wearable sensor 20 with a thermometer, the deep body temperature sensor 31, and the environment sensor 12 in addition to the input unit 11. The input receiving unit 101 takes in the skin temperature and the deep body temperature from the wearable sensor 20 and the deep body temperature sensor 31 before starting the warm-up, and sets them as the initial skin temperature and the initial deep body temperature. Further, the input receiving unit 101 continuously captures the heart rate from the wearable sensor 20 during the exercise after the warm-up is completed.

The warming-up parallel operation unit 102 prepares a plurality of types of individual difference parameters (α1, α2, α3, α4) for which a plurality of values are prepared, and a plurality of delay values for heat transfer and heat exchange, respectively. The delay parameters (β1, β2) are set, and the measured initial core body temperature and initial skin temperature are input, and all the parameter combinations θ _i (α1, α2, α3, α4, β1, Apply β2) to the heat balance equation and execute the parallel calculation repeatedly.

The parameter set selection unit 103 calculates the deep body temperature, which is the true value measured at the end of the warm-up, and the heat balance calculation results of all the parameter combinations θ _i (α1, α2, α3, α4, β1, β2) using an expression ( Applying (calculating) to D), the individual difference parameter is optimized, for example, the optimal parameter set θ _opt that minimizes the difference is selected.

The monitor calculation unit 104 performs a simulation calculation. During the exercise after the warming-up, the selected optimal parameter set θ _opt is applied to the heat balance calculation formula, and the heat balance calculation is repeatedly executed to calculate the core body temperature. Estimate and calculate. The monitor calculation unit 104 may estimate and calculate the skin temperature as needed.

  The warning processing unit 105 outputs a warning (display, warning sound, guide, or the like) and outputs a warning (e.g., a display, a warning sound, or a guide) when, for example, the core body temperature being calculated has risen to the threshold value for heat stroke determination during exercise after the end of the warm-up, for example, when the calculated core body temperature rises to a threshold value for heat stroke determination. To inform. The threshold value may be a fixed value, or may be set in consideration of a delay according to the set delay parameter, or may be set in consideration of a change gradient of a deep body temperature or the like. Good.

As shown in the flowchart of FIG. 7, in the mode of the deep body temperature estimation processing, steps S1 to S13 are during the warm-up period, that is, the selection period of the optimal parameter set θ _opt , and after step S15, the exercise monitoring ( Monitor) period.

First, an initial core body temperature and an initial skin temperature measured immediately before the start of warming-up are taken (step S1). Next, during the warm-up period, each heat balance calculation, that is, a parallel operation is executed for all parameter combinations θ _i (α1, α2, α3, α4, β1, β2) (steps S3 and S5). Note that the warming-up period may be set in advance according to the exercise type or the like, or may be appropriately set each time.

Then, when the warming-up period ends (Yes in step S5), the measurement of the deep body temperature is performed, and the measured depth temperature is captured (step S7). Then, it was estimated from the comparative evaluation, that is, the heat balance calculation result of the measured deep body temperature and the combination θ _i (α1, α2, α3, α4, β1, β2) of all the parameters calculated in parallel. Each core body temperature is applied to equation (D) and compared and evaluated (step S9). As a result, an optimal parameter set θ _opt is selected (step S11). Then, the selected optimum parameter set θ _opt is set in the heat balance calculation equation (step S13).

  Subsequently, during exercise, a deep body temperature estimation calculation (monitor calculation) is executed (step S15), while whether or not the deep body temperature exceeds a threshold is monitored (step S17). During the continuation (No in Step S19), the process returns to Step S15 to continue the same repeated calculation. On the other hand, if the core body temperature has exceeded the threshold, a warning is issued (step S21). The monitoring process is also continued when the estimated deep body temperature escapes (decreases) from the warning state due to a break after the warning and returns to exercise again (No in steps S15 to S19).

As described above, the estimated value of the core body temperature and the true value are compared and evaluated using the warm-up period, and the combination θ _i (α1, α2, α3, α4, β1, β2) of a large number of individual difference parameters is obtained. Since the optimal parameter set θ _opt is selected, in the subsequent estimation of the core body temperature during exercise, each situation at the time of exercise is reflected, and the estimation accuracy is improved. In addition, since the delay parameter is adopted and each situation at the time of the exercise is similarly reflected, the estimation accuracy is further improved even when the exercise or the rest with the load fluctuation is included. Although the delay parameter is employed in connection with heat transfer inside the body and heat exchange with the outside of the body, it may be a single type. Further, the living body heat model is not limited to the two-node model, and various models can be adopted as long as the heat balance calculation formula can be adopted. Even in this case, one kind or plural kinds of delay parameters may be adopted.

  In the case of a type in which the deep body temperature estimation device 1 is worn by a person exercising (including various sports and work), the wearable sensor 20 may be a wired type. Also, when monitoring a plurality of exercisers, the monitoring side, that is, the control unit 10 side is one, and it is possible to provide identification information to each exerciser and cope with the exercisers. Is also suitable.

  The warm-up may be an initial period of exercise or the like in addition to the literal warm-up.

(6. Performance evaluation: experiment)
(Evaluation environment)
The experiments were performed in four types of exercise: walking, running, exercise bike (registered trademark) exercise, and tennis to evaluate the improved model. The results of estimating core body temperature for the collected experimental data are described below. In all experiments, the subject wore the above-mentioned wristwatch-type wearable sensor Basis Peak to obtain heart rate and initial skin temperature. Furthermore, for the measurement of the true value of the core body temperature, in the case of exercise that is safe without contact between subjects such as walking, running, and exercise bike (registered trademark), the eardrum temperature can be continuously measured by inserting it into the ear. An infrared tympanic thermometer DBTL-2 was used. On the other hand, in tennis involving danger of contact, the aforementioned infrared tympanic thermometer MC-510, which can measure the eardrum temperature by the subject himself during a break, was used. Further, the above-mentioned WBGT-203B for measuring the temperature and humidity and the ML-01 (manufactured by EKO Instruments) for measuring the amount of solar radiation were installed in the environment. The subject wore the same clothes, and the heat resistance of the clothes was regarded as Clo = 0.6. The unit time for estimating body temperature using the two-node model was 1 minute.

  In this study, the average absolute error from tw at the end of warm-up to t at the end of exercise was used as an index of performance. The average absolute error is represented by the following equation (G).

In the equation (G), T _core ^∧i represents the measured _core body temperature at time i. The index represented by the equation (G) indicates how many degrees (° C.) the series of the actual core body temperature and the estimated core body temperature are apart on average.

  Furthermore, two methods, DEF and OPT, were considered as comparison methods. DEF is a method of estimating using the normal model and standard individual difference parameters, and OPT is a method of selecting a parameter that minimizes the average estimation error with respect to the measured core body temperature for the improved model. It is. That is, OPT represents the performance limit of error reduction by parameter adjustment according to the present improved method. Note that PROP is a proposed method of performing parameter adjustment using only one core body temperature at the end of warm-up.

  In addition, in order to evaluate the calculation time, the average calculation time for searching for the parameter with the minimum error from 230,400 parameters including the individual difference parameter and the delay parameter was calculated for a warm-up of 30 minutes. For the simulation, a computer equipped with a CPU clock frequency of 2.66 GHz and a memory of 23.6 GB was used. As a result, it was confirmed that parameter adjustment was possible in an average of 50 seconds.

(Deep body temperature estimation error in walking and running)

  Walking and running in the outdoor environment were performed for a total of 60 hours or more, and samples of a total of 34 people were collected and the average error was evaluated. Table 8 shows the experimental environment. The subjects were six males, whose mean, standard and standard deviations of age, height and weight were 22.8 ± 0.8, 173.5 ± 4.1 [cm] and 68.7 ± 8.1 [kg], respectively. Further, the subject performed one of walking and running exercises according to the schedule shown in FIG. 8 according to the individual physical condition. The course that actually traveled was a circuit with a circumference of 850 m on almost flat ground. In walking, three sets of three laps of the course were performed, and in running, four sets of three laps of the course were performed. In each of the exercises, a break of 10 minutes was performed between each set, and during the break, the subject hydrated up to 250 [ml] every time if necessary. The temperature of the ingested water was equivalent to room temperature (about 30 ° C.). In the subsequent evaluations, the first set and the rest were regarded as warming up, and the estimation accuracy of the core body temperature was evaluated for the subsequent exercise and rest.

  9 and 10 show the average absolute error in the walking exercise and the running exercise, respectively. From both results, it can be seen that the improved method improves the estimation accuracy over DEF. Further, it can be seen that the error at the time of running is generally larger than that at the time of walking. It is considered that the reason for this is that the exercise load was higher in running and the core body temperature fluctuated more drastically. Compared to DEF, the reduction rate of the error by the improved method is 13% during walking and 30% during running. In the normal model, it can be reproduced to some extent for walking with relatively small load, but running with high load In, the average estimation error exceeds 0.4 ° C, suggesting that estimation is difficult.

  FIGS. 11 and 12 show the change of the time-series error. In each figure, the horizontal axis represents the progress of the exercise, and the vertical axis represents the average absolute error in the section. For example, Rest2 represents the second break, and Walk3 represents the third set of walking. From this result, it can be seen that the error is almost always maximum in DEF. In addition, the error is particularly large during a break. The reason for this is that it was difficult to reproduce the change in body temperature immediately after a break in the normal model. On the other hand, PROP and OPT tend to have smaller errors at rest than at exercise. This is considered to be a result of appropriately considering the delay in lowering the body temperature when the user enters a break by introducing the delay parameter. However, PROP has a larger error than OPT during exercise. This result indicates that while measuring the deep body temperature at one point at the end of warming-up can roughly match the tendency of the parameters, it is necessary to measure more deep body temperature in order to accurately estimate the rise in body temperature. There is a suggestion.

(Estimation error of core body temperature in exercise bike (registered trademark) exercise)

  Further, data was collected indoors using an exercise bike (registered trademark) under the conditions shown in Table 9 when the load fluctuated finely. Seven subjects performed six 1-hour aerobike® exercises, collecting a total of 42 hours of data sets. Exercise load reproduced the increase and decrease of the load by switching the three-step change of 2.4 [W], 4.8 [W], 7.2 [W] every 5 minutes. In the exercise bike (registered trademark), a break was not performed, so that the first 25 minutes were regarded as a pseudo warm-up, and the subsequent 35 minutes were used as an evaluation data set. Water was not taken in all experiments.

  FIG. 13 shows the average absolute error in the exercise bike (registered trademark) exercise. The results show that the improved method can estimate core body temperature with an average error of 0.20 ° C, and can reduce the error compared to DEF. It is considered that the reason for this is that, by measuring one point of the measured core body temperature, a delay in tracking the core body temperature with respect to the load fluctuation of the exercise bike (registered trademark) and the individual difference parameter could be appropriately determined. Also, as in walking (FIG. 9), the average error is generally smaller than in running (FIG. 10). From these results, it is possible to estimate the core body temperature with an error of less than 0.3 ° C. even in a normal model for exercise with a relatively small load such as walking and a stationary bike (registered trademark), but when the load is large, individual differences may occur. Adjustment of the parameters was found to be effective.

(Estimation error of core body temperature in tennis)

  As an application example in actual sports, data was collected when six subjects practiced tennis for a total of 20 hours in two days. Table 10 shows the experimental environment for each day. All subjects had at least five years of tennis experience, and had a total of three hours or more of basic tennis practice and games on both days. The practice menu for each schedule is as shown in FIG. Since the basic practice was performed on both days at the beginning, this basic practice was regarded as a warm-up period, and the period of the subsequent game was used as a data set for evaluation. The subjects were instructed to freely take a break and supply water, and rehydrated on average 1475 [ml] per person per day. The core body temperature was measured using an infrared tympanic thermometer before the start of exercise and during a break. The measured timing is indicated by a cross in FIG.

  Furthermore, in order to estimate the external work rate due to exercise, an acceleration sensor Speed Cell (made by adidas) was attached to the shoes. Since this sensor can acquire an average speed every 5 seconds, it is possible to determine whether the subject is stationary or moving. Here, it was considered that the vehicle was running when the speed was 5 [km / hour] or more, and it was considered that the vehicle was stationary when the speed was lower than 5 [km / hour]. For the obtained speed every 5 seconds, the external power according to the speed is obtained, and the average value of the obtained external power for one minute is obtained to obtain the external power Δeff at that time. Was.

  15 and 16 show the average estimation error of the core body temperature on both days. As described in the above (evaluation environment), since it is difficult to constantly measure the core body temperature in tennis, the error of the core body temperature at the measurement timing shown in FIG. 14 is evaluated. The results show that the average estimation errors by the proposed method are 0.28 ° C and 0.30 ° C, and that the same accuracy as OPT is obtained. Compared with DEF, the reduction rate of error was 58% and 42%, respectively, confirming the parameter adjustment method of the improved method and the effectiveness of the improved model. On the other hand, DEF has a much larger error than walking, running, and exercise bike (registered trademark) exercises. From these results, it was found that it is difficult to estimate the core body temperature in complex exercises including complicated load fluctuation, hydration, wind, etc. with the normal model.

  17 and 18 show examples of estimating the core body temperature of the subject on both days. In each of the figures, the horizontal axis represents the passage of time, and the core body temperature measured by an infrared tympanic thermometer is indicated by a point (REAL). From this result, it can be understood that the tendency that the body temperature decreases when the user enters a break is reproduced in the DEF, but the absolute value is closer to the actual measurement due to the parameter adjustment. From the above results, it can be said that the adjustment of the model parameters by the improved method was effective for highly accurate estimation of the core body temperature.

  In the present embodiment, a method of estimating the core body temperature during exercise using the two-node model of Gagge has been described. In addition, for more accurate estimation, the individual difference parameter adjustment method based on the value of the core body temperature measured at the end of warming-up and the model formula were improved. As a result of evaluating the improved method using exercise data for a total of more than 120 hours, it was found that individual difference parameters with smaller estimation errors can be selected by observing only one core temperature. Furthermore, it was confirmed that the improved technique can estimate the core body temperature more accurately than conventional models even in complex exercises with load fluctuations including breaks.

1 deep body temperature estimation device 10 control unit (calculation means)
102 Warming-up parallel operation unit (warming-up parallel operation means)
103 Parameter set selection section (selection means)
104 monitor operation unit (monitor operation means)
12 environment sensor 20 wearable sensor (sensor)
31 deep body temperature sensor

Claims (6)

Using the body heat model, the heat transfer in the body and the heat exchange with the outside of the body are repeatedly calculated by the heat balance calculation formula from the heat production in the deep part obtained based on the measurement with the sensor, and the deep part during exercise is calculated. In a deep body temperature estimation device for estimating body temperature,
Initial difference body temperature in which a plurality of types and a plurality of individual values are prepared for each parameter, and a plurality of delay values are prepared for the heat transfer and the heat exchange. And initial skin temperature, warming-up parallel operation means for performing a parallel calculation by applying a combination of all parameters to the heat balance calculation formula in the warming-up stage,
Selection means for selecting the optimal parameter set by comparing the core body temperature measured at the end of the warm-up and each result of the parallel calculation,
A deep body temperature estimating apparatus comprising: a monitor calculating means for executing the heat balance calculation formula by applying the optimum parameter set and calculating the deep body temperature. A calculation unit for calculating heat production information from the heart rate information,
The deep body temperature estimation device according to claim 1, wherein the sensor is mounted on a human body, measures a heart rate, and outputs the heart rate to the calculation means. 3. The apparatus according to claim 2, wherein the sensor wirelessly transmits a measurement result to the calculation unit. It said monitor Starring core body temperature calculated in Sante stage, if it exceeds a predetermined level, core body temperature estimation apparatus according to any one of claims 1 to 3, further comprising a warning unit for issuing a warning. Using the body heat model, the heat transfer in the body and the heat exchange with the outside of the body are repeatedly calculated by the heat balance calculation formula from the heat production in the deep part obtained based on the measurement with the sensor, and the deep part during exercise is calculated. In a core body temperature estimation method for estimating body temperature,
Initial difference body temperature in which a plurality of types and a plurality of individual values are prepared for each parameter, and a plurality of delay values are prepared for the heat transfer and the heat exchange. And an initial skin temperature, and a warm-up parallel operation step of performing a parallel calculation by applying a combination of all parameters to the heat balance calculation formula in the warm-up stage,
A selection step of selecting an optimal parameter set by comparing the core body temperature measured at the end of the warm-up and each result of the parallel calculation,
A monitor calculation step of executing the heat balance calculation formula by applying the optimal parameter set and calculating the core body temperature. Using a body temperature model, the computer repeatedly calculates the heat transfer in the body and the heat exchange with the outside from the heat production in the deep part obtained based on the measurement with the sensor by the computer using the heat balance calculation formula. In a program that estimates core body temperature,
Initial difference body temperature in which a plurality of types and a plurality of individual values are prepared for each parameter, and a plurality of delay values are prepared for the heat transfer and the heat exchange. And initial skin temperature, warming-up parallel calculation means for performing a parallel calculation by applying a combination of all parameters to the heat balance calculation formula in the warming-up stage,
Selecting means for selecting an optimum parameter set by comparing the core body temperature measured at the end of the warm-up with each result of the parallel calculation, and executing the heat balance calculation formula by applying the optimum parameter set, A program for causing the computer to function as monitor calculation means for calculating body temperature.