State estimation device, state estimation method, and state estimation program

Abstract

To obtain an estimation result correlated with a subjective "revitalized" sense of a sauna user in a state estimation device for estimating a state of the sauna user.SOLUTION: A state estimation device 2 in which a state estimation program 43 is embedded acquires sauna execution times TS21-1, TS21-2, TS21-3 and rest execution times TS22-1, TS22-2, TS22-3. The state estimation device 2 extracts, as stress representative values, minimum values L11, L21, L31 for each of sauna execution times TS21-1, TS21-2, TS21-3 from rMSSD time-series data RM calculated on the basis of heart rate time-series data HW acquired from a wearable terminal 1, and extracts, as relaxation representative values, maximum values L12, L22, L32 for each of rest execution times TS22-1, TS22-2, TS22-3. The state estimation device 2 calculates differences D1, D2, D3 between the stress representative values and the relaxation representative values for each set of a sauna bath and a rest, estimates a "revitalized" state on the basis of the calculated differences D1, D2, D3, and outputs the estimation result.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The technical field disclosed in this specification relates to a state estimation device, a state estimation method, and a state estimation program that estimate the state of a sauna user. [Background technology]

[0002] By repeatedly performing a set of sauna bathing, which promotes autonomic nervous activity, followed by a cold water bath and an open-air bath (hereinafter referred to as "resting"), sauna users can achieve a sense of relaxation and mental clarity after the session, in other words, a sense of "totono." It is known that this "totono" can be achieved by repeatedly performing sauna bathing and resting in accordance with the sauna user's physical condition and sauna experience. In order to achieve a better "totono" experience in the sauna, sauna users have needs such as being able to see their state while in the sauna, managing their sauna time, and viewing it later.

[0003] For example, Patent Document 1 discloses a technology in which a judgment model generated based on the sauna user's biometric information, environmental parameters during sauna use, and subjective information of the sauna user is stored in a state estimation device, and based on the sauna user's biometric information, environmental parameters, and judgment model during sauna use, the device determines whether the sauna user is in at least one of a stressed state and a relaxed state, and notifies the user of the judgment result.

[0004] For example, Patent Document 2 discloses a technology that calculates an index value indicating the physical and mental state of a sauna user based on the relationship between the amount of change in heart rate data measured during sauna activity and reference data that indicates the heart rate characteristics corresponding to sauna activity, and outputs the calculated index value. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2023-032578 [Patent Document 2] Japanese Patent Application Publication No. 2023-160344 Summary of the Invention [Problem to be solved by the invention]

[0006] The technology described in Patent Document 1 can notify sauna users whether they are in a stressed or relaxed state when taking a sauna bath or taking a rest, but it does not notify them of their state of "totonoi."

[0007] The technology described in Patent Document 2 compares the amount of change in the sauna user's actual heart rate data with reference data pre-stored in an information processing device, thereby presenting the sauna user with an objective indicator of the sauna user's mental and physical state, i.e., their state of "tonification." However, heart rate changes vary from person to person. For example, the baseline resting heart rate varies from person to person. Furthermore, for example, heavy sauna users (e.g., sauna users who use the sauna at least once a week) tend to have better cardiopulmonary function and a lower increase in heart rate after starting sauna bathing than light sauna users (e.g., sauna users who use the sauna at least once a year). Furthermore, for example, women tend to have more subcutaneous fat than men, and their heart rate tends to increase less after starting sauna bathing. Furthermore, for example, heart rate tends to increase more quickly the younger the person is. Therefore, the technology described in Patent Document 2 may not provide a satisfactory result because the calculated objective indicator of the state of "tonification" does not match the sauna user's subjective sense of "tonification." [Means for solving the problem]

[0008] A state estimation device made for the purpose of solving the above-mentioned problems is a state estimation device including a computer, wherein the computer executes a heart rate acquisition process to acquire heart rate time series data or pulse rate time series data indicating, in time series, the heart rate or pulse rate per unit time of a sauna user who uses the sauna as a set of at least a sauna bath and a rest period, and an execution time acquisition process to acquire a sauna execution time for the sauna bath and a rest execution time for the rest period, the computer also executes an evaluation index calculation process to calculate, in time series, an autonomic nervous system evaluation index indicating the state of the autonomic nervous system based on the heart rate time series data acquired in the heart rate acquisition process, the autonomic nervous system evaluation index being a time-domain heart rate variability index or an exercise intensity index, the computer further extracts a stress representative value indicating the stress state caused by the sauna bath from the autonomic nervous system evaluation index calculated in the evaluation index calculation process for each sauna execution time acquired in the execution time acquisition process, and further For each rest period acquired in the execution time acquisition process, a relaxation representative value indicating the state of relaxation due to the rest is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation process, the difference between the stress representative value and the relaxation representative value is calculated for each set, an estimation process is performed to estimate the state of "relaxation" due to the sauna based on the calculated difference, and an output process is performed to output the estimation result of the estimation process.

[0009] A state estimation device having such a configuration calculates a standardized autonomic nervous system evaluation index value based on heart rate time series data or pulse rate time series data. The feeling of "tonification" is said to refer to a sense of relief after enduring a harsh environment, such as runner's high. Therefore, the state estimation device extracts a stress representative value or a relaxation representative value for each sauna bathing time and resting time acquired in the sauna bathing time acquisition process from the calculated autonomic nervous system evaluation index value, and calculates the difference between the stress representative value and the relaxation representative value for each sauna bathing and resting time set. This difference is thought to correspond to the sense of relief felt by sauna users when engaging in sauna activities, i.e., the subjective feeling of "tonification." Therefore, the state estimation device estimates the state of "tonification" based on the calculated difference and outputs the estimation result. This state estimation device is expected to obtain an estimation result that correlates with the sauna user's subjective feeling of "tonification."

[0010] A method and a program for realizing the functions of the state estimation device, and a computer-readable storage medium storing the program are also novel and useful.

[0011] According to the technology disclosed in this specification, a state estimation device that estimates the state of a sauna user can obtain estimation results that correlate with the sauna user's subjective sense of "relaxation." [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an example of a state estimation device. [Figure 2] 10 is a flowchart illustrating an example of a control procedure for a state estimation process. [Figure 3] FIG. 10 is a diagram illustrating an example of an autonomic nervous system evaluation index value. [Figure 4] FIG. 10 is a diagram showing an example of a stress representative value and a relaxation representative value. [Figure 5] FIG. 10 is a diagram illustrating an example of data processing when estimating a state of "relaxation." [Figure 6] FIG. 10 is a diagram illustrating an example of an evaluation level. [Figure 7] FIG. 10 is a diagram illustrating an example of a procedure for calculating a relaxation score. [Figure 8] FIG. 10 is a diagram illustrating an example of an estimation result display screen. [Figure 9] 10 is a flowchart illustrating an example of a control procedure for an automatic calculation process. [Figure 10] 10 is a flowchart illustrating an example of a control procedure for a MET determination process. [Figure 11] FIG. 10 is a diagram illustrating an example of data processing when using a wet sauna. [Figure 12] FIG. 10 is a diagram illustrating an example of data processing when using a wet sauna. [Figure 13] FIG. 10 is a diagram illustrating an example of data processing when using a dry sauna. [Figure 14] FIG. 10 is a diagram illustrating an example of data processing when using a dry sauna. [Figure 15] 10 is a flowchart illustrating an example of a control procedure for an acceleration determination process. [Figure 16] 10A and 10B are diagrams illustrating an example of data processing based on triaxial acceleration time series data. [Figure 17] FIG. 10 is a diagram illustrating an example of data processing based on uniaxial acceleration time series data. [Figure 18] FIG. 10 is a diagram illustrating an example of data processing based on uniaxial acceleration time series data. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, a state estimation device, a state estimation method, and a state estimation program according to the present embodiment will be described with reference to the drawings. The present embodiment discloses a state estimation device that estimates the state of a sauna user.

[0014] In the state estimation system 100 shown in FIG. 1, a wearable device 1 and a state estimation device 2 are communicatively connected. The state estimation device 2 is an example of an "information processing device." The state estimation system 100 of this embodiment is configured to measure the sauna user's heart rate and other data using the wearable device 1 worn by the sauna user who uses sauna facilities at a sauna facility or at home, and to estimate and notify the sauna user's state of "relaxation" based on the measured data.

[0015] The wearable device 1 is a device that can be worn on the human body, such as on the arm, leg, or neck. The wearable device 1 has at least a communication function and a heart rate measurement function or a pulse rate measurement function. The wearable device 1 has a controller 10 that includes a CPU 11 and a memory 12. The wearable device 1 also has a communication interface (hereinafter referred to as "communication IF") 14, a clock unit 15, a heart rate monitor or pulse rate monitor (hereinafter referred to as "heart rate monitor") 16, and a thermo-hygrometer 17, all of which are connected to the controller 10.

[0016] The state estimation device 2 is an information processing device having at least a communication function and a storage function. The state estimation device 2 is, for example, a mobile terminal such as a smartphone or a tablet terminal, or a personal computer (hereinafter referred to as "PC"). The state estimation device 2 may be owned by the sauna user, or may be loaned to the sauna user by the sauna facility. The state estimation device 2 may also be installed in a fixed location in the sauna facility. The state estimation device 2 may also be a server provided by a vendor of a state estimation program 43 described below.

[0017] The state estimation device 2 has a controller 20 including a CPU 21 and a memory 22. The state estimation device 2 also has a user IF 23 and a communication interface (hereinafter referred to as "communication IF") 24, which are connected to the controller 20. The CPU 21 is an example of a "computer."

[0018] The CPUs 11 and 21 execute various processes in accordance with programs read from the memories 12 and 22 and based on user operations. The memories 12 and 22 store various programs and various data. The memories 12 and 22 are also used as work areas when various processes are executed. The buffers provided in the CPUs 11 and 21 are also an example of memory. Note that examples of the memories 12 and 22 are not limited to ROM, RAM, HDD, etc. built into the wearable device 1 or the state estimation device 2, but may also be storage media readable and writable by the CPUs 11 and 21, such as recording media, for example, CD-ROMs, DVD-ROMs, etc.

[0019] The communication IFs 14 and 24 include hardware for communicating with external devices. The wearable device 1 and the state estimation device 2 can communicate with each other via the communication IFs 14 and 24. The wearable device 1 and the state estimation device 2 may be connected to a network such as the Internet and be communicatively connected via the network. The communication standard of the communication IFs 14 and 24 may be wired or wireless. The communication standard of the communication IFs 14 and 24 may be, for example, Ethernet (registered trademark), Wi-Fi (registered trademark), USB, Bluetooth (registered trademark), or NFC (Near Field Communication). The wearable device 1 and the state estimation device 2 may be provided with multiple communication IFs 14 and 24 that support multiple communication standards.

[0020] The timekeeping unit 15 of the wearable device 1 has a function of measuring time. The timekeeping unit 15 may measure the elapsed time from a certain point in time in response to an instruction to start measurement. The heart rate meter 16 has a function of measuring the heart rate (HR: bpm), which is the number of times the heart beats within a certain period of time (for example, one minute). The heart rate meter 16 may measure raw heart rate signals. The thermo-hygrometer 17 has a function of detecting the temperature and humidity around the wearable device 1.

[0021] The wearable device 1 may have an acceleration sensor 18 connected to the controller 10. The acceleration sensor 18 has a function of detecting acceleration using, for example, three mutually orthogonal axes as detection axes. The acceleration sensor 18 may be a model that can identify the wearing direction, or a model that cannot.

[0022] The user IF 23 of the state estimation device 2 is a touch panel having a display function for notifying the user of information and a function for the user to input operations. The user IF 23 may be a combination of a display or the like capable of displaying information and a mouse, keyboard, or the like having an input reception function.

[0023] The memory 22 of the state estimation device 2 stores various programs including a state estimation program 43, as well as various data including an estimation result 44 and a relaxation index value 45. The state estimation program 43 of this embodiment has a state estimation function that estimates and notifies the sauna user's state of relaxation. The estimation result 44 includes the state of relaxation estimated by the state estimation program 43. The relaxation index value 45 is an index value that serves as a reference when estimating the state of relaxation. The judgment recording function, state estimation function, and relaxation index value 45 will be described later.

[0024] Next, the procedure of the operation executed in the state estimation system 100 of this embodiment will be described with reference to the drawings. Note that in this embodiment, each process other than a user operation basically indicates the processing of the CPUs 11 and 21 in accordance with instructions written in a program such as the state estimation program 43. In this specification, for convenience, various processes performed by the controllers 10 and 20 or the CPUs 11 and 21 in accordance with a program such as the state estimation program 43 may be described as each program independently performing various processes. The processing by the CPUs 11 and 21 also includes hardware control using the API (application programming interface) of the OS. In this specification, the operation of each program will be described without a detailed description of the OS. Also, the term "acquire" is used as a concept that does not necessarily require a request.

[0025] (Sauna behavior) For example, a sauna user puts on wearable device 1 on their arm in a changing room and repeats a set of sauna bathing, cold water bathing, and resting multiple times. After completing the final set, the sauna user returns to the changing room and removes wearable device 1 from their arm.

[0026] The wearable device 1 is initialized before being worn. The wearable device 1 measures the sauna user's heart rate using a heart rate monitor 16. The wearable device 1 also measures the ambient temperature and humidity using a thermo-hygrometer 17. The wearable device 1 associates the measured heart rate, temperature, and humidity with time information (e.g., time of day, elapsed time) measured by the timing unit 15, and stores them in the memory 12 as heart rate time series data, temperature time series data, and humidity time series data. In other words, the wearable device 1 acquires heart rate time series data that indicates the sauna user's heart rate per unit time in time series, and environmental information that includes at least one of the room temperature and humidity when using the sauna.

[0027] The triggers for the wearable terminal 1 to start or end measurement include, for example, when a switch provided on the wearable terminal 1 is operated, when it detects that the wearable terminal 1 has been worn or removed by a sauna user, or when it receives a measurement start signal or measurement end signal from an external device.

[0028] When the wearable terminal 1 includes the acceleration sensor 18, the acceleration measured by the acceleration sensor 18 may be associated with time information measured by the clock unit 15 and stored in the memory 12 as acceleration time-series data.

[0029] (State estimation function) The state estimation function will be described with reference to Fig. 2. Here, an example will be described in which a sauna user records their sauna activity in the state estimation device 2 and then checks their own state of "relaxation" using the state estimation device 2.

[0030] For example, after using the sauna, a sauna user starts the state estimation program 43 of the state estimation device 2 and inputs a state estimation instruction via a screen (not shown) provided by the state estimation program 43. Upon receiving the state estimation instruction, the CPU 21 of the state estimation device 2 executes the state estimation process shown in FIG.

[0031] First, the CPU 21 acquires time-series data (S11). Specifically, the CPU 21 establishes communication with the wearable device 1 worn by the sauna user using the communication IF 24 and requests the transmission of the time-series data. Upon receiving the request using the communication IF 14, the wearable device 1 reads the time-series data from the memory 12 and transmits it to the state estimation device 2 that issued the request. Upon receiving the time-series data using the communication IF 24, the CPU 21 of the state estimation device 2 temporarily stores it in the memory 22. The time-series data includes, for example, heart rate time-series data, temperature time-series data, and humidity time-series data.

[0032] If the wearable device 1 has an acceleration sensor 18, the time series data may include acceleration time series data. S11 is an example of a "heartbeat acquisition process" or a "heartbeat acquisition step".

[0033] If the CPU 21 has already acquired time-series data from the wearable device 1, for example, in a determination recording process described later, the CPU 21 may skip S11.

[0034] The CPU 21 acquires the sauna session duration and the rest session duration (S12). For example, the CPU 21 acquires the sauna session duration and the rest session duration that are automatically calculated by an automatic calculation process described later. S12 is an example of an "exercise duration acquisition process" or an "exercise duration acquisition step."

[0035] The CPU 21 calculates autonomic nervous system evaluation index time series data based on the heart rate time series data acquired in S11 (S13). The autonomic nervous system evaluation index time series data is a time series calculation of an autonomic nervous system evaluation index that indicates the state of the autonomic nervous system. The autonomic nervous system evaluation index is, for example, a time domain heart rate variability index or an exercise intensity index. S13 is an example of an "evaluation index calculation process" or an "evaluation index calculation step".

[0036] Examples of heart rate variability indices include SD (Standard deviation), CV (Coefficient of Variation), rMSSD (Root Mean Square of the Successive Differences), SD / rMSSD, HR ratio based on resting heart rate, and standardized HR. Examples of exercise intensity indices include METs. The calculation method and characteristics of each index will be briefly explained below.

[0037] SD is the standard deviation of a certain interval obtained by converting the heart rate time series data acquired in S11 into beat interval time series data (msec / beat). For example, the standard deviation over a 60-second period is SD. SD is an index that represents the activity state of the entire autonomic nervous system, including both the sympathetic and parasympathetic nervous systems. Generally, the value decreases when you are stressed and increases when you are relaxed.

[0038] CV is a value calculated by multiplying the coefficient of variation of a certain section of beat interval time series data (msec / beat) by 100 (%). For example, the coefficient of variation over a 60-second period is CV. CV is an index of the parasympathetic nervous system, and its value generally increases in a relaxed state and decreases in a stressed state.

[0039] rMSSD is a value calculated by taking the root mean square of the difference between adjacent values ​​in a certain section of beat interval time series data. For example, the root mean square of the difference between adjacent values ​​over a 60-second period is rMSSD. rMSSD is an index of parasympathetic nervous system activity, and its value increases in a relaxed state and decreases in a stressed state.

[0040] SD / rMSSD is an index of sympathetic nervous activity. SD / rMSSD increases when under stress.

[0041] The HR ratio is the ratio or rate of change of HR, where HR is the raw data or moving average of heart rate time series data (beats / min = bpm), and is based on the resting heart rate or the minimum value during the measurement period. Since resting HR varies from person to person, it is converted to a value that removes this individual variation. The HR ratio will be higher than 1 when exposed to the thermal stress of a sauna or when activity levels are increased.

[0042] Normalized HR is calculated by calculating the average and standard deviation of the data during the measurement period, and then dividing the difference between HR and the calculated average by the standard deviation. If the heart rate value is higher than the average, it may be due to the heat stress of the sauna or an increase in activity level.

[0043] METs are calculated using, for example, heart rate time series data, resting heart rate, and age. METs are an index of physical activity that indicates how many times the intensity of exercise is compared to the resting state (1). The higher the MET value, the greater the physical load.

[0044] An example of autonomic nervous system evaluation index time series data will be described with reference to FIG. 3. FIG. 3(A) shows an example of heart rate time series data HW, with the horizontal axis representing time and the vertical axis representing heart rate (bpm). FIG. 3(B) shows rMSSD time series data RM calculated based on the heart rate time series data HW shown in FIG. 3(A), with the horizontal axis representing time and the vertical axis representing rMSSD. FIG. 3(C) shows METs moving average time series data MW obtained by taking a moving average of METs time series data converted from the heart rate time series data HW shown in FIG. 3(A), and judgment time series data C indicating sauna use and rest use. The horizontal axis of FIG. 3(C) represents time, the left vertical axis represents METs, and the right vertical axis represents the judgment value of the judgment time series data C. In Figure 3(C), a judgment value >0 indicates "sauna taken" (taking a sauna bath) or "not taking a break" (not taking a break), and a judgment value <0 indicates "not sauna taken" (not taking a sauna bath) or "taken a break" (taking a break). Furthermore, judgment values ​​"1" and "-1" indicate sauna bathing and resting in the first set. Judgment values ​​"2" and "-2" indicate sauna bathing and resting in the second set. Judgment values ​​"3" and "-3" indicate sauna bathing and resting in the third set. Judgment value "0" indicates behavior other than sauna bathing and resting.

[0045] From the heart rate time series data (HW) in Figure 3(A), the rMSSD time series data (RM) in Figure 3(B), and the moving average METs time series data (MW) in Figure 3(C), it can be seen that three sets of sauna bathing and resting were performed. As shown in Figures 3(A) and 3(C), heart rate and METs increased from the start of sauna bathing, rapidly decreased after sauna bathing ended, and were lowest during resting. On the other hand, as shown in Figure 3(B), rMSSD decreased from the start of sauna bathing, rose immediately after sauna bathing ended, and remained at a high level during resting, albeit with some fluctuations. Therefore, a correlation was observed between the autonomic nervous system evaluation index and METs and sauna behavior. The moving average METs time series data (MW) in Figure 3(C) was normalized to account for individual differences due to age.

[0046] The state of "Totonoi" is achieved when a stressful state dominated by the sympathetic nervous system switches to a relaxed state dominated by the parasympathetic nervous system, and adrenaline is reduced by half. Therefore, as shown in Figure 2, the CPU 21 calculates a difference based on the characteristics of the autonomic nervous system evaluation index value calculated in S13 (S14).

[0047] For example, as shown in Figure 4, if the autonomic nervous system evaluation index values ​​are SD, CV, and rMSSD, CPU 21 extracts the minimum value for each sauna bathing time as the stress representative value, extracts the maximum value for each resting time as the relaxation representative value, and calculates the difference between the stress representative value (minimum value during sauna bathing) and the relaxation representative value (maximum value during resting) for each set. This is because SD, CV, and rMSSD decrease as the heart rate increases after sauna bathing begins, rise instantaneously as the heart rate increases sharply after sauna bathing ends, and then stabilize around the maximum value during resting as the heart rate stabilizes at the resting heart rate.

[0048] A specific description will be given with reference to Figure 5. Figure 5 is a diagram corresponding to Figure 3(B), where the horizontal axis represents time, the left vertical axis represents rMSSD, and the right vertical axis represents the judgment value. The judgment value is the same as the judgment value shown on the right vertical axis in Figure 3(C), so a description thereof will be omitted.

[0049] The CPU 21 extracts the minimum rMSSD values ​​L11, L21, and L31 as stress representative values ​​from the waveforms of the rMSSD time series data RM shown in Figure 5 that correspond to the sauna times TS21-1, TS21-2, and TS21-3 of the judgment time series data C. The CPU 21 extracts the maximum rMSSD values ​​L12, L22, and L32 as relaxation representative values ​​from the waveforms of the rMSSD time series data RM shown in Figure 5 that correspond to the rest times TS22-1, TS22-2, and TS22-3 of the judgment time series data C. For example, for the first set, the CPU 21 calculates the difference ΔD1 by subtracting the minimum value L11 from the maximum value L12. The CPU 21 similarly calculates the differences ΔD2 and ΔD3 for the second and third sets.

[0050] 4, when the autonomic nervous system evaluation index values ​​are METs, SD / rMSSD, HR ratio, and standardized HR, CPU 21 extracts the maximum value for each sauna session as the stress representative value, extracts the minimum value for each rest session as the relaxation representative value, and calculates the difference between the stress representative value (maximum value during sauna bathing) and the relaxation representative value (minimum value during resting) for each set. This is because METs, SD / rMSSD, HR ratio, and standardized HR rise as the heart rate increases after sauna bathing begins, drop instantaneously as the heart rate increases sharply after sauna bathing ends, and then stabilize near the minimum value during resting as the heart rate stabilizes at the resting heart rate.

[0051] Returning to Fig. 2, when one set consists of a sauna bath and a rest, the CPU 21 determines the number of sets N based on the determination time-series data C. In the data processing shown in Fig. 5, the number of sets N is 3. The CPU 21 calculates the average value of the differences ΔD1, ΔD2, and ΔD3 (hereinafter referred to as the "average difference value") by dividing the sum of the differences ΔD1, ΔD2, and ΔD3 calculated in S14 by the determined number of sets N (S15).

[0052] 2, CPU 21 calculates the length of the rest time relative to the sauna time, i.e., the time ratio between the sauna time and the rest time, for each set (S16). For example, CPU 21 calculates the time ratio for the first set by dividing the sauna time (elapsed time during sauna bathing) TS21-1 for the first set by the rest time (elapsed time during rest) TS22-1. The time ratios for the second and third sets are calculated in the same manner.

[0053] The CPU 21 calculates the average value of the time ratios (hereinafter referred to as "average time ratio") by adding up the time ratios of each set and dividing the sum by the number of sets N (S17). S16 and S17 are an example of "time ratio calculation processing".

[0054] The CPU 21 estimates the state of "relaxation" based on the average difference value calculated in S15 and the average time ratio calculated in S17 (S18). In this embodiment, the state of "relaxation" is estimated by calculating a relaxation score that objectively indicates the state of "relaxation" based on the relaxation index value 45. S14 to S18 are examples of "estimation processing" or "estimation step".

[0055] The calculation procedure for the relaxation index value 45 and the relaxation score will be described with reference to FIGS.

[0056] The inventors conducted a field survey to investigate the actual state of sauna bathing. Approximately 42 subjects (36 men and 6 women) in their 20s to 50s were asked to use the sauna in a set consisting of sauna bathing, cold water bathing, and rest (open-air bathing). There were no restrictions on the number of sets or duration of sauna use. The subjects wore a wearable device 1 equipped with a heart rate monitor on their non-dominant hand. The subjects were also asked to provide their age and the type of sauna they used (e.g., dry sauna, wet sauna). The subjects were also asked to provide information on correct behavior, including the duration of sauna bathing, cold water bathing, resting duration, and the number of sets. The subjects were also asked to provide information on their posture during rest (sitting, supine, etc.). The subjects were also asked to provide information on their sauna satisfaction and subjective level of relaxation.

[0057] The inventors analyzed the subjects' heart rate time series data using a PC, calculated rMSSD time series data, and calculated the average rMSSD difference based on the calculated rMSSD time series data. The inventors also used a PC to convert the heart rate time series data into MET moving average time series data, compared the MET moving average time series data with the MET threshold, and classified the sauna users' behavior into sauna bathing and resting. Cold water bathing was included in sauna bathing because the cold water exposure time was extremely short, there were drastic changes in body movement and posture, and some people did not participate in cold water bathing. The inventors used a PC to calculate the sauna time and rest time based on the classification results and calculated the average time ratio.

[0058] Furthermore, the sauna time and rest time calculated based on the METs moving average time series data were able to be calculated with an accuracy of over 87% for the correct behavior provided by the subjects. In reality, the METs moving average time series data includes the time lag between entering the sauna room and sitting down, and the time lag between moving to the changing room after the rest. Therefore, an error of about one minute can be considered within the acceptable range. Therefore, there is no problem using the average time ratio calculated above in the analysis results.

[0059] From the information provided by the subjects, it was found that sauna satisfaction was not affected by the thermal environment of the sauna, but was significantly related to the subjective level of relaxation. The feeling of "relaxation" is achieved during the process of switching from a stressful state dominated by the sympathetic nervous system to a relaxed state dominated by the parasympathetic nervous system, and adrenaline levels are reduced by half. Therefore, the feeling of "relaxation" achieved through sauna bathing is likely to be achieved by ensuring sufficient rest time and staying in the sauna until parasympathetic nervous activity becomes dominant.

[0060] Therefore, the inventors analyzed the relationship between the average rMSSD difference values ​​and average time ratios calculated using a PC, and the subjective level of relaxation provided by sauna users on a 10-point scale (10 being the most relaxed state), which were aggregated into 5 levels. The results of this analysis are shown in Figure 6. In Figure 6, the horizontal axis shows the average time ratio, and the vertical axis shows the average rMSSD difference values.

[0061] In Figure 6, the first to fifth levels of subjective well-being J1, J2, J3, J4, and J5 indicate the level of subjective well-being experienced by the subjects after using the sauna. The first level of subjective well-being J1 indicates a level where almost no subjective well-being was achieved. The second level of subjective well-being J2 indicates a level where a slight level of subjective well-being was achieved. The third level of subjective well-being J3 indicates a level where a moderate level of subjective well-being was achieved. The fourth level of subjective well-being J4 indicates a level where a moderate level of subjective well-being was achieved. The fifth level of subjective well-being J5 indicates a level where a great deal of subjective well-being was achieved. Figure 6 LX is a graph showing the subjective well-being levels for each level and a regression line based on a plot of the average rMSSD difference and average time ratio for each level.

[0062] As shown in Figure 6, the higher the average rMSSD difference and average time ratio, the higher the level of subjective relaxation. Therefore, by using the average rMSSD difference and average time ratio, it is expected that the state of relaxation can be estimated in a way that satisfies the sauna user.

[0063] 7, the state estimation program 43 has a score table that assigns points based on the average rMSSD difference value and the average time ratio. This score table is an example of the "relaxation index value 45." The CPU 21 assigns points according to the average rMSSD difference value and the average time ratio based on the score table (relaxation index value 45) (Step 1).

[0064] As shown in Figure 6, when the average rMSSD difference is less than 3, the level of subjective relaxation is low, and as shown in Figure 7, the score to be awarded is low at 30. As shown in Figure 6, the higher the average rMSSD difference, the higher the level of subjective relaxation, and as shown in Figure 7, the higher the score to be awarded.

[0065] On the other hand, as shown in Figure 6, when the average time ratio is less than 0.5, the level of subjective relaxation is low, and as shown in Figure 7, the score is set low at -20 points. As shown in Figure 6, the higher the average time ratio, the higher the level of subjective relaxation, and as shown in Figure 7, the higher the score is. However, if the rest time is extremely long compared to the sauna time, the body may become too cold, even though parasympathetic nervous activity was dominant, and sympathetic nervous activity may become dominant again. Therefore, as shown in Figure 7, if the average time ratio is 1.75 or higher, the score will decrease accordingly.

[0066] The CPU 21 calculates the total score of the score calculated based on the average difference value and the score calculated based on the average time ratio (step 2).

[0067] The CPU 21 corrects the total points based on the resting posture information indicating the posture during rest (step 3). Step 3 is an example of "weighting processing".

[0068] For example, when a state estimation instruction is received, the state estimation program 43 displays a screen on the user IF 23 for prompting the sauna user to input the resting posture for each break, and receives the resting posture information for each break. Note that the state estimation program 43 may receive the resting posture setting through a process separate from the state estimation process.

[0069] When resting, lying down in a reclining chair, i.e., lying supine, is more relaxing than sitting in a chair, i.e., sitting position. Therefore, the CPU 21 expresses the sauna user's posture during rest as a binary value based on the posture information during rest.

[0070] For example, the CPU 21 sets "1" for a supine position and "2" for a sitting position. The CPU 21 calculates the average value of the posture setting values ​​by adding the posture setting values ​​set for each break and dividing by the number of sets N. If the average value of the posture setting values ​​is "1", which indicates a supine position, the CPU 21 awards 5 points. On the other hand, if the average value of the posture setting values ​​is "2", which indicates a sitting position, the CPU 21 awards 0 points. If the sauna user does not answer about the posture, the CPU 21 awards 0 points.

[0071] The CPU 21 corrects the calculated score, with 100 being the maximum score (step 4). If the score is greater than 100, the CPU 21 subtracts 5 points. This is to give the sauna user a reasonable sense of satisfaction.

[0072] The CPU 21 includes the score calculated based on steps 1 to 4 in the estimation result 44 as a relaxation score and stores the estimation result 44 in the memory 22. The estimation result 44 may be accumulated and stored in a non-volatile area of ​​the memory 22, or may be temporarily stored in a volatile area. When the estimation result 44 is stored in the non-volatile area of ​​the memory 22, it is stored in association with, for example, user information identifying the sauna user and the date and time of sauna use. This allows the state estimation program 43 to manage the history of the estimation result 44 for each sauna user.

[0073] As shown in FIG. 2, the CPU 21 outputs an estimation result 44 including the state of "relaxation" estimated in S18 (S19). For example, the CPU 21 causes the user IF 23 to display an estimation result display screen D10 shown in FIG. 8. The estimation result display screen D10 includes a score display area DA11 that displays the relaxation score calculated in S18. The score display area DA11 may be configured to display stars indicating the relaxation level by filling them in according to the relaxation score. S19 is an example of an "output process" or "output step."

[0074] The estimation result display screen D10 may include a detailed information display area DA12 that displays the heart rate time series data and the judgment time series data for the estimation target day. The estimation result display screen D10 may also display the sitting resting heart rate, maximum heart rate, maximum exercise intensity, amount of energy consumed, total sauna time, number of sauna bath and rest sets, etc.

[0075] The estimation result display screen D10 may compare the performance on the estimated target day with past performance based on the estimation results 44 stored in memory 22, analyze the characteristics of the sauna user, and display advice on how to feel more relaxed or how to use the sauna safely.

[0076] (Automatic calculation process) The above-mentioned automatic calculation process will be described. First, the CPU 21 acquires time-series data (S21). S21 is similar to S11, and therefore a description thereof will be omitted. If the CPU 21 has already acquired the time-series data, it may skip S21. S21 is an example of "heart rate acquisition process" and "acceleration acquisition process."

[0077] The CPU 21 determines whether acceleration time series data has been acquired (S22). If the time series data acquired in S21 does not include acceleration time series data, the CPU 21 determines that acceleration time series data has not been acquired (S22: NO). In this case, the CPU 21 executes MET determination processing (S23) and proceeds to S24. The MET determination processing is processing for determining whether to take a sauna bath or rest based on the MET time series data converted from the heart rate time series data. In contrast, if the time series data acquired in S21 includes acceleration time series data, the CPU 21 determines that acceleration time series data has been acquired (S22: YES). In this case, the CPU 21 executes acceleration determination processing (S26) and proceeds to S24. The acceleration determination processing is processing for determining whether to take a sauna bath or rest based on the acceleration time series data. The MET determination processing and the acceleration determination processing will be described later.

[0078] The CPU 21 stores the determination result of S23 or S26 in the non-volatile area of ​​the memory 22 in association with the sauna use date and time and the sauna user's user identification information (S24), and ends the automatic calculation process.

[0079] The CPU 21 may store the determination result in an external storage device such as a server. In this case, the CPU 21 may attach the sauna user's user identification information and user name, and device identification information that identifies the state estimation device 2, to the determination result, and the external storage device may store these in association with each other. This allows the external storage device to determine who made the determination and which state estimation device 2 was used, and makes it possible to provide the determination result in response to a request that specifies the sauna user or the state estimation device 2.

[0080] (METS determination process) An example of the control procedure for the mets determination process will be described with reference to Fig. 10. For example, if the wearable device 1 does not include the acceleration sensor 18 and the state estimation device 2 cannot acquire acceleration time-series data from the wearable device 1, the mets determination process is executed.

[0081] First, the CPU 21 determines whether or not the sauna user's personal information has been registered (S31). If the sauna user's personal information, such as age, sex, weight, and height, is registered in the memory 22, the CPU 21 determines that the sauna user has been registered (S31: YES) and proceeds to S32. On the other hand, if the sauna user's personal information is not registered in the memory 22, the CPU 21 determines that the sauna user has not been registered (S31: NO) and executes the registration process (S44).

[0082] For example, in the registration process, the CPU 21 causes the user IF 23 to display a registration screen for prompting the sauna user to input personal information. The registration screen includes fields for inputting personal information such as age, sex, weight, and height. The registration screen may also accept input of the user's physical condition and mood before bathing, posture during rest, etc. The CPU 21 proceeds to S32 on the condition that the personal information has been registered.

[0083] The CPU 21 converts the heart rate time series data acquired in S21 into MET time series data (S32). In this embodiment, the MET time series data is generated by converting each heart rate included in the heart rate time series data acquired in S21 into METs using the age information accepted in the registration process in S44.

[0084] METs can be calculated, for example, by the relative heart rate reserve %HRR × maximum oxygen uptake VO2max (ml / kg / min) ÷ oxygen uptake at rest.

[0085] Of these, the relative heart rate reserve value %HRR can be calculated by (heart rate - resting heart rate) ÷ heart rate reserve HRR (bpm). The heart rate is the heart rate included in the heart rate time series data acquired in S21, the resting heart rate is the minimum heart rate included in the heart rate time series data acquired in S21, and the heart rate reserve HRR (bpm) can be calculated by the maximum heart rate - the resting heart rate. The maximum heart rate HRmax (bpm) can be calculated by 220 - age. The age is the age accepted in the registration process in S44.

[0086] The maximum oxygen intake (VO2max) (ml / kg / min) can be calculated by dividing the maximum heart rate by the resting heart rate x 15.3. The resting heart rate for the maximum heart rate can be calculated as described above. Furthermore, the resting oxygen intake (VO2rest) is a constant, 3.5 ml / kg / min.

[0087] Therefore, the CPU 21 can automatically calculate METs using the minimum heart rate included in the heart rate time-series data and the age accepted in the registration process, and convert the heart rate time-series data into METs time-series data. Note that there are various formulas for calculating METs and maximum oxygen uptake, but the calculation method shown here is just one example.

[0088] The state estimation program 43 may also reflect the posture accepted in the registration process in the MET time-series data. For example, it is known that the resting heart rate is lower when lying down than when sitting because the supine position is less affected by gravity during rest. Therefore, a weighting coefficient may be applied based on the information registered in the registration process so that the MET time-series data value is lower when lying down. In this way, the MET time-series data will be more in line with the actual behavior of sauna users.

[0089] The CPU 21 calculates a moving average of the converted MET time series data to calculate MET moving average time series data (S33). The MET moving average time series data is an example of "MET time series data".

[0090] For example, Figures 11 and 12 are diagrams illustrating an example of data processing based on METs when using a wet sauna. HW in Figure 11(A) shows an example of heart rate time series data of a sauna user who performed sauna activities, with one set consisting of a wet sauna bath, a cold water bath, and a break (open air bath). MW in Figure 11(A) shows an example of METs moving average time series data based on the heart rate time series data HW. The horizontal axis of Figure 11(A) represents time, the left vertical axis represents heart rate (bpm), and the right vertical axis represents METs.

[0091] As shown in Figure 11(A), the MET moving average time series data MW and the original heart rate time series data HW show similar trends. However, the heart rate time series data HW is affected by the age and resting heart rate, which vary from sauna user to sauna user, so a threshold must be set for each individual. On the other hand, the MET moving average time series data MW is standardized taking into account age and resting heart rate, so only one threshold needs to be set.

[0092] Therefore, as shown in FIG. 10, the CPU 21 determines whether the sauna user has used a dry sauna or a wet sauna (S34), and sets the MET threshold value according to the determination result.

[0093] In a dry sauna, the temperature is generally maintained between 70°C and 100°C, and the humidity is maintained at 10% or less. In a wet sauna, the temperature is generally maintained between 40°C and 70°C, and the humidity is maintained at 50% or more, due to heat radiation from steam or mist. In other words, a wet sauna is used in a hotter and more humid environment than a dry sauna. Therefore, it is possible to automatically distinguish between a dry sauna and a wet sauna based on at least one of the temperature and humidity. In this embodiment, based on at least one of the temperature time series data and the humidity time series data acquired in S11 of FIG. 2, it is automatically determined whether the sauna user has used a wet sauna or a dry sauna.

[0094] For example, if the maximum temperature in the temperature time series data is less than the temperature threshold value (e.g., 60°C) and the maximum humidity in the humidity time series data is equal to or greater than the humidity threshold value (e.g., 50%), the CPU 21 determines that the sauna is a wet sauna. On the other hand, if the maximum temperature in the temperature time series data is less than the temperature threshold value and the maximum humidity in the humidity time series data is not equal to or greater than the humidity threshold value, the CPU 21 determines that the sauna is a dry sauna.

[0095] When the CPU 21 determines that the sauna user has used a wet sauna (S34: wet sauna), it acquires the wet threshold value N1 (S35) and proceeds to a determination process (S36). In the determination process, the CPU 21 compares the METs moving average time series data MW with the wet threshold value N1 to determine whether the sauna user has used a sauna or a rest.

[0096] The sauna flag time series data FS2 in Figure 11(B) shows the results of a judgment process performed on the MET moving average time series data MW. The sauna flag time series data FS2, shown by a solid line in Figure 11(B), is data that indicates whether sauna bathing was performed or not in a time series. The rest flag time series data FR2, shown by a dashed line in Figure 11(B), is data that indicates whether rest was performed or not in a time series. The horizontal axis in Figure 11(B) shows time. The left vertical axis shows the sauna judgment value of the sauna flag time series data FS2, with a sauna judgment value of "0" indicating no sauna bathing and a sauna judgment value of "1" indicating sauna bathing. The right vertical axis shows the rest judgment value of the rest flag time series data FR2, with a rest judgment value of "0" indicating no sauna bathing and a rest judgment value of "1" indicating a resting break.

[0097] In addition, because cold water bathing involves an extremely short exposure time, drastic changes in body movement and posture, and some people do not bathe in cold water, cold water bathing was included in sauna bathing, and the behavior of sauna users was separated into sauna bathing and rest.

[0098] The CPU 21 compares the METs moving average time series data MW in Fig. 11(A) with the wet threshold N1. In this embodiment, the wet threshold N1 is set to 2.0 METs, which corresponds to a slow walking speed, but the value of the wet threshold N1 is not limited to this.

[0099] As shown in section N11 of FIG. 11(A), when the MET moving average time series data MW exceeds the wet threshold N1, the CPU 21 determines that a sauna bath was taken and that a rest period was not missed, and sets the sauna determination value of the sauna flag time series data FS2 to "1" and the rest determination value of the rest flag time series data FR2 to "0" as shown in FIG. 11(B). On the other hand, when the MET moving average time series data MW is equal to or less than the wet threshold N1 as shown in N12 of FIG. 11(A), the CPU 21 determines that a rest period was taken and that a sauna bath was not taken, and sets the sauna determination value of the sauna flag time series data FS2 to "0" and the rest determination value of the rest flag time series data FR2 to "1" as shown in FIG. 11(B). This classifies the sauna user's behavior into sauna bathing and rest.

[0100] The CPU 21 corrects the determination result of S36 (S37), as shown in Fig. 10. For example, the CPU 21 calculates the moving average of each of the sauna determination value and the rest determination value shown in Fig. 11(B), compares each moving average with a threshold value, and corrects the sauna implementation time N41 for implementing the sauna and the rest implementation time N52 for implementing the rest. In this embodiment, the calculation range for the moving average is 120 seconds before and after (a total of 240 seconds), but the calculation range is not limited to this embodiment.

[0101] The sauna flag moving average time series data FSs2 shown by the solid line in Figure 12(A) is data that shows, in time series, the sauna flag moving average value obtained by taking the moving average of the sauna judgment values ​​of the sauna flag time series data FS2 shown by the solid line in Figure 11(B). The rest flag moving average time series data FRs2 shown by the dashed line in Figure 12(A) is data that shows, in time series, the rest flag moving average value obtained by taking the moving average of the rest judgment values ​​of the rest flag time series data FR2 shown by the dashed line in Figure 11(B). The horizontal axis of Figure 12(A) represents time, the left vertical axis represents the sauna flag moving average value, and the right vertical axis represents the rest flag moving average value.

[0102] FSr2, shown by a solid line in Figure 12(B), is sauna flag corrected time series data that shows, in time series, the correction results based on the sauna flag moving average time series data FSs2 shown in Figure 12(A). FRr2, shown by a dashed line in Figure 12(B), is rest flag corrected time series data that shows, in time series, the correction results based on the rest flag moving average time series data FRs2 shown in Figure 12(A). The horizontal axis in Figure 12(B) represents time. The left vertical axis represents the sauna determination value of the sauna flag corrected time series data FSr2, with a sauna determination value of "0" indicating no sauna use and a sauna determination value of "1" indicating use of a sauna. The right vertical axis represents the rest determination value of the rest flag corrected time series data FRr2, with a rest determination value of "0" indicating no rest use and a rest determination value of "-1" indicating use of a rest.

[0103] The CPU 21 compares the sauna flag moving average time series data FSs2 with a sauna threshold N91, which is a criterion for determining whether a person is having a sauna bath. The heart rate gradually increases after the sauna bath begins, and then suddenly decreases when the sauna bath ends and the person takes a cold water bath. Therefore, the sauna determination value is likely to fluctuate between the start and end of the sauna bath. Therefore, in this embodiment, the sauna threshold N91 is set to 0.5. Note that the sauna threshold N91 may be different from that in this embodiment.

[0104] As shown in Fig. 12(A), if the sauna flag moving average value of the sauna flag moving average time series data FSs2 is equal to or greater than the sauna threshold N91, the CPU 21 determines that sauna bathing has been performed, and sets the sauna determination value to "1" as shown in Fig. 12(B). Also, as shown in Fig. 12(A), if the sauna flag moving average value of the sauna flag moving average time series data FSs2 is less than the sauna threshold N91, the CPU 21 determines that sauna bathing has not been performed, and sets the sauna determination value to "0" as shown in Fig. 12(B). This divides sauna behavior into sauna bathing time N41, when sauna bathing is performed, and non-sauna bathing time N42, when sauna bathing is not performed.

[0105] The CPU 21 compares the rest flag moving average time series data FRs2 with the rest threshold N92, which is the criterion for determining whether or not a rest has occurred. The heart rate stabilizes around the resting heart rate during rest. Therefore, the rest determination value does not fluctuate much during rest. Therefore, the rest threshold N92 is set to a value smaller than the sauna threshold N91. In this embodiment, the rest threshold N92 is set to 0.2. The rest threshold N92 may be different from that in this embodiment.

[0106] As shown in Fig. 12(A), when the rest flag moving average value of the rest flag moving average time series data FRs2 is equal to or less than the rest threshold value N92, the CPU 21 determines that a rest is not being taken, and sets the rest determination value to "0" as shown in Fig. 12(B). Also, as shown in Fig. 12(A), when the rest flag moving average value of the rest flag moving average time series data FRs2 exceeds the rest threshold value N92, the CPU 21 determines that a rest is being taken, and sets the rest determination value to "-1" as shown in Fig. 12(B). This divides the sauna activity into a rest implementation time N52 when a rest is taken and a non-rest implementation time N51 when a rest is not taken.

[0107] As shown by N44 and N45 in Fig. 12(B), in the sauna flag-corrected time series data FSr2 and the rest flag-corrected time series data FRr2, the small fluctuations in sauna bathing and resting shown by N31 in Fig. 11(B) are treated as a single continuous sauna bathing and resting, respectively. Also, as shown by N46 and N47 in Fig. 12(B), the noise of sauna bathing and resting shown by N33 in Fig. 11(B) has been removed from the sauna flag-corrected time series data FSr2 and the rest flag-corrected time series data FRr2.

[0108] As shown in FIG. 10, the CPU 21 determines whether or not there is an overlapping time between the waveform of the sauna flag corrected time series data FSr2 and the waveform of the rest flag corrected time series data FRr2 (S38).

[0109] The heart rate gradually increases after the sauna bath begins. Therefore, for example, as shown in FIG. 12(B), an overlap time N61 may occur in which the sauna bathing time N41 in the sauna flag corrected time series data FSr2 and the rest time N52 in the rest flag corrected time series data FRr2 overlap. In this case (S38: YES), the CPU 21 regards the overlap time N61 as the sauna bathing time (S43) and proceeds to S39. In this case, the CPU 21 changes the rest determination value for the overlap time N61 from "-1" to "0."

[0110] For example, as shown in Fig. 12(B), if there is a break implementation time N71 of a predetermined time or less (e.g., 3 minutes or less) between adjacent sauna implementation times N41, the CPU 21 adjusts the sauna implementation time by regarding the adjacent sauna implementation times N41 as one continuous sauna implementation time (S39). Specifically, the CPU 21 changes the sauna determination value corresponding to the break implementation time N71 from "0" to "1" and changes the break determination value from "-1" to "0". After completing the adjustment of the sauna implementation time, the CPU 21 proceeds to S40 in Fig. 10.

[0111] The CPU 21 calculates the determination time series data based on the data processed in S36 to S39 and S43 (S40).

[0112] FIG. 12(C) shows an example of judgment time series data C2. The horizontal axis indicates time, and the vertical axis indicates judgment value. A judgment value >0 indicates sauna bathing and no rest, and a judgment value <0 indicates sauna bathing and no rest. Furthermore, judgment values ​​"1" and "-1" indicate sauna bathing and resting in the first set. Judgment values ​​"2" and "-2" indicate sauna bathing and resting in the second set.

[0113] For example, the CPU 21 adds the sauna judgment value and the rest judgment value shown in Fig. 12(B) for each time series, thereby setting a judgment value of "1" for the sauna implementation time TS21 during which the sauna is implemented, and setting a judgment value of "-1" for the rest implementation time TS22 during which the rest is implemented.

[0114] The CPU 21 counts the number of sets, where one set is a sauna bath and a rest. Then, it multiplies the calculated judgment value by the corresponding number of sets. As a result, for the first set, the sauna implementation time TS21 is set to a judgment value of "1" and the rest implementation time TS22 is set to a judgment value of "-1." For the second set, the sauna implementation time TS21 is set to a judgment value of "2" and the rest implementation time TS22 is set to a judgment value of "-2."

[0115] There is a time lag between when the heart rate measurement starts and when the sauna user actually enters the sauna room and sits down. Therefore, the CPU 21 sets the judgment value for the first sauna session time TS21 and before to "0."

[0116] As a result, the judgment time series data C2 shown in Fig. 12(C) is calculated. After calculating the judgment time series data C2, the CPU 21 ends the Mets judgment process of Fig. 10.

[0117] In contrast, as shown in FIG. 10, if the CPU 21 determines that the sauna user has used a dry sauna (S34: dry sauna), it acquires a dry threshold M1 (S42) and proceeds to S36. A dry sauna has a higher temperature than a wet sauna and places a greater strain on the circulatory system. Therefore, it is desirable to set the dry threshold M1 to a value greater than the wet threshold N1. In this embodiment, the dry threshold M1 is set to 3.0 METs, which corresponds to normal walking. The dry threshold M1 may be different from this embodiment.

[0118] The CPU 21 performs the processes from S36 onward to determine whether the sauna user is taking a sauna bath or resting, in the same manner as when it is determined that the sauna user has used a wet sauna.

[0119] 13 and 14 are diagrams illustrating an example of data processing based on METs during dry sauna use. HD in FIG. 13(A) shows an example of heart rate time series data of a sauna user who performed sauna activities consisting of a dry sauna bath, a cold water bath, and a rest (open-air bath) as one set. MD in FIG. 13(A) shows an example of METs moving average time series data based on the heart rate time series data HD. The horizontal axis of FIG. 13(A) represents time, the left vertical axis represents heart rate (bpm), and the right vertical axis represents METs. FIGS. 13(B), 14(A), 14(B), and 14(C) correspond to FIGS. 11(B), 12(A), 12(B), and 12(C), respectively.

[0120] From S36 onwards, data processing is carried out in the same way as when using a wet sauna. In Figures 13 and 14, the same symbols as in Figures 11 and 12 are used for data processed in the same way as when using a wet sauna, and their explanations will be omitted.

[0121] Because the room temperature in a dry sauna is kept higher than in a wet sauna, the heart rate is less likely to drop suddenly during sauna bathing, as shown in the heart rate time series data HD in Figure 13(A). Therefore, as shown in Figures 13(A) and 13(B), when using a dry sauna, the METs moving average time series data MD, sauna flag time series data FS1, and rest flag time series data FR1 are less likely to fluctuate rapidly compared to when using a wet sauna.

[0122] However, during the first sauna bath, the body is not yet accustomed to the temperature of the dry sauna, so as shown in N31 in Figure 13(B), the sauna judgment value and rest judgment value may fluctuate slightly immediately after the start of the first sauna bath.

[0123] Even in this case, the CPU 21 compares the sauna flag moving average time series data FSs1 and rest flag moving average time series data FRs1 shown in Fig. 14(A), which are calculated by taking the moving averages of the sauna determination value and rest determination value shown in Fig. 13(B), with the sauna threshold N91 and rest threshold N92, respectively, to calculate the sauna bathing time N41, non-sauna bathing time N42, rest bathing time N52, and non-rest bathing time N51, and corrects the sauna determination value and rest determination value as shown in the sauna flag corrected time series data FSr1 and the rest flag corrected time series data FRr1 in Fig. 14(B). As a result, the small fluctuations shown at N31 in Fig. 13(B) are regarded as one continuous sauna bath, as shown at N44 and N45 in Fig. 14(B).

[0124] Furthermore, the CPU 21 regards the overlap time N61 between the sauna flag corrected time series data FSr1 and the rest flag corrected time series data FRr1 shown in Fig. 14(B) as the sauna implementation time. If there is no rest implementation time of a predetermined time (e.g., 3 minutes) or less between the sauna implementation times N41, as shown in the sauna flag corrected time series data FSr1 and the rest flag corrected time series data FRr1 in Fig. 14(B), the CPU 21 skips S39 in Fig. 10 and executes S40 to calculate the determination time series data C1.

[0125] Therefore, by performing the METs determination process, the state estimation device 2 can determine whether a sauna bath or a rest period is being performed based on the METs time series data, whether a dry sauna or a wet sauna is being used, and can calculate the sauna time TS21, the rest time TS22, and the number of sets.

[0126] (Acceleration determination process) Next, the acceleration determination process will be described with reference to Fig. 15. For example, if the wearable device 1 has an acceleration sensor 18 and the state estimation device 2 can acquire acceleration time-series data from the wearable device 1, the acceleration determination process is executed.

[0127] First, CPU 21 determines whether the wearing state of wearable device 1, i.e., the direction setting of acceleration sensor 18, can be identified (S202). This is because the direction of acceleration depends on the direction setting of acceleration sensor 18. The determination of whether the wearing state can be identified differs depending on, for example, whether wearable device 1 is worn on the trunk of the body, where the orientation of wearable device 1 is the same as the orientation of the body, and whether wearable device 1 is worn on any of the limbs such as the arms or legs, or the head, where the orientation of wearable device 1 may differ from the orientation of the body.

[0128] For example, if the CPU 21 does not acquire acceleration direction setting information from the wearable device 1 when acquiring time-series data in S11 of Fig. 2, it determines that the wearing state of the wearable device 1, i.e., the direction setting of the acceleration sensor 18, cannot be identified (S202: NO). In this case, the CPU 21 acquires triaxial acceleration time-series data (acceleration time-series data on the mutually orthogonal x-, y-, and z-axes) from the time-series data acquired in S11 of Fig. 2, and calculates a composite vector in time series based on the acquired triaxial acceleration time-series data (S203). The CPU 21 acquires a composite threshold P1 (S204) and performs a determination process (S205).

[0129] FIG. 16 is a diagram illustrating an example of data processing based on triaxial acceleration when using a wet sauna. The dashed line in FIG. 16(A) indicates the heart rate time series data HW, and the solid line indicates the composite vector time series data BM. The horizontal axis of FIG. 16(A) indicates time, the left vertical axis indicates the heart rate (bpm), and the right vertical axis indicates the acceleration of the composite vector (m / s 2 ) is shown.

[0130] When a sauna user is resting in the same posture, the composite vector BodyMotion is 0 m / s as shown in the composite vector time series data BM in Figure 16(A). 2 On the other hand, when the sauna user makes body movements, such as when washing the body, entering and sitting in the sauna room, standing up and leaving the sauna room, lying down to rest, or standing up from a lying position, the composite vector BodyMotion of the composite vector time series data BM fluctuates wildly.

[0131] Therefore, the CPU 21 extracts a peak time P21 at which the composite vector BodyMotion of the composite vector time series data BM exceeds a composite threshold value P1, as shown in Fig. 16(A). In this embodiment, the composite threshold value P1 is 0.75 m / s 2 The composite threshold value P1 can be set in accordance with the sensor characteristics of the acceleration sensor 18, and may be different from that in this embodiment.

[0132] PBM in Figure 16(B) shows the body movement presence / absence time series data PBM indicating whether the sauna user is moving. C3 in Figure 16(B) shows the judgment time series data. The horizontal axis in Figure 16(B) shows time. The left vertical axis in Figure 16(B) shows the body movement presence / absence judgment value of the body movement presence / absence time series data PBM, with a body movement presence / absence judgment value of "1" indicating "body movement present" and a body movement presence / absence judgment value of "0" indicating "body movement absent." The right vertical axis in Figure 16(B) shows the judgment value of the judgment time series data C3. A judgment value > 0 indicates sauna use and no rest, and a judgment value < 0 indicates no sauna use and rest. Furthermore, judgment values ​​"1" and "-1" indicate the first sauna bath and rest. Judgment values ​​"2" and "-2" indicate the second sauna bath and rest.

[0133] As shown in the body motion presence / absence time-series data PBM in Fig. 16(B), the CPU 21 sets a body motion presence / absence determination value of "1" at the extracted peak time P21. The CPU 21 also sets a body motion presence / absence determination value of "0" at a time P22 when the composite vector BodyMotion of the composite vector time-series data BM is equal to or less than the composite threshold.

[0134] Body movement during time P11 when the heart rate is increasing is likely to be an action associated with sauna bathing. Therefore, during time P11 when the heart rate is increasing, CPU 21 considers the period from the first peak time P21f to the last peak time P21e as one continuous sauna bathing period P41 and sets the body movement presence / absence determination value to "1." As a result, the body movement presence / absence determination value "1" is set for time P41 when the heart rate is increasing and the composite vector time series data BM exceeds the composite threshold value P1, which satisfies the determination conditions, making it possible to determine that the person is sauna bathing. Furthermore, the body movement presence / absence determination value "0" is set for time periods that do not satisfy these determination conditions, making it possible to determine that the person is resting.

[0135] As shown in Fig. 15, the CPU 21 adjusts the sauna session time P41 (S206). For example, as shown in Fig. 16(B), the CPU 21 changes the body movement presence / absence determination value from "0" to "1" for time P62, during which the composite vector BodyMotion fluctuates, among time P31, for which the body movement presence / absence determination value is set to "0." As a result, the times P41 adjacent to and before time P62, for which the body movement presence / absence determination value is set to "1," are joined together and considered to be one continuous sauna session time.

[0136] For example, when taking a wet sauna bath while bathing the lower half of the body, a slight change in posture can cause a temporary drop in heart rate. Even in such a case, the process in S206 determines that the action is a sauna bath, avoiding the misidentification of the action as a rest.

[0137] As shown in Fig. 15, CPU 21 calculates the determination time-series data (S207). As shown in Fig. 16(B), CPU 21 sets a determination value of "1" at the time when the body movement presence / absence determination value is set to "1", and sets a determination value of "-1" at the time when the body movement presence / absence determination value is set to "0".

[0138] Then, the CPU 21 calculates the number of sets, where one set is the sauna time and the rest time. The CPU 21 multiplies the judgment value by a value corresponding to the number of sets. As a result, the sauna time TS21 of the first set is set to a judgment value of "1," the rest time TS22 of the first set is set to a judgment value of "-1," the sauna time TS21 of the second set is set to a judgment value of "2," and the rest time TS22 of the second set is set to a judgment value of "-2." Therefore, the CPU 21 can also determine the sauna time TS21, the rest time TS22, and the number of sets based on the judgment values ​​of the judgment time-series data C3. The CPU 21 sets the judgment value before the sauna time TS21 of the first set to "0."

[0139] As a result, the determination time series data C3 is calculated. After calculating the determination time series data C3, the CPU 21 ends the acceleration determination process shown in FIG.

[0140] 15, for example, when acquiring time-series data in S11 of FIG. 2, if CPU 21 acquires acceleration direction setting information from wearable device 1, CPU 21 determines that the wearing state of wearable device 1, i.e., the direction setting of acceleration sensor 18, can be identified (S202: YES). In this case, CPU 21 acquires vertical acceleration time-series data with the vertical direction as the detection axis from the triaxial acceleration time-series data acquired in S11 of FIG. 2 (triaxial acceleration time-series data acquired from the acceleration sensor in this embodiment) (S211). This is because sauna bathing and resting movements involve vertical movements, such as entering the sauna room and sitting down, leaving the sauna room and entering the cold bath, and lying down in a rest area.

[0141] The CPU 21 acquires the uniaxial threshold value P2 (S212). After that, the CPU 21 proceeds to the determination process (S205). The vertical acceleration is a value larger than the composite vector. Therefore, the uniaxial threshold value P2 is set to a value higher than the composite threshold value P1. In this embodiment, the uniaxial threshold value P2 is set to 1.0 (m / s 2 ) The single-axis threshold P2 may be different from that in this embodiment depending on the sensor characteristics, etc. The processing from S205 onwards has been described above, but will be briefly explained below.

[0142] FIG. 17 is a diagram illustrating an example of data processing based on uniaxial acceleration during use of a wet sauna. FIG. 17(A) shows heart rate time series data HW, with the horizontal axis representing time and the vertical axis representing heart rate (bpm). FIG. 17(B) shows vertical acceleration time series data AC, with the horizontal axis representing time and the vertical axis representing vertical acceleration (m / s 2) PA in Figure 18(A) shows an example of body movement presence / absence time series data, with the horizontal axis showing time and the vertical axis showing body movement presence / absence judgment value. A body movement presence / absence judgment value of "1" indicates the presence of movement, and a body movement presence / absence judgment value of "0" indicates the absence of movement. C4 in Figure 18(B) shows judgment time series data, with the horizontal axis showing time and the vertical axis showing judgment value. A judgment value > 0 indicates sauna bathing and no resting, and a judgment value < 0 indicates sauna bathing and no resting. Furthermore, judgment values ​​"1" and "-1" indicate the first set of sauna bathing and resting. Judgment values ​​"2" and "-2" indicate the second set of sauna bathing and resting.

[0143] The CPU 21 compares the vertical acceleration time series data AC in Fig. 17(B) with the uniaxial threshold P2, and sets a body movement presence / absence determination value of "1" as shown in Fig. 18(A) at peak time P21 when the vertical acceleration time series data AC exceeds the uniaxial threshold P2, and sets a body movement presence / absence determination value of "0" at time P22 when the vertical acceleration time series data AC is equal to or less than the uniaxial threshold P2 as shown in Fig. 17(B). The CPU 21 connects peak times P21 at time P11 when the heart rate is increasing as shown in Fig. 17(A) to form one continuous time P41 (S205).

[0144] Furthermore, as shown in Figure 18(A), the CPU 21 changes the body movement determination value "0" from "0" to "1" for the time P62 during which the composite vector BodyMotion fluctuates, among the time P31 during which the body movement determination value "0" is set, and adjusts the sauna duration (S206).

[0145] The CPU 21 calculates a determination value based on the body movement presence / absence time-series data PA in Fig. 18(A), and calculates determination time-series data C4 that can determine the sauna time TS21, the rest time TS22, and the number of sets, as shown in Fig. 18(B) (S207). Thereafter, the CPU 21 ends the acceleration determination process shown in Fig. 15.

[0146] As described above, the state estimation device 2 of this embodiment calculates standardized autonomic nervous system evaluation indices, such as rMSSD and METs, based on the heart rate time series data HW. The feeling of "tonification" is said to refer to a sense of relief after enduring a harsh environment, such as runner's high. Therefore, the state estimation device 2 extracts a stress representative value or a relaxation representative value for each sauna bathing time and resting time acquired in S12 of FIG. 2 from the calculated autonomic nervous system evaluation indices, and calculates the difference between the stress representative value and the relaxation representative value for each sauna bathing and resting time set. This difference is thought to correspond to the sense of relief felt by sauna users when engaging in sauna activities, i.e., the subjective feeling of "tonification." Therefore, the state estimation device 2 estimates the state of "tonification" based on the calculated difference and outputs the estimation result. This state estimation device for estimating the state of a sauna user is expected to obtain an estimation result that correlates with the sauna user's subjective feeling of "tonification."

[0147] It should be noted that this embodiment is merely an example and does not limit the present invention in any way. Therefore, the present invention can naturally be improved and modified in various ways without departing from the spirit and scope of the present invention. For example, the state estimation program 43 may use pulse time-series data indicating pulse rates in time series instead of heart rate time-series data. The wearable terminal may be provided with a pulse meter that measures pulse rates instead of the heart rate meter 16.

[0148] For example, the estimation result 43 may be output by a method other than a display, such as audio. Furthermore, the state estimation device 2 may output the estimation result 43 to an external device, such as a smartphone of the sauna user, and display it.

[0149] For example, in S12, the state estimation device 2 may acquire the sauna use time and the rest time input in response to a user operation. For example, the state estimation program 43 may cause the user IF 23 to display an execution time input screen for prompting the sauna user to input the sauna use time and the rest time, and acquire the sauna use time and the rest time manually input into the execution time input screen. However, by acquiring the sauna use time and the rest time that are automatically calculated in S12, the state estimation device 2 can reduce the effort required for the sauna user to input the sauna use time and the rest time.

[0150] For example, the sauna session duration and rest session duration acquired in S12 may be automatically calculated based on the heart rate time series data or the autonomic nervous system evaluation index time series data other than the MET time series data.

[0151] For example, the automatic calculation process may be executed at the same time as S12 in FIG. 2 is executed, or may be executed at a different time from the time when the state estimation process is executed.

[0152] For example, the relaxation score may be calculated for each set. In this case, the state estimation device 2 may display the relaxation score for each set, or may display the average relaxation score for each set. This allows the user to check changes in the relaxation score for each set, and for example, the number of sets can be increased or decreased depending on the degree of relaxation.

[0153] For example, a sauna history including the sauna use time and the rest time may be uploaded to a server. In this case, the state estimation device 2 may receive and acquire the sauna use time and the rest time from the server in S12 of FIG. 2. Alternatively, the state estimation device 2 may upload the estimation result 44 to the server, receive the estimation result 44 from the server at any timing, and display it on the user IF 23. This reduces the memory load on the state estimation device 2.

[0154] For example, the state estimation program 43 may acquire the sauna use time and the rest use time determined by another program in S12 of FIG.

[0155] For example, the Totonoi score may be calculated without considering the time ratio between sauna time and rest time. However, if sauna bathing is performed without sufficient rest, it is difficult to obtain a subjective sense of "refreshment." Therefore, by calculating the Totonoi score based on the difference between the stress representative value and the relaxation representative value of the autonomic nervous system evaluation index calculated for each set and the time ratio between sauna time and rest time calculated for each set, it is more likely to obtain an estimated result that is highly correlated with the subjective feeling of "refreshment."

[0156] For example, step 3 in Figure 7 may be omitted. However, the supine position provides a greater relaxation effect than the seated position during rest. Therefore, correcting the relaxation score based on the posture during rest, i.e., weighting the estimation result, increases the likelihood of obtaining estimation results that are highly correlated with the subjective feeling of "relaxation." Also, for example, step 4 in Figure 7 may be omitted.

[0157] For example, if the wearable device 1 includes a user interface, the wearable device 1 may display the estimation result 44 on the user interface. The displayed estimation result 44 may be estimated by the wearable device 1 itself, or may be estimated by an external device such as a server or the state estimation device 2.

[0158] For example, the state estimation program 43 may use a value input by the sauna user or a value measured in advance as the resting heart rate to be used in calculating the MET time-series data in S32 of Fig. 10. However, by using the minimum value of the heart rate time-series data measured by the wearable device 1 for that day as the resting heart rate, the physical condition of that day is reflected with high accuracy, and the effort of manually inputting and measuring the resting heart rate is reduced.

[0159] For example, the state estimation program 43 may omit the process of S44 in Fig. 10, and personal information may not be registered. However, by registering personal information in the state estimation program 43, the effort of inputting personal information each time the automatic determination process is executed can be avoided.

[0160] For example, the state estimation program 43 may omit the processes of S34, S35, and S42 in Fig. 10 and use a common METs threshold for both dry saunas and wet saunas to determine whether to sauna or rest. However, wet saunas and dry saunas place different strains on the circulatory system. Therefore, by distinguishing between wet saunas and dry saunas and using appropriate wet and dry thresholds for each, improved determination accuracy can be expected.

[0161] 10, the state estimation program 43 may distinguish between a dry sauna and a wet sauna in response to a user operation. For example, prior to S34, the state estimation program 43 may cause the state estimation device 2 to display a type selection screen that allows the sauna user to select the type of sauna used, and in S34, distinguish between a dry sauna and a wet sauna based on the type selected on the type selection screen. However, distinguishing between a dry sauna and a wet sauna based on at least one of the temperature time-series data and the humidity time-series data acquired from the wearable device 1 increases the likelihood that an appropriate MET threshold will be used, and improved determination accuracy can be expected.

[0162] 10 may be omitted. However, by calculating the moving averages of the sauna determination value and the rest determination value, comparing them with the sauna threshold N91 and the rest threshold N92, and correcting the sauna and rest times determined in the determination process of S36, noise N33 contained in the determination result can be removed. Furthermore, if there is an overlap time N61 where the corrected sauna and rest times N41 and N52 overlap, the overlap time N61 can be regarded as the sauna time. This increases the likelihood of properly detecting sauna bathing, and improves the accuracy of the determination, even when the tendency for heart rates to increase after sauna bathing differs depending on the characteristics of sauna users, such as heavy and light users.

[0163] For example, S39 in Fig. 10 may be omitted. However, if there is a rest period N62 of a predetermined length or less between adjacent sauna periods N41 after correction, correcting the adjacent sauna periods N41 to one continuous sauna period increases the likelihood of determining that a sauna bath has occurred even if, for example, the heart rate decreases for some reason during a sauna bath, which is expected to improve the accuracy of the determination.

[0164] 15, the composite vector of the triaxial acceleration time series data and the vertical acceleration time series data may be compared with the same threshold value. However, the acceleration of the vertical acceleration time series data will be greater than the acceleration of the composite vector. Therefore, if the wearing state of the wearable device 1 can be identified, the triaxial acceleration time series data and the vertical acceleration time series data may be compared with the composite threshold value P1 and a uniaxial threshold value P2 greater than the composite threshold value P1, respectively, which is expected to improve the accuracy of the determination.

[0165] For example, the judgment time series data C1 and C2 calculated in S40 of Fig. 10 and the judgment time series data C3 and C4 calculated in S207 of Fig. 15 do not have to be values ​​obtained by multiplying the judgment value by the number of sets. However, by using a judgment value obtained by multiplying the judgment value by the number of sets, it is easier to understand the relationship between the number of sets, sauna bathing, and rest.

[0166] For example, S33 in Fig. 10 may be omitted, and the MET time series data before the moving average may be used directly in the determination process of S36. However, by determining whether to sauna bath or rest based on the MET moving average time series data MW, which is the moving average of the MET time series data, erroneous determination due to noise N43 can be avoided, and improved determination accuracy can be expected.

[0167] Furthermore, in any flowchart disclosed in the embodiments, the execution order of multiple processes in any multiple steps can be changed or they can be executed in parallel as long as no contradiction occurs in the processing content.

[0168] The processes disclosed in the embodiments may be executed by hardware such as a single CPU, multiple CPUs, or ASIC, or a combination thereof. The processes disclosed in the embodiments may be realized in various ways, such as a recording medium on which a program for executing the processes is recorded, or a method. [Explanation of symbols]

[0169] 2. State Estimation Device 21 CPU 43 State Estimation Program

Claims

1. A state estimation device including a computer, The computer a heart rate acquisition process for acquiring heart rate time series data or pulse rate time series data that indicates the heart rate or pulse rate per unit time of a sauna user who uses the sauna as a set of at least a sauna bath and a rest; an execution time acquisition process for acquiring a sauna execution time for performing the sauna bath and a break execution time for performing the break; Run Moreover, the computer executes an evaluation index calculation process to calculate an autonomic nervous system evaluation index in time series, which indicates an autonomic nervous state, based on the heart rate time series data or pulse rate time series data acquired in the heart rate acquisition process, wherein the autonomic nervous system evaluation index is a time domain heart rate variability index or an exercise intensity index; Furthermore, the computer an estimation process in which, for each sauna bathing time acquired in the sauna bathing time acquisition process, a stress representative value indicating the stress state caused by the sauna bathing is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation process; and, for each resting time acquired in the sauna bathing time acquisition process, a relaxation representative value indicating the relaxed state caused by the resting time is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation process, and the difference between the stress representative value and the relaxation representative value is calculated for each set, and the state of "relaxation" caused by the sauna bathing is estimated based on the calculated difference. an output process for outputting an estimation result of the estimation process; To execute A state estimation device configured as follows.

2. 2. The state estimation device according to claim 1, The autonomic nervous system evaluation index is rMSSD, which is an index representing the activity state of the parasympathetic nervous system; A state estimation device configured as follows.

3. 3. The state estimation device according to claim 1, The computer execute a time ratio calculation process to calculate the time ratio of the sauna session time and the rest session time acquired in the session time acquisition process for each set; In the estimation process, the state of relaxation is estimated based on the difference and the time ratio. A state estimation device configured as follows.

4. 4. The state estimation device according to claim 3, In the implementation time acquisition process, the sauna implementation time and the rest implementation time are automatically calculated by comparing MET time series data obtained by converting the heart rate time series data with a MET threshold value. A state estimation device configured as follows.

5. 4. The state estimation device according to claim 3, The computer further comprises: Execute an acceleration acquisition process to acquire acceleration time series data associated with the sauna user's behavior; In the implementation time acquisition process, a time that satisfies a judgment condition is determined to be a sauna implementation time and acquired, and a time that does not satisfy the judgment condition is determined to be a rest implementation time and acquired, and the judgment condition is a time when the heart rate time series data acquired in the heart rate acquisition process is on an increasing trend and the acceleration time series data acquired in the acceleration acquisition process is a time when the acceleration time series data fluctuates wildly. A state estimation device configured as follows.

6. 2. The state estimation device according to claim 1, The computer a weighting process for weighting the estimation result of the estimation process depending on the posture of the sauna user when taking the break; To execute A state estimation device configured as follows.

7. a heart rate acquisition step of acquiring heart rate time series data or pulse rate time series data showing the heart rate or pulse rate per unit time of a sauna user who uses the sauna as a set of at least a sauna bath and a rest; an execution time acquisition step of acquiring a sauna execution time for performing the sauna bath and a break execution time for performing the break; and Furthermore, an evaluation index calculation step is performed to calculate an autonomic nervous system evaluation index indicating a state of the autonomic nervous system in a time series based on the heart rate time series data acquired in the heart rate acquisition step, and the autonomic nervous system evaluation index is a time domain heart rate variability index or an exercise intensity index; Furthermore, for each sauna bathing time acquired in the sauna bathing time acquisition step, a stress representative value indicating the stress state caused by the sauna bathing is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation step, and a relaxation representative value indicating the relaxed state caused by the resting time is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation step for each resting time acquired in the sauna bathing time acquisition step, and an estimation step of calculating the difference between the stress representative value and the relaxation representative value for each set and estimating the state of "relaxation" caused by the sauna bathing based on the calculated difference. an output step of outputting an estimation result of the estimation step; To do The state estimation method is configured as follows.

8. A state estimation program executable by an information processing device, The information processing device includes: a heart rate acquisition process for acquiring heart rate time series data or pulse rate time series data that indicates the heart rate or pulse rate per unit time of a sauna user who uses the sauna as a set of at least a sauna bath and a rest; an execution time acquisition process for acquiring a sauna execution time for performing the sauna bath and a break execution time for performing the break; Execute In addition, the information processing device executes an evaluation index calculation process for calculating an autonomic nervous system evaluation index in time series, which indicates a state of the autonomic nervous system, based on the heart rate time series data acquired in the heart rate acquisition process, wherein the autonomic nervous system evaluation index is a time domain heart rate variability index or an exercise intensity index; Furthermore, the information processing device an estimation process in which, for each sauna bathing time acquired in the sauna bathing time acquisition process, a stress representative value indicating the stress state caused by the sauna bathing is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation process; and, for each resting time acquired in the sauna bathing time acquisition process, a relaxation representative value indicating the relaxed state caused by the resting time is extracted from the autonomic nervous system evaluation index calculated in the evaluation index calculation process, and the difference between the stress representative value and the relaxation representative value is calculated for each set, and the state of "relaxation" caused by the sauna bathing is estimated based on the calculated difference. an output process for outputting an estimation result of the estimation process; Execute A state estimation program configured as follows.

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