Stress estimation method

Abstract

A stress estimation device 100 of the present invention includes: a first computing unit 121 for computing a first stress value by inputting to a first stress computation model first data according to biometric data measured from a target person; and a second computing unit 122 for computing a second stress value by inputting to a second stress computation model the first stress value and second data according to subjective data related to stress acquired from the target person, wherein the second stress computation model is generated by learning using a first stress value and second data previously acquired for a predetermined person.

Description

[Technical Field]

[0001] The present invention relates to a stress estimation method, a stress estimation device, and a program. [Background technology]

[0002] Known methods for estimating a person's stress include a method based on the person's subjective opinion and a method based on the person's biological information. The method based on the person's subjective opinion estimates stress based on the person's responses to a predetermined questionnaire, for example. The method based on the person's biological information estimates stress based on the person's biological information acquired from a wearable device or an image.

[0003] In Patent Document 1, a final stress level is estimated based on a stress level based on a person's subjective opinion and a stress level based on the person's biological information. Specifically, Patent Document 1 prepares a coordinate plane defined by a coordinate axis related to the subjective stress level and a coordinate axis related to the objective stress level, and estimates the final stress level based on the area on the coordinate plane where the acquired subjective stress level and objective stress level are located. Patent Document 1 also estimates stress by calculating a total stress level using the subjective stress level, the objective stress level, and a predefined function. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-169974 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the method of Patent Document 1 mentioned above, the final stress state is determined based on subjective stress values ​​and objective stress values ​​using preset criteria (coordinate plane or function), and since the criteria are uniform, it is difficult to estimate stress with higher accuracy.

[0006] Therefore, an object of the present invention is to provide a stress estimation method that can solve the above-mentioned problem of being unable to estimate stress with higher accuracy. [Means for solving the problem]

[0007] A stress estimation method according to one aspect of the present invention includes: Calculating a first stress value by inputting first data based on biometric data measured from the target person into a first stress calculation model; calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; The structure is as follows.

[0008] Furthermore, the stress estimation device according to one aspect of the present invention comprises: a first calculation unit that calculates a first stress value by inputting first data based on biological data measured from a target person into a first stress calculation model; a second calculation unit that calculates a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; Equipped with the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; The structure is as follows.

[0009] Furthermore, a program according to one aspect of the present invention includes: In the information processing device, Calculating a first stress value by inputting first data based on biometric data measured from the target person into a first stress calculation model; calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; It is a program for executing a process, the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; The structure is as follows. [Effects of the Invention]

[0010] With the above-described configuration, the present invention can estimate stress with higher accuracy. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram showing a configuration of a stress estimation device according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a diagram showing an example of a test for acquiring subjective data on a person's stress to be input to the stress estimation device disclosed in FIG. 1. [Figure 3] 2 is a flowchart showing the operation of the stress estimation device disclosed in FIG. 1. [Figure 4] 2 is a flowchart showing the operation of the stress estimation device disclosed in FIG. 1. [Figure 5] FIG. 3 is a diagram for explaining the effect of the stress estimation device according to the first embodiment of the present invention. [Figure 6] FIG. 10 is a block diagram showing a hardware configuration of a stress estimation device according to a second embodiment of the present invention. [Figure 7]FIG. 10 is a block diagram showing the configuration of a stress estimation device according to a second embodiment of the present invention. [Figure 8] 10 is a flowchart showing the operation of the stress estimation device according to the second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] <Embodiment 1> A first embodiment of the present invention will be described with reference to Figures 1 to 4. Figures 1 and 2 are diagrams for explaining the configuration of a stress estimation device, and Figures 3 and 4 are diagrams for explaining the processing operation of the stress estimation device.

[0013] [composition] The stress estimation device 10 of the present invention is used to estimate a person's stress. For example, the stress estimation device 10 is used to calculate a stress value that represents a person's chronic or acute stress state. However, the stress estimation device 10 of the present invention may calculate any stress value of a person.

[0014] The stress estimation device 10 is configured with one or more information processing devices each including a calculation device and a storage device. As shown in FIG. 1 , the stress estimation device 10 includes a data acquisition unit 11, a learning unit 12, a first calculation unit 13, a second calculation unit 14, and an output unit 15. The functions of the data acquisition unit 11, the learning unit 12, the first calculation unit 13, the second calculation unit 14, and the output unit 15 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The stress estimation device 10 also includes a person information storage unit 16, a first model storage unit 17, and a second model storage unit 18. The person information storage unit 16, the first model storage unit 17, and the second model storage unit 18 are configured with a storage device. Each component will be described in detail below.

[0015] The data acquisition unit 11 (acquisition unit) acquires data used to estimate a person's stress. First, the data acquisition unit 11 acquires learning data for generating, by machine learning, a stress calculation model used to calculate a stress value. The learning data is data on an arbitrary number of people (predetermined people), and is stored in the person information storage unit 16 in association with each person.

[0016] Specifically, the data acquiring unit 11 acquires, as the learning data, second data based on subjective data regarding the person's stress. At this time, the data acquiring unit 11 acquires a "PSS score" obtained by aggregating a "Perceived Stress Scale (PSS)" as the second data based on the subjective data regarding the person's stress. Here, the PSS, as shown in FIG. 2 as an example, is a set of 14 pre-set questions asking the user how they feel about what is happening, with five levels of answers provided. Each answer on the five-level scale is assigned a score of 0 to 4 points, and the sum of the scores of the answers to 10 of the 14 questions that are the subject of calculation is calculated as the PSS score. Therefore, the PSS score ranges from 0 to 40 points. For example, the data acquiring unit 11, as shown in FIG. 1, displays PSS questions as shown in FIG. 2 from an input device 20, such as an information processing terminal operated by the person U, acquires answers input by the person U via the input device 20, and aggregates the PSS score from the answers. However, the data acquisition unit 11 is not limited to acquiring data based on a single numerical value as data based on subjective data regarding a person's stress, but may acquire data based on multiple numerical values, and may also acquire data of any value, not limited to numerical values.

[0017] Furthermore, the data acquisition unit 11 acquires, as learning data, first data that is data based on biometric data of a person during the person's daily life or work. For example, as shown in FIG. 1, the data acquisition unit 11 acquires the heart rate of the person U as biometric data via a measurement device such as a wearable terminal W worn by the person U, or acquires the degree of eye opening extracted from a facial image of the person U captured by a camera (not shown) as biometric data. Note that the data acquisition unit 11 may acquire, as learning data, the measured biometric data itself or first data consisting of feature amounts extracted from the measured biometric data. However, the data acquisition unit 11 may acquire any biometric data using any measurement device as the person's biometric data.

[0018] Furthermore, the data acquisition unit 11 acquires, as learning data, third data representing the stress state of the person U at the time the person's biometric data was measured as the first data, as described above. For example, the data acquisition unit 11 acquires data based on the subjective data regarding the person U's stress by asking the person U preset questions via the input device 20 during or within a predetermined period before and after the acquisition of the biometric data, and acquiring and compiling responses from the person U. In this case, the data acquired as the third data is data calculated from responses to multiple questions asking about the person U's psychological burden around the time the biometric data was measured. For example, if the PSS score described above is used as the third data, it represents the stress state of the person U for approximately one month from the time the biometric data was measured. However, the third data may also be data based on responses to another stress test, representing the stress state during the measurement of the person's biometric data or the stress state for a predetermined period including the time the biometric data was measured.

[0019] The learning unit 12 performs machine learning using the acquired learning data as described above to generate a stress calculation model for calculating a stress value representing the stress state of person U. Specifically, before the machine learning, the learning unit 12 first calculates a first stress value from the biometric data of person U measured as described above or from first data that is a feature of the biometric data. At this time, the learning unit 12 inputs the first data into the first stress calculation model stored in the first model storage unit 17, and calculates the first stress value that is the output. Note that the first stress calculation model is configured as a model that is preset to calculate a stress value corresponding to the biometric data of person U. For example, the first stress calculation model may be a model generated by machine learning of biometric data and subjective data related to stress previously acquired from a person, a model based on an arithmetic formula generated by manually examining the biometric data and subjective data, or a model based on an arithmetic formula generated from biometric data according to a predetermined theory. As an example, the first stress calculation model is a model configured to input the first data, which is the biometric data of person U, and calculate a value of 0-40 as an output, similar to the PSS score, which is the second data described above. However, the first stress calculation model may be a model that calculates a value in any format as an output.

[0020] Next, the learning unit 12 generates a second stress calculation model that calculates a second stress value by learning using the first stress calculation model from the first data based on the biometric data of person U as described above, and second data that is subjective data related to stress, such as a PSS score previously acquired from person U. At this time, the learning unit 12 generates the second stress calculation model by machine learning using the first stress value and the second data as explanatory variables and third data that represents the stress state of person U at the time the biometric data from which the first stress value was calculated was acquired as a target variable. Then, the learning unit 12 stores the generated second stress calculation model in the second model storage unit 18.

[0021] Next, the functions of each unit when estimating the stress of a target person (target person) using the second stress calculation model after generating the second stress calculation model as described above will be described. For example, assume that the target person U constantly measures biological data using a wearable device or the like as described below, and estimates a chronic stress value once a day. However, this also includes situations in which the person U estimates stress with any frequency, such as estimating an acute stress value every hour. Note that the target person U is a person different from the arbitrary large number of people who provided the learning data as described above, but may be any of the arbitrary people who provided the learning data.

[0022] The data acquisition unit 11 (acquisition unit) acquires data used to estimate the stress of the target person U. Specifically, the data acquisition unit 11 first acquires in advance a "PSS score" obtained by aggregating "Perceived Stress Scale (PSS)" as second data, which is data based on subjective data regarding the stress of the target person U. The PSS score as the acquired second data is data acquired in the same manner as the learning data described above, and is, for example, a value of 1 to 40 calculated based on answers input by the person U via the input device 20 to questions such as those shown in FIG. 2, as described above. The data acquisition unit 11 then stores the acquired PSS score in the person information storage unit 16 as second data of the target person U. Note that the second data of the target person U is acquired and updated, for example, when the person U registers in a stress estimation system, such as when the person U belongs to a workplace, or periodically, such as once a year. However, the data acquisition unit 11 is not limited to acquiring data consisting of a single value such as the PSS score described above as data used to estimate stress, but may acquire data consisting of multiple numerical values, and may also acquire data of any value, not limited to numerical values.

[0023] Furthermore, after previously acquiring second data such as a PSS score from the target person U as described above, the data acquiring unit 11 acquires first data, which is data based on biometric data, from the target person U when the target person U actually estimates stress. For example, as shown in FIG. 1 , the data acquiring unit 11 acquires, as biometric data, the heart rate of the target person U, which is constantly measured by a measuring device such as a wearable device W worn by the target person U, or acquires, as biometric data, the degree of eye opening extracted from a facial image of the target person U captured by a camera (not shown). The data acquiring unit 11 then acquires the first data based on biometric data from the target person U at predetermined timings (e.g., at regular time intervals) or at arbitrary timings (irregularly). For example, the data acquiring unit 11 acquires the first data daily when estimating the target person U's chronic stress, or hourly when measuring acute stress. Note that the data acquiring unit 11 may acquire the measured biometric data itself as the first data, or may acquire, as the first data, feature quantities extracted from the measured biometric data using a predetermined method. However, the data acquisition unit 11 may acquire any biometric data of a person using any measuring device.

[0024] As described above, each time first calculation unit 13 acquires first data based on biometric data from person U, it calculates a first stress value from the first data. At this time, first calculation unit 13 reads out the first stress calculation model stored in first model storage unit 17, inputs the acquired first data into the first stress calculation model, and calculates the first stress value, which is the output of the first stress calculation model. That is, data acquisition unit 11 and first calculation unit 13 cooperate to acquire first data based on biometric data from person U at predetermined timing, such as a fixed time interval, and calculate a first stress value based on the biometric data each time the first data is acquired. For example, first calculation unit 13 may use the first stress calculation model to calculate a value of 0-40 as an output, similar to the PSS score, which is the second data described above.

[0025] Each time first calculation unit 13 calculates a first stress value, second calculation unit 14 calculates a second stress value based on the first stress value and second data based on subjective data related to stress of person U that has been previously acquired from person U. Specifically, second calculation unit 14 reads out the second stress calculation model stored in second model storage unit 18, and inputs the calculated first stress value and the previously acquired second data into the second stress calculation model, thereby calculating the second stress value that is its output. In other words, second calculation unit 14 calculates the second stress value each time a predetermined timing occurs, such as a fixed time interval at which biometric data is acquired from person U, as described above.

[0026] The output unit 15 outputs information based on the second stress value calculated by the second calculation unit 14. For example, each time the second stress value is calculated, if the second stress value exceeds a predetermined threshold value for determining high stress, the output unit 15 outputs an alert to that effect on the display device 30 of an information processing device operated by a workplace manager, family member, or the like of person U. Alternatively, each time the second stress value is calculated, the output unit 15 may always output the second stress value itself, i.e., the time-series change in person U's second stress value, or may output any data based on the second stress value. Furthermore, the output unit 15 may output data based on the second stress value to any person, such as person U.

[0027] [Operation] Next, the operation of the stress estimation device 10 will be described mainly with reference to the flowcharts of Figures 3 and 4. First, with reference to the flowchart of Figure 3, the operation when generating a stress calculation model by machine learning will be described.

[0028] First, the stress estimation device 10 acquires, as learning data, second data that is data based on subjective data related to stress of any plurality of persons, in this case, "PSS scores" obtained by aggregating "Perceived Stress Scales (PSS)" (step S1). As an example, the stress estimation device 10 acquires, as the second data, a value of 0 to 40 points obtained by aggregating scores of 0 to 4 points corresponding to answers on a five-point scale to 10 questions as shown in FIG. 2. However, the stress estimation device 10 may acquire data of any value as the second data that is data based on subjective data related to stress of a person.

[0029] Thereafter, the stress estimation device 10 acquires, as learning data, first data that is data based on biometric data of any plurality of persons (step S2). As an example, the stress estimation device 10 acquires, as biometric data, the heart rate of person U or the degree of eye opening extracted from a facial image of person U, and acquires the biometric data itself or feature quantities of the biometric data as the first data. However, the stress estimation device 10 may acquire, as the first data, any biometric data of a person.

[0030] Furthermore, the data acquisition unit 11 acquires, as learning data, third data that represents the stress state of the person U at the time the biometric data was measured as described above (step S3). As an example, the stress estimation device 10 acquires the third data based on subjective data related to stress of the person U by asking the person U questions about stress around the time the biometric data was measured and acquiring and compiling the answers from the person U. In other words, the third data represents the stress value actually experienced by the person at the time the biometric data was measured. However, the stress estimation device 10 may acquire data representing the person's stress as the third data by any method.

[0031] The stress estimation device 10 then performs machine learning using the learning data, including the first data, second data, and third data acquired as described above, to generate a stress calculation model for calculating a stress value representing the stress state of the person U (step S4). Specifically, the stress estimation device 10 first calculates a first stress value from the measured biometric data of the person U itself or the first data, which is a feature of the biometric data. The stress estimation device 10 inputs the first data into the first stress calculation model stored in the first model storage unit 17, and calculates the first stress value, which is the output of the first data. The first stress calculation model is a model pre-configured to calculate a stress value corresponding to the biometric data of the person U. As an example, the stress estimation device 10 inputs the first data, which is the biometric data of the person U, into the first stress calculation model, and calculates a value of 0-40 as the first stress value, similar to the PSS score described above. However, the stress estimation device 10 may calculate the first stress value in any format by inputting the first data into the first stress calculation model.

[0032] Next, the stress estimation device 10 generates a second stress calculation model that calculates a second stress value by learning using the calculated first stress value and second data, which is subjective data related to stress, such as a PSS score previously acquired from person U. As an example, the stress estimation device 10 generates the second stress calculation model by machine learning using the first stress value and the second data as explanatory variables and third data representing the stress state of person U at the time the biological data from which the first stress value was calculated was acquired as a target variable. Then, the stress estimation device 10 stores the generated second stress calculation model in the second model storage unit 18.

[0033] Next, with reference to the flowchart in FIG. 4, an operation for estimating the stress of a target person using the generated stress calculation model will be described.

[0034] First, the stress estimation device 10 acquires and stores in advance a "PSS score" obtained by aggregating "Perceived Stress Scale (PSS)" as second data, which is data based on subjective data related to stress of the target person U (step S11). The PSS score acquired as second data at this time is data acquired in the same way as the learning data described above, and as an example, the stress estimation device 10 acquires a value of 0-40 as the second data. However, the stress estimation device 10 may acquire data of any value as data used to estimate stress.

[0035] Next, the stress estimation device 10 acquires first data, which is data based on biometric data from the target person U, at the timing of estimating the stress of the target person U (step S12). As an example, the stress estimation device 10 acquires biometric data itself, such as the person U's heart rate or the degree of eye opening extracted from a facial image of the person U, or feature quantities of the biometric data, as the first data (step S13). As shown in step S16, the stress estimation device 10 acquires the first data based on the biometric data from the person U at predetermined timings, for example, at regular time intervals, until the period for estimating the person's stress ends. As an example, the first data is acquired daily when estimating the person U's chronic stress, or hourly when measuring acute stress. The stress estimation device 10 may acquire any biometric data as the person's biometric data.

[0036] Next, when the stress estimation device 10 acquires first data based on the biometric data from the person U, it inputs the first data into a first stress calculation model and calculates a first stress value (step S13). As an example, the stress estimation device 10 calculates a value of 0-40 as the first stress value, similar to the PSS score, which is the second data described above.

[0037] The stress estimation device 10 then inputs the calculated first stress value and second data based on subjective data related to stress of the target person U that has been previously acquired from the target person U and stored into a second stress calculation model to calculate a second stress value (step S14).The stress estimation device 10 then outputs stress information about the target person U, such as the calculated second stress value itself and an alert based on the second stress value (step S15).

[0038] The stress estimation device 10 then repeatedly calculates the second stress value in the same manner as described above until the stress estimation period for the target person U ends (No in step S16). That is, when it is time to perform the next stress estimation, the stress estimation device 10 acquires first data based on biometric data from the person U (step S12), calculates a first stress value from the first data using a first stress calculation model (step S13), inputs the calculated first stress value and second data based on previously acquired subjective data about the person U's stress into the second stress calculation model, and calculates a second stress value (step S14). In this way, the stress estimation device 10 continues to estimate the stress state of the target person U.

[0039] Here, we will explain the effect of calculating stress values ​​(first stress value, second stress value) in two stages using biological data and subjective data related to stress as described above. "MAE (Mean Absolute Error)" in Figure 5 represents the average absolute value of the difference between the estimated stress value and the correct value. As the numerical values ​​in this figure indicate, the method of the present invention is evaluated to have a smaller value than previous methods, indicating better accuracy. Furthermore, the "correlation coefficient" in Figure 5 indicates the strength and direction of the relationship between the estimated stress value and the correct value. As the numerical values ​​in this figure indicate, the method of the present invention is evaluated to have a larger positive value than previous methods, indicating better accuracy.

[0040] <Embodiment 2> Next, a second embodiment of the present invention will be described with reference to Figures 6 to 8. Figures 6 to 7 are block diagrams showing the configuration of a stress estimation device in embodiment 2, and Figure 8 is a flowchart showing the operation of the stress estimation device. Note that this embodiment shows an outline of the configuration of the stress estimation device and stress estimation method described in the above embodiments.

[0041] First, the hardware configuration of the stress estimation device 100 in this embodiment will be described with reference to Fig. 6. The stress estimation device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, for example. ·CPU(Central Processing Unit)101(Arithmetic unit) ROM (Read Only Memory) 102 (storage device) RAM (Random Access Memory) 103 (storage device) Programs 104 loaded into RAM 103 A storage device 105 for storing a group of programs 104 A drive device 106 that reads and writes from a storage medium 110 external to the information processing device A communication interface 107 that connects to a communication network 111 outside the information processing device Input / output interface 108 for inputting and outputting data Bus 109 connecting each component

[0042] The stress estimation device 100 can be equipped with a first calculation unit 121 and a second calculation unit 122 shown in Fig. 7 by having the CPU 101 acquire and execute the program group 104. The program group 104 is stored in advance in, for example, the storage device 105 or the ROM 102, and is loaded into the RAM 103 and executed by the CPU 101 as needed. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, and the drive device 106 may read out the programs and supply them to the CPU 101. However, the first calculation unit 121 and the second calculation unit 122 may be constructed using dedicated electronic circuits for realizing such means.

[0043] 6 shows an example of the hardware configuration of the information processing device that is the stress estimation device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with a part of the above-described configuration, such as excluding the drive device 106.

[0044] The stress estimation device 100 then executes the stress estimation method shown in the flowchart of FIG. 8 by the functions of the first calculation unit 121 and the second calculation unit 122 constructed by the program as described above.

[0045] As shown in FIG. 8, the stress estimation device 100 A first stress value is calculated by inputting first data based on biological data measured from a target person into a first stress calculation model (step S101); A second stress value is calculated by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model (step S102). Execute the process, the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; The structure is as follows.

[0046] As configured as described above, the present invention can estimate a person's stress with high accuracy by calculating a stress value (a first stress value and a second stress value) in two stages using the person's biometric data and subjective data related to stress.

[0047] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can supply the program to a computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0048] Although the present invention has been described above with reference to the above-described embodiments, the present invention is not limited to the above-described embodiments. Various modifications that are understandable to those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. Furthermore, at least one or more functions of the first calculation unit 121 and the second calculation unit 122 described above may be executed by an information processing device installed and connected anywhere on a network, that is, may be executed by so-called cloud computing.

[0049] <Additional Notes> A part or all of the above-described embodiments can be described as follows: The following provides an outline of the configurations of the stress estimation method, stress estimation device, and program according to the present invention. However, the present invention is not limited to the following configurations. (Appendix 1) Calculating a first stress value by inputting first data based on biometric data measured from the target person into a first stress calculation model; calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; Stress estimation methods. (Appendix 2) 10. The stress estimation method according to claim 1, further comprising: The second stress calculation model is generated by learning using the first stress value calculated based on biological data measured from a specific person and the second data previously acquired from the specific person as explanatory variables, and third data related to stress acquired when measuring the biological data from the specific person as a dependent variable. Stress estimation methods. (Appendix 3) 3. The stress estimation method according to claim 2, further comprising: The third data is data based on subjective data regarding stress of a predetermined person. Stress estimation methods. (Appendix 4) 4. The stress estimation method according to any one of Supplementary Notes 1 to 3, Acquire the second data from the target person in advance; Thereafter, the first data based on the biological data acquired from the target person is input to the first stress calculation model to calculate the first stress value; calculating the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model; Stress estimation methods. (Appendix 5) 5. The stress estimation method according to claim 4, every time biometric data is acquired from a target person, the first data based on the biometric data is input to the first stress calculation model to calculate the first stress value; every time the first stress value is calculated, the calculated first stress value and the second data acquired in advance are input into the second stress calculation model to calculate the second stress value; Stress estimation methods. (Appendix 6) 6. The stress estimation method according to claim 4 or 5, acquiring biometric data from the target person at a predetermined timing, and inputting the first data based on the acquired biometric data into the first stress calculation model to calculate the first stress value, every time the first stress value is calculated, the calculated first stress value and the second data acquired in advance are input to the second stress calculation model to calculate the second stress value, and information based on the calculated second stress value is output. Stress estimation methods. (Appendix 7) 7. The stress estimation method according to any one of Supplementary Notes 4 to 6, acquiring the second data calculated based on the target person's answers to predetermined questions; Stress estimation methods. (Appendix 8) a first calculation unit that calculates a first stress value by inputting first data based on biological data measured from a target person into a first stress calculation model; a second calculation unit that calculates a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; Equipped with the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; Stress estimation device. (Appendix 9) 9. The stress estimation device according to claim 8, The second stress calculation model is generated by learning using the first stress value calculated based on biological data measured from a specific person and the second data previously acquired from the specific person as explanatory variables, and third data related to stress acquired when measuring the biological data from the specific person as a dependent variable. Stress estimation device. (Appendix 10) 10. The stress estimation device according to claim 9, The third data is data based on subjective data regarding stress of a predetermined person. Stress estimation device. (Appendix 11) 11. The stress estimation device according to any one of Supplementary Notes 8 to 10, an acquisition unit that acquires the second data from a target person in advance; the first calculation unit then inputs the first data based on the biometric data acquired from the target person into the first stress calculation model to calculate the first stress value; the second calculation unit calculates the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model; Stress estimation device. (Appendix 12) 12. The stress estimation device according to claim 11, the first calculation unit calculates the first stress value by inputting the first data based on the biometric data into the first stress calculation model every time biometric data is acquired from the target person; the second calculation unit calculates the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model every time the second calculation unit calculates the first stress value; Stress estimation device. (Appendix 13) 13. The stress estimation device according to claim 11, the first calculation unit repeatedly acquires biometric data from the target person at a preset timing, and calculates the first stress value by inputting the first data based on the acquired biometric data into the first stress calculation model; the second calculation unit calculates the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model every time the second calculation unit calculates the first stress value, and outputs information based on the calculated second stress value. Stress estimation device. (Appendix 14) 14. The stress estimation device according to any one of Supplementary Notes 11 to 13, the acquiring unit acquires the second data calculated based on a response by the target person to a preset question. Stress estimation device. (Appendix 15) In the information processing device, Calculating a first stress value by inputting first data based on biometric data measured from the target person into a first stress calculation model; calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; It is a program for executing a process, the second stress calculation model is generated by learning using the first stress value of a predetermined person previously acquired and the second data; A computer-readable storage medium storing a program characterized by: [Explanation of symbols]

[0050] 10 Stress estimation device 11 Data Acquisition Section 12 Learning Department 13 First calculation section 14 Second calculation section 15 Output section 16 Person information storage section 17 First model storage unit 18 Second model storage unit 20 Input Devices 30 Display device 100 Stress Estimation Device 101 CPU 102 ROM 103 RAM 104 Programs 105 Storage device 106 Drive device 107 Communication Interface 108 Input / Output Interface 109 Bus 110 Storage medium 111 Communication Network 121 First Calculation Department 122 Second calculation section

Claims

1. calculating a first stress value by inputting first data based on biological data measured from the target person into a first stress calculation model; calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; the second stress calculation model is generated by learning using the first stress value and the second data of a predetermined person previously obtained, and is further generated by learning using the first stress value calculated based on biological data measured from the predetermined person and the second data previously obtained from the predetermined person as explanatory variables, and third data related to stress obtained when the biological data of the predetermined person is measured as a dependent variable. Stress estimation methods.

2. The stress estimation method according to claim 1, The third data is data based on subjective data regarding stress of a predetermined person. Stress estimation methods.

3. 3. The stress estimation method according to claim 1 or 2, acquiring the second data from the target person in advance; Thereafter, the first data based on the biological data acquired from the target person is input to the first stress calculation model to calculate the first stress value; calculating the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model; Stress estimation methods.

4. The stress estimation method according to claim 3, every time biometric data is acquired from a target person, the first data based on the biometric data is input to the first stress calculation model to calculate the first stress value; every time the first stress value is calculated, the calculated first stress value and the second data acquired in advance are input to the second stress calculation model to calculate the second stress value; Stress estimation methods.

5. 5. The stress estimation method according to claim 3 or 4, acquiring biometric data from the target person at a predetermined timing, and inputting the first data based on the acquired biometric data into the first stress calculation model to calculate the first stress value, and repeating this process; every time the first stress value is calculated, the calculated first stress value and the second data acquired in advance are input to the second stress calculation model to calculate the second stress value, and information based on the calculated second stress value is output. Stress estimation methods.

6. 6. The stress estimation method according to claim 3, further comprising: acquiring the second data calculated based on the target person's answers to predetermined questions; Stress estimation methods.

7. a first calculation unit that calculates a first stress value by inputting first data based on biological data measured from a target person into a first stress calculation model; a second calculation unit that calculates a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from the target person into a second stress calculation model; Equipped with the second stress calculation model is generated by learning using the first stress value and the second data of a predetermined person previously obtained, and is further generated by learning using the first stress value calculated based on biological data measured from the predetermined person and the second data previously obtained from the predetermined person as explanatory variables, and third data related to stress obtained when the biological data of the predetermined person is measured as a dependent variable. Stress estimation device.

8. The stress estimation device according to claim 7, an acquisition unit that acquires the second data from a target person in advance; the first calculation unit then inputs the first data based on the biological data acquired from the target person into the first stress calculation model to calculate the first stress value; the second calculation unit calculates the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model; Stress estimation device.

9. In the information processing device, calculating a first stress value by inputting first data based on biological data measured from the target person into a first stress calculation model; calculating a second stress value by inputting the first stress value and second data based on subjective data on stress acquired from the target person into a second stress calculation model; It is a program for executing a process, the second stress calculation model is generated by learning using the first stress value and the second data of a predetermined person previously obtained, and is further generated by learning using the first stress value calculated based on biological data measured from the predetermined person and the second data previously obtained from the predetermined person as explanatory variables, and third data related to stress obtained when the biological data of the predetermined person is measured as a dependent variable. A program characterized by:

10. The program according to claim 9, The information processing device further includes: acquiring the second data from the target person in advance; Thereafter, the first data based on the biological data acquired from the target person is input to the first stress calculation model to calculate the first stress value; calculating the second stress value by inputting the calculated first stress value and the second data acquired in advance into the second stress calculation model; A program for executing a process.

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