WO2023105790A1 - ストレス推定方法 - Google Patents

ストレス推定方法 Download PDF

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Publication number
WO2023105790A1
WO2023105790A1 PCT/JP2021/045653 JP2021045653W WO2023105790A1 WO 2023105790 A1 WO2023105790 A1 WO 2023105790A1 JP 2021045653 W JP2021045653 W JP 2021045653W WO 2023105790 A1 WO2023105790 A1 WO 2023105790A1
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Prior art keywords
stress
data
value
person
stress value
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English (en)
French (fr)
Japanese (ja)
Inventor
祐 北出
剛範 辻川
佳祐 鈴木
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NEC Corp
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NEC Corp
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Priority to US18/713,777 priority Critical patent/US20250037873A1/en
Priority to JP2023566061A priority patent/JP7740372B2/ja
Priority to PCT/JP2021/045653 priority patent/WO2023105790A1/ja
Publication of WO2023105790A1 publication Critical patent/WO2023105790A1/ja
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to a stress estimation method, a stress estimation device, and a program.
  • Known methods for estimating a person's stress include a method based on the person's subjectivity and a method based on the person's biological information.
  • stress is estimated based on a person's response to a predetermined questionnaire.
  • the stress is estimated based on the biometric information of the person acquired from the wearable terminal or the image.
  • the final stress is estimated based on the subjective stress value of the person and the stress value based on the person's biological information.
  • a coordinate plane defined by a coordinate axis related to the subjective stress value and a coordinate axis related to the objective stress value is prepared, and the subjective stress value and the objective stress value obtained on the coordinate plane are positioned.
  • stress is estimated by calculating a total stress value using a subjective stress value, an objective stress value, and a predetermined function.
  • the final stress state is determined based on the subjective stress value and the objective stress value based on preset criteria (coordinate planes and functions), and the criteria for determination are uniform. Therefore, it is difficult to estimate stress with higher accuracy.
  • an object of the present invention is to provide a stress estimation method that can solve the above-mentioned problem that stress cannot be estimated with higher accuracy.
  • a stress estimation method which is one embodiment of the present invention, comprises: calculating a first stress value by inputting first data based on biological data measured from a target person into the 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 a 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 obtained in the past and the second data, take the configuration.
  • the stress estimation device which is one embodiment of the present invention, a first calculation unit that calculates a first stress value by inputting first data based on biometric data measured from a target person to the first stress calculation model; 2) calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from a target person to a second stress calculation model; a calculation unit; with The second stress calculation model is generated by learning using the first stress value of a predetermined person obtained in the past and the second data, take the configuration.
  • a program that is one embodiment of the present invention is information processing equipment, calculating a first stress value by inputting first data based on biological data measured from a target person into the 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 a target person into a second stress calculation model; It is a program for executing processing, The second stress calculation model is generated by learning using the first stress value of a predetermined person obtained in the past and the second data, take the configuration.
  • the present invention can estimate stress with higher accuracy.
  • FIG. 1 is a block diagram showing the configuration of a stress estimation device according to Embodiment 1 of the present invention
  • FIG. FIG. 2 is a diagram showing an example of a test for acquiring subjective data on a person's stress that is input to the stress estimating device disclosed in FIG. 1; 2 is a flow chart showing the operation of the stress estimating device disclosed in FIG. 1; 2 is a flow chart showing the operation of the stress estimating device disclosed in FIG. 1; It is a figure for demonstrating the effect of the stress estimation apparatus in Embodiment 1 of this invention. It is a block diagram which shows the hardware constitutions of the stress estimation apparatus in Embodiment 2 of this invention.
  • FIG. 4 is a block diagram showing the configuration of a stress estimation device according to Embodiment 2 of the present invention; 9 is a flowchart showing the operation of the stress estimating device according to Embodiment 2 of the present invention;
  • FIG. 1 and 2 are diagrams for explaining the configuration of the stress estimation device
  • FIGS. 3 and 4 are diagrams for explaining the processing operation of the stress estimation device.
  • the stress estimating device 10 in the present invention is used for estimating the stress of a person.
  • the stress estimating device 10 is used to calculate a stress value representing a person's chronic or acute stress state.
  • the stress estimation device 10 in the present invention may calculate any stress value of a person.
  • the stress estimation device 10 is composed of one or a plurality of information processing devices each having an arithmetic device and a storage device.
  • 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, as shown in FIG.
  • Each function 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 executes a program for realizing each function stored in the storage device. It can be realized by
  • 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 by storage devices. Each configuration will be described in detail below.
  • the data acquisition unit 11 acquires data used for estimating a person's stress. First, the data acquisition unit 11 acquires learning data for generating a stress calculation model used for calculating a stress value by machine learning.
  • the learning data is data of a large number of arbitrary persons (predetermined persons), and is stored in the person information storage unit 16 in association with each person.
  • the data acquisition unit 11 acquires, as learning data, second data that is data based on subjective data regarding stress of a person.
  • the data acquisition unit 11 acquires the "PSS score" obtained by aggregating the "perceived stress scale (PSS)" as the second data based on the subjective data regarding the person's stress. do.
  • the PSS consists of 14 preset questions asking how the user feels about what is happening, and 5 levels of answers are prepared. It is what is done. A score of 0 to 4 points is assigned to the answers on a scale of 5, and the total score of the answers to the 10 questions to be calculated among the 14 questions is calculated as the PSS score. Therefore, the PSS score ranges from 0 to 40 points.
  • the data acquisition unit 11 displays PSS questions as shown in FIG. It obtains the answers input from the , and obtains the PSS score by aggregating the answers.
  • the data acquisition unit 11 is not limited to data based on a single numerical value, and may acquire data based on a plurality of numerical values as data based on subjective data regarding stress of a person. data may be obtained.
  • the data acquisition unit 11 acquires, as learning data, first data that is data based on the person's biometric data 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 measuring device such as a wearable terminal W worn by the person U, or acquires the heart rate of the person U using a camera (not shown). The degree of eye openness extracted from the face image of the person U photographed by the camera is obtained as biometric data. Note that the data acquisition unit 11 may acquire the measured biological data itself or the first data composed of the feature amount extracted from the measured biological data as the learning data. However, the data acquisition unit 11 may acquire any biometric data using any measuring device as the biometric data of a person.
  • the data acquisition unit 11 also acquires, as learning data, the third data representing the stress situation of the person U when the person's biometric data is measured as the first data as described above.
  • the data acquisition unit 11 poses preset questions to the person U via the input device 20 during the acquisition of biometric data or within a predetermined period before and after the acquisition, and acquires an answer from the person U.
  • Data based on the subjective data regarding the stress of the person U is acquired by summarizing the data.
  • the data acquired as the third data is data calculated from answers to a plurality of questions about the degree of psychological burden on the person U around the time when the biometric data was measured.
  • the third data when the above-described PSS score is used as the third data, it represents the stress state for about one month when the person's biometric data is measured.
  • the third data is data based on responses to other stress tests, and includes the stress state during the measurement of the person's biometric data and the stress during a predetermined period including when the biometric data was measured. Data representing a state may be used.
  • the learning unit 12 performs machine learning using the learning data acquired as described above, and generates a stress calculation model for calculating a stress value representing the stress situation of the person U. Specifically, before machine learning, the learning unit 12 first calculates a first stress value from the biometric data itself of the person U measured as described above or the first data that is the feature amount of the biometric data. do. At this time, the learning unit 12 inputs the first data to the first stress calculation model stored in the first model storage unit 17, and calculates the first stress value as the output. .
  • the first stress calculation model is configured by a model that is set in advance so as to calculate the stress value corresponding to the person U's biometric data.
  • 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, or may be a model generated by manually examining biometric data and subjective data. It may be a model based on an arithmetic expression generated by the above method, or a model based on an arithmetic expression generated from biometric data according to a predetermined theory.
  • the first stress calculation model uses the first data, which is the biological data of the person U, as an input, and calculates a value between 0 and 40 as an output in the same manner as the PSS score, which is the second data described above.
  • This model is configured as follows.
  • the first stress calculation model may be a model that calculates any type of value as an output.
  • the learning unit 12 calculates the first stress value calculated using the first stress calculation model from the first data based on the biometric data of the person U as described above, and the PSS obtained from the person U in advance.
  • a second stress calculation model for calculating a second stress value is generated by learning using second data, which is subjective data related to stress such as a score.
  • the learning unit 12 uses the first stress value and the second data as explanatory variables, and uses the third stress value representing the stress situation of the person U when the biometric data for calculating the first stress value is acquired. data is used as an objective variable, machine learning is performed to generate a second stress calculation model. Then, the learning unit 12 stores the generated second stress calculation model in the second model storage unit 18 .
  • the target person U constantly measures biometric data with a wearable terminal or the like, as will be described later, and estimates the chronic stress value once a day. However, it also includes scenes in which the person U estimates the stress at any frequency, such as estimating the acute stress value every hour. Note that the target person U is a person different from the large number of arbitrary persons who provided the learning data as described above, but any one of the arbitrary persons who provided the learning data may correspond. .
  • the data acquisition unit 11 acquires data used for estimating the stress of the target person U. Specifically, the data acquisition unit 11 first obtains in advance a "perceived stress scale (PSS)" as second data based on subjective data on the stress of the target person U. to obtain a "PSS score".
  • PSS perceived stress scale
  • the PSS score as the second data acquired at this time is data acquired in the same manner as the learning data described above. It is a value between 1 and 40 calculated based on the answer input from person U via . Then, the data acquisition unit 11 stores the acquired PSS score in the person information storage unit 16 as the second data of the person U who is the target.
  • the second data of the target person U is acquired and updated periodically, for example, when the person U is registered in a system for estimating stress, such as when the person U belongs to the workplace, or once a year.
  • the data used for estimating stress is not limited to the PSS score consisting of one value as described above, and the data acquisition unit 11 may acquire data using a plurality of numerical values. Data of any value may be acquired without limitation.
  • First data based on biometric data is obtained from the person U.
  • the data acquisition unit 11 acquires the heart rate of the person U, which is always measured by a measuring device such as a wearable terminal W worn by the person U, as biometric data, The eye opening degree extracted from the face image of the person U photographed by a camera (not shown) is obtained as biometric data.
  • the data acquisition unit 11 acquires the first data based on the biometric data from the person U at preset timing (for example, at regular time intervals) or arbitrary timing (irregular).
  • the data acquisition unit 11 acquires the first data every day when estimating the chronic stress of the target person U, and every hour when measuring acute stress. do.
  • the data acquisition unit 11 may acquire the measured biological data itself as the first data, or may acquire the feature amount extracted from the measured biological data by a preset method as the first data. good.
  • the data acquisition unit 11 may acquire any biometric data using any measuring device as the biometric data of a person.
  • the first calculation unit 13 Every time the first calculation unit 13 acquires the first data based on the biometric data from the person U as described above, the first calculation unit 13 calculates the first stress value from the first data. At this time, the first calculation unit 13 reads out the first stress calculation model stored in the first model storage unit 17, and inputs the obtained first data to the first stress calculation model. Thus, the first stress value, which is the output, is calculated. That is, the data acquisition unit 11 and the first calculation unit 13 cooperate to acquire the first data based on the biometric data from the person U at predetermined timing such as a certain time interval; Calculating the first stress value based on the biological data is repeated each time the first data is acquired. For example, the first calculation unit 13 may use the first stress calculation model to calculate a value between 0 and 40 as an output in the same manner as the PSS score, which is the second data described above.
  • the second calculation unit 14 calculates a second and a second stress value is calculated. Specifically, the second calculation unit 14 reads out the second stress calculation model stored in the second model storage unit 18, and calculates the calculated first stress value and , and second data obtained in advance are input to calculate the second stress value, which is the output. That is, the second calculation unit 14 calculates the second stress value each time a preset timing such as a certain time interval for acquiring biometric data from the person U as described above comes.
  • the output unit 15 outputs information based on the second stress value calculated by the second calculation unit 14. For example, every time the second stress value is calculated, the output unit 15 detects that the second stress value exceeds a preset reference value for determining that the stress is high. An alert is output to the display device 30 of the information processing apparatus operated by the administrator or family of the person. Alternatively, the output unit 15 may always output the second stress value itself, that is, the chronological change in the second stress value of the person U, every time the second stress value is calculated. Any data based on the second stress value may be output. In addition, the output unit 15 may output the data based on the second stress value to any person, such as the person U who is the target.
  • the stress estimating apparatus 10 uses, as learning data, second data that is data based on subjective data regarding stress of any plurality of persons, here, a "perceived stress scale (PSS)". is acquired (step S1).
  • PSS perceived stress scale
  • the stress estimating device 10 uses, as a PSS score, a value of 0 to 40 points obtained by aggregating scores of 0 to 4 points corresponding to 5-level answers to 10 questions as shown in FIG. data.
  • the stress estimating apparatus 10 may acquire data of any value as the second data based on subjective data regarding stress of a person.
  • the stress estimation device 10 acquires, as learning data, first data based on the biometric data of any plurality of persons (step S2).
  • the stress estimation apparatus 10 acquires the heart rate of the person U and the degree of eye opening extracted from the face image of the person U as biometric data, and acquires the biometric data itself and the feature amount of the biometric data as the first data.
  • the stress estimation device 10 may acquire any biometric data of a person as the first data.
  • the data acquisition unit 11 acquires, as learning data, third data representing the state of stress of the person U when the biometric data is measured as described above (step S3).
  • the stress estimating apparatus 10 asks a question about stress to the person U around the time when the biological data is measured, and acquires and aggregates the answers from the person U, thereby obtaining subjective information about the stress of the person U.
  • the stress estimating device 10 may acquire data representing the stress of a person by any method as the third data.
  • the stress estimating device 10 performs machine learning using learning data including the first data, the second data, and the third data acquired as described above, and performs the stress A stress calculation model for calculating the value is generated (step S4). Specifically, the stress estimation device 10 first calculates a first stress value from the measured biometric data of the person U or the first data that is the feature amount of the biometric data. At this time, the stress estimating device 10 inputs the first data to the first stress calculation model stored in the first model storage unit 17, and calculates the first stress value as the output. do.
  • the first stress calculation model is a model set in advance so as to calculate a stress value corresponding to the person U's biometric data.
  • the stress estimating apparatus 10 inputs the first data, which is the biometric data of the person U, to the first stress calculation model, and calculates the value of 0-40 as the first stress value in the same manner as the PSS score described above. Calculate as a value.
  • 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.
  • the stress estimating apparatus 10 learns using the calculated first stress value and the second data, which is subjective data related to stress such as the PSS score acquired in advance from the person U, to obtain the second stress value. generates a second stress calculation model for calculating the stress value of As an example, the stress estimating apparatus 10 uses the first stress value and the second data as explanatory variables, and uses the first stress value and the second data as explanatory variables, and the stress estimating device 10 obtains the first stress value representing the stress situation of the person U when the biometric data obtained by calculating the first stress value is acquired. A second stress calculation model is generated by performing machine learning using the data of 3 as an objective variable. Then, the stress estimation device 10 stores the generated second stress calculation model in the second model storage unit 18 .
  • the stress estimating device 10 previously aggregated a "perceived stress scale (PSS)" as second data based on subjective data on the stress of the target person U.
  • a "PSS score” is acquired and stored (step S11).
  • the PSS score as the second data acquired at this time is data acquired in the same manner as the learning data described above.
  • the stress estimation device 10 acquires a value of 0-40 as the second data.
  • the stress estimation device 10 may acquire data of any value as data used for estimating stress.
  • the stress estimating device 10 acquires the first data based on the biological data from the target person U at the timing of estimating the stress of the target person U (step S12).
  • the stress estimating apparatus 10 acquires biometric data such as the heart rate of the person U and the degree of eye opening extracted from the face image of the person U, or the feature amount of the biodata as the first data (step S13).
  • the stress estimating apparatus 10 until the period for estimating the stress of the person ends, at preset timings, for example, at regular time intervals, Get the first data.
  • the first data is obtained every day when estimating the chronic stress of the person U, and every hour when measuring the acute stress.
  • the stress estimation device 10 may acquire any biometric data as the biometric data of the person.
  • the stress estimation device 10 acquires the first data based on the biometric data from the person U
  • the stress estimation device 10 inputs the first data into the first stress calculation model to calculate the first stress value (step S13).
  • the stress estimating device 10 calculates a value of 0-40 as the first stress value in the same manner as the PSS score, which is the second data described above.
  • the stress estimating apparatus 10 combines the calculated first stress value and the second data based on the subjective data regarding the stress of the person U, which is obtained from the target person U in advance and stored, as a second data. 2 to calculate a second stress value (step S14). After that, the stress estimation device 10 outputs the stress information of the target person U, such as the calculated second stress value itself or an alert based on the second stress value (step S15).
  • the stress estimation device 10 repeats calculation of the second stress value in the same manner as described above until the stress estimation period of the target person U ends (No in step S16). That is, when the next stress estimation timing comes, the stress estimation device 10 acquires the first data based on the biological data from the person U (step S12), and generates the first stress calculation model from the first data. is used to calculate the first stress value (step S13), and the calculated first stress value and the second data based on subjective data on the stress of the person U obtained in advance are combined into a second stress calculation model to calculate the second stress value (step S14). In this way, the stress estimating device 10 continues to estimate the stress situation of the target person U.
  • “MAE (Mean Absolute Error)” in FIG. 5 represents the average absolute value of the difference between the estimated stress value and the correct value.
  • the value of the method of the present invention is evaluated to be smaller than that of the past method, and it can be seen that the accuracy is better.
  • the "correlation coefficient" in FIG. 5 represents the strength and direction of the relationship between the stress estimated value and the correct value.
  • the method of the present invention is evaluated to have a higher positive value than the past method, indicating that the accuracy is higher.
  • FIG. 6 and 7 are block diagrams showing the configuration of the stress estimating device according to Embodiment 2
  • FIG. 8 is a flowchart showing the operation of the stress estimating device.
  • an outline of the configuration of the stress estimation device and the stress estimation method described in the above-described embodiments is shown.
  • the stress estimating device 100 is configured by a general information processing device, and has, as an example, the following hardware configuration.
  • - CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • Program group 104 loaded into RAM 103
  • Storage device 105 for storing program group 104
  • a drive device 106 that reads and writes from/to a storage medium 110 external to the information processing device
  • Communication interface 107 connected to communication network 111 outside the information processing apparatus
  • Input/output interface 108 for inputting/outputting data
  • a bus 109 connecting each component
  • the stress estimation apparatus 100 can construct and equip the first calculation unit 121 and the 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 the storage device 105 or the ROM 102 in advance, for example, and is loaded into the RAM 103 and executed by the CPU 101 as necessary.
  • the program group 104 may be supplied to the CPU 101 via the communication network 111 or may be stored in the storage medium 110 in advance, and the drive device 106 may read the program and supply it to the CPU 101 .
  • the above-described first calculation unit 121 and second calculation unit 122 may be configured by a dedicated electronic circuit for realizing such means.
  • FIG. 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 case described above.
  • the information processing apparatus may be composed of part of the above-described configuration, such as not having the drive device 106 .
  • the stress estimation device 100 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.
  • the stress estimation device 100 calculating a first stress value by inputting first data based on biometric data measured from a target person to the 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 to the second stress calculation model (step S102), Execute the process of The second stress calculation model is generated by learning using the first stress value of a predetermined person obtained in the past and the second data, take the configuration.
  • the present invention is configured as described above to calculate a stress value (first stress value, second stress value) in two steps using biometric data of a person and subjective data relating to stress. Therefore, the stress of a person can be estimated with high accuracy.
  • 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 discs, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical discs), CD-ROMs (Read Only Memory), CD-Rs, CD-R/W, semiconductor memory (eg mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)).
  • the program may also be delivered to the computer on various types of transitory computer readable medium. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. Transitory computer-readable media can deliver the program to the computer via wired channels, such as wires and optical fibers, or wireless channels.
  • At least one or more of the functions of the first calculation unit 121 and the second calculation unit 122 described above may be executed by an information processing apparatus installed and connected to any location on the network. It may also run on cloud computing.
  • the stress estimation method according to Appendix 1 The stress estimation method according to Appendix 1, The second stress calculation model uses the first stress value calculated based on biological data measured from a predetermined person and the second data obtained in advance from the predetermined person as explanatory variables, It is generated by learning as an objective variable the third data related to stress acquired when biometric data is measured from a predetermined person, stress estimation method.
  • the third data is data based on subjective data on the stress of a predetermined person, stress estimation method.
  • Appendix 4 The stress estimation method according to any one of Appendices 1 to 3, Obtaining the second data from the target person in advance, Thereafter, calculating the first stress value by inputting the first data based on biometric data obtained from the target person to the first stress calculation model, calculating the second stress value by inputting the calculated first stress value and the second data obtained in advance into the second stress calculation model; stress estimation method.
  • Appendix 5 The stress estimation method according to Appendix 4, calculating the first stress value by inputting the first data based on the biometric data to the first stress calculation model each time biometric data is obtained from the target person; By inputting the calculated first stress value and the second data obtained in advance to the second stress calculation model each time the first stress value is calculated, the calculating a second stress value; stress estimation method.
  • Appendix 6 The stress estimation method according to Appendix 4 or 5, Biometric data is acquired from a target person at preset timing, and the first stress value is calculated by inputting the first data based on the acquired biometric data to the first stress calculation model.
  • (Appendix 8) a first calculation unit that calculates a first stress value by inputting first data based on biometric data measured from a target person to the first stress calculation model; 2) calculating a second stress value by inputting the first stress value and second data based on subjective data on stress obtained from a target person to a second stress calculation model; a calculation unit; with The second stress calculation model is generated by learning using the first stress value of a predetermined person obtained in the past and the second data, Stress estimator.
  • the stress estimation device uses the first stress value calculated based on biological data measured from a predetermined person and the second data obtained in advance from the predetermined person as explanatory variables, It is generated by learning as an objective variable the third data related to stress acquired when biometric data is measured from a predetermined person, Stress estimator.
  • the stress estimation device according to appendix 9, The third data is data based on subjective data on the stress of a predetermined person, Stress estimator.
  • Appendix 11 The stress estimation device according to any one of Appendices 8 to 10, An acquisition unit that acquires the second data from the target person in advance, The first calculation unit then calculates the first stress value by inputting the first data based on biometric data obtained from the target person to 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 obtained in advance to the second stress calculation model. to calculate Stress estimator.
  • Appendix 12 The stress estimation device according to Appendix 11, The first calculation unit calculates the first stress value by inputting the first data based on the biometric data to the first stress calculation model each time biometric data is obtained from the target person.
  • the second calculation unit converts the calculated first stress value and the previously obtained second data into the second stress calculation model each time the first stress value is calculated. calculating the second stress value by inputting to Stress estimator.
  • the stress estimation device according to appendix 11 or 12, The first calculation unit acquires biometric data from a target person at a preset timing, and inputs the first data based on the acquired biometric data to the first stress calculation model. Repeatedly calculating the first stress value, The second calculation unit transfers the calculated first stress value and the previously acquired second data to the second stress calculation model each time the first stress value is calculated. calculating the second stress value by inputting and outputting information based on the calculated second stress value; Stress estimator. (Appendix 14) 14.
  • the stress estimation device acquires the second data calculated based on the target person's answer to a preset question.
  • Stress estimator (Appendix 15) information processing equipment, calculating a first stress value by inputting first data based on biological data measured from a target person into the 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 a target person into a second stress calculation model; It is a program for executing processing, The second stress calculation model is generated by learning using the first stress value of a predetermined person obtained in the past and the second data,
  • a computer-readable storage medium storing a program characterized by:
  • stress estimation device 11 data acquisition unit 12 learning unit 13 first calculation unit 14 second calculation unit 15 output unit 16 person information storage unit 17 first model storage unit 18 second model storage unit 20 input device 30 display device 100 stress estimation Device 101 CPU 102 ROMs 103 RAM 104 program group 105 storage device 106 drive device 107 communication interface 108 input/output interface 109 bus 110 storage medium 111 communication network 121 first calculator 122 second calculator

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