WO2021161469A1 - ストレス分析装置、ストレス分析方法、及びコンピュータ読み取り可能な記録媒体 - Google Patents

ストレス分析装置、ストレス分析方法、及びコンピュータ読み取り可能な記録媒体 Download PDF

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WO2021161469A1
WO2021161469A1 PCT/JP2020/005638 JP2020005638W WO2021161469A1 WO 2021161469 A1 WO2021161469 A1 WO 2021161469A1 JP 2020005638 W JP2020005638 W JP 2020005638W WO 2021161469 A1 WO2021161469 A1 WO 2021161469A1
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Prior art keywords
stress
user
information
factors
activity
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English (en)
French (fr)
Japanese (ja)
Inventor
祐 北出
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NEC Corp
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NEC Corp
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Priority to PCT/JP2020/005638 priority Critical patent/WO2021161469A1/ja
Priority to JP2021577807A priority patent/JP7459885B2/ja
Priority to US17/797,617 priority patent/US20230056194A1/en
Publication of WO2021161469A1 publication Critical patent/WO2021161469A1/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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • 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
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • 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
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate

Definitions

  • the present invention relates to a stress analyzer and a stress analysis method for analyzing stress applied to a person, and further relates to a computer-readable recording medium on which a program for executing these is recorded.
  • Patent Document 1 discloses a device for evaluating the state of autonomic nervous function of a subject. The device disclosed in Patent Document 1 first acquires the biological information and schedule of the subject, then calculates an index indicating the stress state from the biological information, and obtains the calculated index and the event included in the schedule. Record in association with each other.
  • the index indicating the stress state of the subject and the event experienced by the subject are recorded in association with each other. Therefore, by checking the recorded contents, the event in which the subject feels stress is identified.
  • Patent Document 1 only records a past event in association with an index at that time.
  • An example of an object of the present invention is to provide a stress analyzer, a stress analysis method, and a computer-readable recording medium that can solve the above problems and predict the stress state of a user in the future.
  • the stress analyzer in one aspect of the present invention is A stress factor extraction unit that extracts factors that increase stress in the user from stress information that associates the user's past stress status with information on the user's past activity.
  • a stress prediction unit that predicts an increase in stress in the user based on the activity schedule of the user and the extracted factors. Is equipped with It is characterized by that.
  • a factor extraction step that extracts factors that increase stress in the user from stress information that associates the user's past stress status with information on the user's past activity.
  • a stress increase prediction step that predicts an increase in stress in the user based on the activity schedule of the user and the extracted factors. Have, It is characterized by that.
  • the computer-readable recording medium in one aspect of the present invention is used.
  • a factor extraction step that extracts factors that increase stress in the user from stress information that associates the user's past stress status with information on the user's past activity.
  • a stress increase prediction step that predicts an increase in stress in the user based on the activity schedule of the user and the extracted factors. Recording a program, including instructions to execute, It is characterized by that.
  • FIG. 1 is a block diagram schematically showing a configuration of a stress analyzer according to an embodiment of the present invention.
  • FIG. 2 is a block diagram specifically showing the configuration of the stress analyzer according to the embodiment of the present invention.
  • FIG. 3 is a flow chart showing the operation of the stress analyzer according to the embodiment of the present invention when constructing a stress model.
  • FIG. 4 is a flow chart showing the operation of the stress analyzer according to the embodiment of the present invention when extracting stress factors and predicting stress.
  • FIG. 5 is a flow chart showing an operation at the time of creating advice of the stress analyzer according to the embodiment of the present invention.
  • FIG. 6 is a block diagram showing an example of a computer that realizes the stress analyzer according to the embodiment of the present invention.
  • FIG. 1 is a block diagram schematically showing a configuration of a stress analyzer according to an embodiment of the present invention.
  • the stress analyzer 10 in the embodiment shown in FIG. 1 is an apparatus that analyzes the factors of stress of the user and predicts the increase in stress in the future user. As shown in FIG. 1, the stress analyzer 10 includes a stress factor extraction unit 11 and a stress prediction unit 12.
  • the stress factor extraction unit 11 extracts factors that increase stress in the user from the stress information that associates the past stress state of the user with the information of the past activity of the user.
  • the stress prediction unit 12 predicts an increase in stress in the user based on the schedule of the user's activity and the extracted factors.
  • the stress analyzer 10 in the embodiment can extract the factors that cause stress for the user from the past information, and by using this, the stress state of the user in the future can be predicted.
  • FIG. 2 is a block diagram specifically showing the configuration of the stress analyzer according to the embodiment of the present invention.
  • the stress analyzer 10 includes a stress level estimation unit 13, a stress model generation unit 14, and a stress model in addition to the stress factor extraction unit 11 and the stress prediction unit 12 described above. It includes a storage unit 15, an activity information storage unit 16, a stress reduction unit 17, and an output unit 18. Further, the stress analyzer 10 is connected to the sensor device 20 that acquires the biometric information of the user and the terminal device 30 of the user by wired communication or wireless communication.
  • the stress analyzer 10 can also be constructed inside the terminal device 30 by installing the program according to the embodiment described later on the computer of the terminal device 30 and executing the program.
  • Examples of the terminal device 30 include smartphones, tablet terminals, notebook PCs (Personal Computers), and the like.
  • the sensor device 20 is a device provided with a sensor capable of detecting biological information, and outputs the detected biological information.
  • Examples of the sensor device 20 include a heart rate monitor that detects the heart rate, a skin electric potential meter that detects the amount of electrical activity on the skin, a sweat meter that measures the amount of sweating, and an accelerometer that detects the acceleration of human movement. Further, the sensor device 20 may be a camera that captures a user's face image.
  • the stress level estimation unit 13 acquires the biometric information output from the sensor device 20, and estimates the stress level in the user from the acquired biometric information. Specifically, for example, when the sensor device 20 is an accelerometer, the stress level estimation unit 13 acquires an acceleration indicating the movement of the user's body as biological information. Then, the stress level estimation unit 13 estimates the stress level from the acquired acceleration according to the method disclosed in Reference 1 or 2 described later.
  • the stress level estimation unit 13 acquires the user's heart rate as biometric information.
  • the stress level estimation unit 13 acquires the amount of electrical activity of the user's skin as biometric information.
  • the stress level estimation unit 13 acquires the amount of perspiration as biometric information.
  • the stress level estimation unit 13 estimates the stress level according to the type of acquired biological information by using the corresponding method, and the stress level information for specifying the estimated stress level is generated by the stress model generation unit 14. Output to.
  • an existing method or a method to be developed in the future can be used as a method for estimating the acceleration, the heart rate, the amount of electrical activity on the skin, the amount of sweating, and the stress level from the facial image.
  • the stress level estimation unit 13 adds the information of the time when the biological information is output from the sensor device 20 to the estimated stress level, and estimates the time-series change of the stress level.
  • the sensor device 20 may be a device that includes a plurality of types of sensors and outputs a plurality of types of biological information correspondingly.
  • the stress level estimation unit 13 can acquire a plurality of types of biometric information and estimate the stress level using the plurality of types of biometric information.
  • the stress model generation unit 14 first receives stress level information from the stress level estimation unit 13. Then, the stress model generation unit 14 specifies the activity information corresponding to the stress level estimation from the activity information storage unit 16.
  • the activity information storage unit 16 stores activity information (activity information) for the user.
  • the activity information includes information on meetings in which the user participates (attendees, purpose, meeting time, etc.), information on business trips in charge of the user (accompaniment, purpose, place, departure time, etc.), and information on tasks performed by the user.
  • Information business content, etc. can be mentioned.
  • Activities variable X i is composed of one or more values, if the case of a binary ⁇ 0,1 ⁇ , it may become continuous value.
  • the activity variable X i is the number of attendees in the meeting X 1 , a binary flag (0 or 1) X 2 indicating whether or not a particular individual has attended, and a particular job title. greater number of attendees X 3, consists of the time X 4 etc. of the meeting. If the time during which the activity was performed, such as the time of the meeting, is used as the activity variable, the value becomes a continuous value. At this time, the value may be normalized.
  • the stress model generation unit 14, notation and stress level Y estimated by the stress level estimating unit 13, and a activity variable X i is set from the activity information, as learning data, the learning model (hereinafter "Stress Model" To build).
  • the constructed learning model is stored in the stress model storage unit 15.
  • the variable Y in addition to the stress level estimated by the stress level estimation unit 13, the result of the questionnaire answered by the user, the result of the user's self-judgment, or the like may be used.
  • the stress model generating unit 14, the learning objective variable stress levels Y stress level estimating unit 13 estimates, as an explanatory variable activity variable X i, by performing a multiple regression analysis, the weighting factor a i do.
  • the stress model shown in Equation 1 below is constructed. Further, the constructed stress model is stored in the stress model storage unit 15.
  • the stress factor extraction unit 11 uses the stress model stored in the stress model storage unit 15 as stress information to extract factors that increase stress in the user. That is, the stress factor extraction unit 11 extracts an activity variable having a high correlation with stress as a factor for increasing stress in the user from the stress model stored in the stress model storage unit 15.
  • stress factor extraction unit 11 for each activity, the value of the weighting factor a i can identify the high activity variable X i, the identified activity variable X i, extracts as a factor of increasing the stress in a user .. Further, the stress factor extraction unit 11, the extracted activity variable X i, and outputs the stress estimation unit 12.
  • Stress prediction unit 12 in the embodiment, the activity variables X i that has been outputted from the stress factor extraction unit 11, and a future activity information stored in the activity information storage unit 16, an increase of stress in the user Predict.
  • the stress prediction unit 12 first sets activity variables for each future activity using future activity information. Then, the stress prediction unit 12 compares the output activity variable X i with the set activity variable, and identifies the future activity including the output activity variable X i.
  • the stress prediction unit 12 applies the activity variable set for the specified future activity to the stress model stored in the stress model storage unit 15 to calculate the stress level Y. Then, when the calculated stress level Y exceeds the threshold value, the stress prediction unit 12 predicts an increase in the stress of the user.
  • the stress prediction unit 12 calculates the difference between the calculated stress level Y and the current stress level. Then, the stress level estimation unit 13 predicts an increase in the stress of the user even when the calculated stress level Y is larger than the current stress level and the difference is equal to or larger than the threshold value.
  • the stress level estimation unit 13 can also predict the time when the stress of the user rises. ..
  • the output unit 18 transmits the prediction result by the stress prediction unit 12 to, for example, the user's terminal device 30.
  • the terminal device 30 receives the prediction result
  • the terminal device 30 displays the received prediction result on the screen. This allows the user to know the potential for increased stress.
  • the destination of the prediction result by the output unit 18 is not limited to the user's terminal device 30, and the destination may also include a stress manager's terminal device, a data management device, and the like.
  • the stress reduction unit 17 extracts factors that reduce stress in the user from stress information, and creates advice for reducing stress in the user based on the extracted factors. Further, the output unit 18 notifies the user of the advice created by the stress reduction unit 17.
  • the stress reduction unit 17 has a low correlation with stress as a factor for reducing stress in the user from the stress model stored in the stress model storage unit 15. Extract activity variables.
  • the stress reduction unit 17 identifies an activity variable having a low weight coefficient ai value for each activity, and extracts the specified activity variable as a factor for reducing stress in the user.
  • the stress reduction unit 17 sets activity variables for each future activity using future activity information. Then, the stress reduction unit 17 compares the activity variable extracted as the stress reducing factor with the set activity variable, and identifies the future activity including the activity variable extracted as the stress reducing factor.
  • the stress reduction unit 17 applies the activity variable set for the specified future activity to the stress model stored in the stress model storage unit 15 to calculate the stress level Y. Then, the stress prediction unit 12 determines that the stress of the user is reduced when the calculated stress level Y is equal to or less than the threshold value.
  • the stress reduction unit 17 calculates the difference between the calculated stress level Y and the current stress level. Then, the stress reduction unit 17 determines that the stress of the user is reduced even when the calculated stress level Y is smaller than the current stress level and the difference is equal to or larger than the threshold value.
  • the stress reduction unit 17 can also predict the time when the stress of the user decreases.
  • the stress reduction unit 17 determines that the stress of the user is reduced
  • the future activity specified above is used as advice for stress reduction to the user via the output unit 18 to the user's terminal device.
  • Send to 30 the terminal device 30 receives the advice
  • the terminal device 30 displays the received advice on the screen. This allows the user to know the activity that reduces their stress.
  • FIGS. 1 and 2 will be referred to as appropriate.
  • the stress analysis method is carried out by operating the stress analyzer 10. Therefore, the description of the stress analysis method in the embodiment will be replaced with the following description of the operation of the stress analysis device.
  • FIG. 3 is a flow chart showing the operation of the stress analyzer according to the embodiment of the present invention when constructing a stress model.
  • the stress level estimation unit 13 determines the stress level in the user from the biometric information output from the sensor device 20 during the set period or until the user gives an instruction. Estimate (step A1). Further, the stress level estimation unit 13 outputs the estimated stress level Y to the stress model generation unit 14 each time it is estimated.
  • the stress model generation unit 14 accumulates the output stress level Y, and when the accumulated stress level Y period reaches a constant value, the activity information storage unit 16 responds to the estimation of the stress level Y. Identify the activity information (step A2).
  • the stress model generating unit 14 uses the activity information specified in step A2, for each activity, in accordance with configuration rules, set the activity variable X i (step A3).
  • the stress model generation unit 14 constructs a stress model using the stress level Y estimated in step A1 and the corresponding activity variable X i set in step A3 as training data (step A4).
  • the constructed stress model is stored in the stress model storage unit 15.
  • FIG. 4 is a flow chart showing the operation of the stress analyzer according to the embodiment of the present invention when extracting stress factors and predicting stress.
  • the stress factor extraction unit 11 has a high correlation with stress as a factor for increasing stress in the user from the stress model stored in the stress model storage unit 15. Extract the activity variable (step B1).
  • the stress prediction unit 12 identifies future activities including the latter from the activity variables set from the future activity information and the activity variables extracted in step B1 (step B2).
  • the stress prediction unit 12 applies the activity specified in step B2 to the stress model constructed in step A4 shown in FIG. 3, calculates the stress level, and uses the calculated stress level to use the user. Predict an increase in stress (step B3).
  • the output unit 18 transmits the prediction result in step B3 to the user's terminal device 30 (step B4).
  • the terminal device 30 receives the prediction result by executing step B3, the terminal device 30 displays the received prediction result on the screen. This allows the user to know the potential for increased stress.
  • FIG. 5 is a flow chart showing an operation at the time of creating advice of the stress analyzer according to the embodiment of the present invention.
  • the stress reduction unit 17 extracts an activity variable having a low correlation with stress as a factor for reducing stress in the user from the stress model stored in the stress model storage unit 15 ( Step C1).
  • the stress reduction unit 17 identifies future activities including the latter from the activity variables set from the future activity information and the activity variables extracted in step C1 (step C2).
  • the stress reduction unit 17 applies the activity specified in step C2 to the stress model constructed in step A4 shown in FIG. 3, calculates the stress level, and uses the calculated stress level. Then, the user's stress reduction is determined (step C3).
  • the stress reduction unit 17 uses the activity determined in step C3 to reduce the stress of the user as advice for stress reduction to the user (step C4).
  • the output unit 18 transmits the advice created in step C4 to the user's terminal device 30 (step C5).
  • the terminal device 30 displays the received advice on the screen. This allows the user to know the activity that reduces their stress.
  • the stress analyzer 10 can construct a stress model, and by using this stress model, factors that cause stress for the user can be extracted. Therefore, the stress analyzer 10 can predict the stress state of the user in the future, and can also create advice for stress reduction.
  • the program in the embodiment may be any program that causes a computer to execute steps B1 to B4 shown in FIG. Then, by installing this program on a computer and executing it, the stress analyzer 10 and the stress analysis method according to the embodiment can be realized.
  • the computer processor functions as the stress factor extraction unit 11 and the stress prediction unit 12 to perform processing.
  • the program in the embodiment may be a program that causes a computer to execute steps A1 to A4 shown in FIG. 3 and further steps C1 to C5 shown in FIG.
  • the computer processor functions not only as the stress factor extraction unit 11 and the stress prediction unit 12, but also as the stress level estimation unit 13, the stress model generation unit 14, and the stress reduction unit 17, and performs processing.
  • the stress model storage unit 15 and the activity information storage unit 16 can be realized by storing the data files constituting them in a storage device such as a hard disk provided in the computer.
  • the program in the embodiment may be executed by a computer system constructed by a plurality of computers.
  • each computer functions and processes as a stress factor extraction unit 11 or a stress prediction unit 12, a stress level estimation unit 13, a stress model generation unit 14, or a stress reduction unit 17, respectively.
  • the stress model storage unit 15 and the activity information storage unit 16 may be built on a computer different from the computer that executes the program in the embodiment.
  • FIG. 6 is a block diagram showing an example of a computer that realizes the stress analyzer according to the embodiment of the present invention.
  • the computer 110 includes a CPU (Central Processing Unit) 111, a main memory 112, a storage device 113, an input interface 114, a display controller 115, a data reader / writer 116, and a communication interface 117. And. Each of these parts is connected to each other via a bus 121 so as to be capable of data communication. Further, the computer 110 may include a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array) in addition to the CPU 111 or in place of the CPU 111.
  • a GPU Graphics Processing Unit
  • FPGA Field-Programmable Gate Array
  • the CPU 111 expands the programs (codes) of the present embodiment stored in the storage device 113 into the main memory 112 and executes them in a predetermined order to perform various operations.
  • the main memory 112 is typically a volatile storage device such as a DRAM (Dynamic Random Access Memory).
  • the program according to the present embodiment is provided in a state of being stored in a computer-readable recording medium 120.
  • the program in the present embodiment may be distributed on the Internet connected via the communication interface 117.
  • the storage device 113 include a semiconductor storage device such as a flash memory in addition to a hard disk drive.
  • the input interface 114 mediates data transmission between the CPU 111 and an input device 118 such as a keyboard and mouse.
  • the display controller 115 is connected to the display device 119 and controls the display on the display device 119.
  • the data reader / writer 116 mediates the data transmission between the CPU 111 and the recording medium 120, reads the program from the recording medium 120, and writes the processing result in the computer 110 to the recording medium 120.
  • the communication interface 117 mediates data transmission between the CPU 111 and another computer.
  • the recording medium 120 include a general-purpose semiconductor storage device such as CF (CompactFlash (registered trademark)) and SD (SecureDigital), a magnetic recording medium such as a flexible disk, or a CD-.
  • CF CompactFlash (registered trademark)
  • SD Secure Digital
  • magnetic recording medium such as a flexible disk
  • CD- CompactDiskReadOnlyMemory
  • optical recording media such as ROM (CompactDiskReadOnlyMemory).
  • the stress analyzer 10 in the present embodiment can also be realized by using hardware corresponding to each part instead of the computer on which the program is installed. Further, the stress analyzer 10 may be partially realized by a program and the rest may be realized by hardware.
  • a stress factor extraction unit that extracts factors that increase stress in the user from stress information that associates the user's past stress status with information on the user's past activity.
  • a stress prediction unit that predicts an increase in stress in the user based on the activity schedule of the user and the extracted factors. Is equipped with A stress analyzer characterized by this.
  • Appendix 2 The stress analyzer according to Appendix 1, From the stress information, factors that reduce stress in the user are extracted, advice for reducing stress in the user is created based on the extracted factors, and the created advice is notified to the user for stress reduction. It has more parts, A stress analyzer characterized by this.
  • the stress analyzer according to Appendix 1 or 2.
  • the stress factor extraction unit uses a learning model constructed by learning the relationship between the user's past stress state and the user's past activity information as the stress information, and stresses the user. Extract factors that increase A stress analyzer characterized by this.
  • Appendix 4 The stress analyzer according to Appendix 3, The stress prediction unit predicts an increase in stress in the user by applying the activity schedule of the user and the extracted factors to the learning model. A stress analyzer characterized by this.
  • a factor extraction step that extracts factors that increase stress in the user from stress information that associates the user's past stress status with information on the user's past activity.
  • a stress increase prediction step that predicts an increase in stress in the user based on the activity schedule of the user and the extracted factors. Have, A stress analysis method characterized by this.
  • Appendix 6 The stress analysis method described in Appendix 5 An advice notification that extracts a factor that lowers stress in the user from the stress information, creates advice for reducing stress in the user based on the extracted factor, and notifies the user of the created advice. Have more steps, A stress analysis method characterized by this.
  • Appendix 7 The stress analysis method according to Appendix 5 or 6.
  • a learning model constructed by learning the relationship between the user's past stress state and the user's past activity information is used as the stress information to apply stress to the user. Extract factors that increase A stress analysis method characterized by this.
  • Appendix 8 The stress analysis method described in Appendix 7 In the stress increase prediction step, the stress increase in the user is predicted by applying the activity schedule of the user and the extracted factors to the learning model. A stress analysis method characterized by this.
  • a factor extraction step that extracts factors that increase stress in the user from stress information that associates the user's past stress status with information on the user's past activity.
  • a stress increase prediction step that predicts an increase in stress in the user based on the activity schedule of the user and the extracted factors. Recording a program, including instructions to execute A computer-readable recording medium characterized by that.
  • Appendix 10 The computer-readable recording medium according to Appendix 9, which is a computer-readable recording medium.
  • the program is on the computer Address notification that extracts factors that reduce stress in the user from the stress information, creates advice for reducing stress in the user based on the extracted factors, and notifies the user of the created advice. Perform steps, include more instructions, A computer-readable recording medium characterized by that.
  • Appendix 11 A computer-readable recording medium according to Appendix 9 or 10.
  • a learning model constructed by learning the relationship between the user's past stress state and the user's past activity information is used as the stress information to apply stress to the user. Extract factors that increase A computer-readable recording medium characterized by that.
  • Appendix 12 The computer-readable recording medium according to Appendix 11, which is a computer-readable recording medium.
  • the stress increase in the user is predicted by applying the activity schedule of the user and the extracted factors to the learning model.
  • the present invention it is possible to predict the stress state of the user in the future.
  • the present invention is useful for systems that manage human stress.
  • Stress analyzer 11 Stress factor extraction unit 12 Stress prediction unit 13 Stress level estimation unit 14 Stress model generation unit 15 Stress model storage unit 16 Activity information storage unit 17 Stress reduction unit 18 Output unit 20 Sensor device 30 Terminal device 110 Computer 111 CPU 112 Main memory 113 Storage device 114 Input interface 115 Display controller 116 Data reader / writer 117 Communication interface 118 Input device 119 Display device 120 Recording medium 121 Bus

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PCT/JP2020/005638 2020-02-13 2020-02-13 ストレス分析装置、ストレス分析方法、及びコンピュータ読み取り可能な記録媒体 Ceased WO2021161469A1 (ja)

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JP2021577807A JP7459885B2 (ja) 2020-02-13 2020-02-13 ストレス分析装置、ストレス分析方法、及びプログラム
US17/797,617 US20230056194A1 (en) 2020-02-13 2020-02-13 Stress analysis apparatus, stress analysis method, and computer-readable recording medium

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JP2016209404A (ja) * 2015-05-12 2016-12-15 アルプス電気株式会社 ストレス検知システム
JP2019030389A (ja) * 2017-08-04 2019-02-28 パナソニックＩｐマネジメント株式会社 自律神経状態評価装置、自律神経状態評価システム、自律神経状態評価方法及びプログラム
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JP2016209404A (ja) * 2015-05-12 2016-12-15 アルプス電気株式会社 ストレス検知システム
JP2019030389A (ja) * 2017-08-04 2019-02-28 パナソニックＩｐマネジメント株式会社 自律神経状態評価装置、自律神経状態評価システム、自律神経状態評価方法及びプログラム
JP2019067151A (ja) * 2017-09-29 2019-04-25 コージーベース株式会社 ストレス軽減プラン提案システム、ストレス軽減プラン提案方法、およびプログラム

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