WO2012105422A1 - 時差ぼけ症状の推定装置、推定システム、推定方法およびプログラム - Google Patents

時差ぼけ症状の推定装置、推定システム、推定方法およびプログラム Download PDF

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WO2012105422A1
WO2012105422A1 PCT/JP2012/051724 JP2012051724W WO2012105422A1 WO 2012105422 A1 WO2012105422 A1 WO 2012105422A1 JP 2012051724 W JP2012051724 W JP 2012051724W WO 2012105422 A1 WO2012105422 A1 WO 2012105422A1
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Prior art keywords
estimation
feature amount
jet lag
physiological index
symptom
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English (en)
French (fr)
Japanese (ja)
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あかね 佐野
直也 佐塚
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Sony Corp
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Sony Corp
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Priority to EP12742458.8A priority Critical patent/EP2671510A4/en
Priority to CN201280006539.7A priority patent/CN103338703B/zh
Priority to US13/982,076 priority patent/US20140114143A1/en
Publication of WO2012105422A1 publication Critical patent/WO2012105422A1/ja
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1118—Determining activity level
    • 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
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • 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
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • 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
    • A61B5/021—Measuring pressure in heart or blood vessels
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4857—Indicating the phase of biorhythm
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6814—Head
    • A61B5/6815—Ear
    • A61B5/6817—Ear canal
    • 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

Definitions

  • the present disclosure relates to an apparatus, an estimation system, an estimation method, and a program for estimating jet lag symptoms.
  • Jet lag in a broad sense in which the biological rhythm is shifted and the physical condition goes wrong due to movement with shift, shift working, irregular life, etc. (life habits that work days and holidays have significantly different sleep phases, irregular sleep awakening habits) It occurs. Jet lag causes symptoms such as sleep disorder and persistent feeling of fatigue. If you become unwell due to jet lag symptoms, your physical ability may decline or an unexpected accident may occur. For this reason, from the viewpoint of physical condition management, it is desired to objectively determine the presence or absence of jet lag or quantitatively evaluate the degree of jet lag symptoms.
  • jet lag symptoms include individual differences, age differences, and differences in moving directions in the east and west. For example, it is said that there is an individual difference of 1.7 days to 17.9 days in duration of jet lag symptoms (Reference 1). For this reason, objective judgment and quantitative evaluation are difficult, and these judgments and evaluations are only performed subjectively or qualitatively.
  • biological rhythms such as body temperature, blood pressure, heartbeat, and sleep awakening are known as biological rhythms of the circadian cycle.
  • the synchronization time of biological rhythm is the physiological index of the core body temperature system and the physiological index of the active mass (sleep awakening system) It is known that there is a large gap between them.
  • the biological rhythm shift caused by a time difference of 5 hours or more is about 2 weeks at core temperature and 2 to 3 days in heart rate and sleep awakening (References 2 and 3).
  • the symptoms of jet lag become the heaviest on the third and fourth days after the cause of jet lag occurs (Reference 4).
  • the present disclosure is to provide a jet lag symptom estimating apparatus, estimation system, estimation method and program capable of objectively determining the presence or absence of jet lag and quantitatively evaluating the degree of jet lag symptom. It is.
  • the feature amount of the physiological index of the deep body temperature system and the feature amount of the physiological index of the active amount are each Acquisition unit that classifies according to the degree and specifies the feature amount range according to the division of the degree of jet lag symptom and acquires the estimation reference set for the subject, and the feature amount and activity of the physiological index of the deep body temperature system
  • the feature amount of the subject's physiological index is included in the feature amount range of the estimation standard for each of the extraction unit that extracts the feature amount of the physiological index of the quantity system and the physiological index of the deep body temperature system and the physiological index of the activity system.
  • the estimation unit includes a feature amount of a physiological index of a deep body temperature system included in a feature amount range of a sample having a jet lag symptom, and a feature amount of a physiological index of an active mass is a feature amount of a sample having no jet lag symptom.
  • a feature amount of a physiological index of an active mass is a feature amount of a sample having no jet lag symptom.
  • the acquisition unit divides feature amounts of a plurality of samples into a feature amount range of a sample having jet lag symptoms and a feature amount range of a sample without jet lag symptoms, and a feature amount range of a sample having jet lag symptoms Based on the center of gravity of the subject, the feature quantities of a plurality of samples are divided into three or more according to the degree of jet lag symptom, and the estimation is performed by specifying the feature quantity subrange according to the degree of jet lag symptom
  • the criterion may be acquired, and the estimation unit may determine which feature amount subrange of the estimation criterion the feature amount of the subject's physiological index falls within, and estimate the subject's jet lag symptom.
  • the acquisition unit classifies the feature amounts of a plurality of samples into three or more based on subjective evaluations of the degree of jet lag symptoms by the samples, and specifies and sets the feature amount sub-range according to the degree of jet lag symptoms.
  • the estimation unit may acquire the estimation criterion, and the estimation unit may determine which feature amount subrange of the estimation criterion the feature amount of the physiological index of the subject is included in, and estimate the jet lag symptom of the subject.
  • the acquisition unit classifies the feature quantities of a plurality of samples including the subject according to the degree of jet lag symptom, and obtains the estimation criterion set by specifying the feature quantity range according to the division of the grade of jet lag symptom You may
  • the estimation apparatus is specified from an estimation history storage unit storing estimation results of jet lag symptoms in association with jet lag conditions indicating conditions of jet lag causing jet lag symptoms, and estimation results of jet lag symptoms stored.
  • the information processing apparatus may further include: an estimation history extraction unit that extracts an estimation result that matches the time difference condition.
  • the estimation device acquires the feature amount of the subject's physiological index and the classification result of the feature amount according to the jet lag symptom from the plurality of estimation devices, and specifies the feature amount range according to the division of the jet lag symptom.
  • the acquisition unit may be connected to the management device that sets the estimation criterion, and the acquisition unit may acquire the estimation criterion set by the management device.
  • the estimation device acquires the classification result of the feature amount of the subject's physiological index, the time difference condition indicating the condition of the time difference causing the jet lag symptom, and the estimation result of the jet lag symptom from the plurality of estimation devices.
  • the estimation apparatus is connected to a classification result and a time difference condition, and is connected to a management device that manages an estimation result of jet lag symptom, and the estimation device is configured to set a designated time difference condition and a feature amount among estimation results of jet lag symptom being managed.
  • the information processing apparatus may further include an estimation history acquisition unit that acquires an estimation result that matches the classification result.
  • the extraction unit extracts the feature quantity of the physiological index of the deep body temperature system and the feature quantity of the physiological index of the activity system for a plurality of samples having different degrees of jet lag symptoms, and the estimation apparatus calculates the physiology of the deep body temperature system. For each of the index and the activity-based physiological index, the feature quantity of the extracted physiological index is classified according to the degree of jet lag symptom, and the feature quantity range according to the division of the degree of jet lag symptom is identified and the estimation criterion
  • the acquisition unit may further acquire an estimation criterion set by the setting unit.
  • the estimation criterion may be set for each life style of the sample, and the estimation unit may estimate the jet lag symptom of the subject using the estimation criteria according to the life style of the subject.
  • the estimation apparatus may further include a notification unit that notifies the subject of the estimation result of the jet lag condition.
  • the physiological index of the deep body temperature system may be the tympanic temperature
  • the physiological index of the active mass system may be the active mass
  • the estimation apparatus may further include a storage unit that stores the estimation reference, and the acquisition unit may acquire the estimation reference from the storage unit.
  • an extraction unit that extracts a feature amount of a physiological index of a deep body temperature system and a feature amount of a physiological index of an activity amount for a plurality of samples having different degrees of jet lag symptoms; For each of the core body temperature physiological index and the activity mass physiological index, the feature quantity of the extracted physiological index is classified according to the degree of jet lag symptom, and the feature quantity range according to the division of jet lag symptom
  • a setting device including a setting unit that specifies and sets an estimation reference; an extraction unit that extracts a feature amount of a deep body temperature physiological index and a feature amount of an activity amount physiological index for a subject; For each of the physiological index and the activity-based physiological index, it is determined whether the feature amount of the subject's physiological index falls within which feature amount range of the estimation criterion, and the estimation unit estimates the subject's jet lag phenomenon Time difference with estimation device Only the symptoms of the estimated system is provided.
  • each of the feature amount of the physiological index of the deep body temperature system and the feature amount of the physiological index of the active amount is a jet lag symptom for a plurality of samples having different degrees of jet lag symptom.
  • obtain the estimation criteria set by specifying the feature amount range according to the division of the degree of jet lag symptom and obtain the feature amount of the physiological index of the deep body temperature system and the activity amount system for the subject.
  • the feature quantities of the physiological index of the subject are extracted, and for each of the physiological index of the deep body temperature system and the physiological index of the activity system, it is determined which feature quantity range of the estimation standard the feature quantity of the subject's physiological index falls within.
  • a method of estimating jet lag symptoms comprising: estimating jet lag symptoms of a subject.
  • each of the feature amount of the physiological index of the deep body temperature system and the feature amount of the physiological index of the activity system is classified according to the degree of jet lag symptom, and the division of the degree of jet lag symptom ,
  • An extraction unit for acquiring an estimation criterion specifying a feature amount range according to the feature an extraction unit for extracting a feature amount of a physiological index of a deep body temperature system and a feature amount of a physiological index of an active amount from a user, and For each of the physiological index and the activity-based physiological index, it is determined whether the feature amount of the user's physiological index falls within which feature amount range of the estimation criterion, and the estimation unit estimates the user's jet lag phenomenon
  • An apparatus for estimating jet lag symptoms is provided.
  • a program for causing a computer to execute the method for estimating a jet lag symptom may be provided using a computer readable recording medium, may be provided via communication means or the like.
  • an apparatus an estimation system, an estimation method, and a program for estimating jet lag symptoms that can objectively determine the presence or absence of jet lag and quantitatively evaluate the degree of jet lag symptoms. .
  • FIG. 1 is a diagram showing an overall configuration of an estimation system according to an embodiment of the present disclosure.
  • the estimation system estimates the jet lag symptom using the setting device 1 for setting the estimation reference EC used to estimate the jet lag symptom and the estimation reference EC, and generates an estimation result ER. It consists of two.
  • the estimation criterion EC is set based on a plurality of samples Sa having different jet lag symptoms, and is used to estimate a jet lag symptom of the subject Su.
  • the setting device 1 performs the setting process of the estimation reference EC
  • the estimation device 2 performs the estimation process of the jet lag condition.
  • the estimation criterion EC is set for each of the physiological index of the deep body temperature system and the physiological index of the activity (sleep awakening) system.
  • the setting apparatus 1 measures the physiological signal of the sample Sa for each of a plurality of samples Sa having different degrees of jet lag symptoms (symptoms) (steps S11 and S21), and derives a physiological index from the physiological signal (S12) , S22), and the feature quantity is extracted from the physiological index (S13, S23), and the degree of the symptom is acquired (S14, S24).
  • the core body temperature system index is an index in which the synchronization time of biological rhythm is longer than that of the activity type index. Examples of this include tympanic temperature, oral temperature, melatonin content in blood, and cortisol content.
  • the activity-based index is an index in which the synchronization time of the biological rhythm is shorter than that of the deep body temperature-based index. Examples of this include activity amount, pulse rate, AI (Augmentation Index) value, and blood pressure value.
  • the characteristic values of the physiological index include, for example, tympanic temperature, oral temperature, melatonin and cortisol content, pulse rate, AI value, blood pressure value, appearance time of specific phase (maximum amplitude, minimum, etc.), amplitude ( Maximum, minimum, average, etc.).
  • the amount of activity there are sleep onset time and sleep onset time.
  • the plurality of samples Sa may be a plurality of samplers Sa different from the subject Su whose symptom is to be estimated, and the same samplers Sa may be used as two or more samples Sa. Although the plurality of samples Sa may include the subject Su, a case where the plurality of samples Sa are included will be described here.
  • the setting apparatus 1 classifies the feature quantities of the physiological index of the plurality of samples Sa according to the degree of symptoms (S15, S25), and specifies the feature quantity range R according to the class of the degree of symptoms (S16, S26) Thus, the estimation reference EC is set (S17, S27).
  • the estimation criterion EC is set for each of the feature amount of the deep body temperature system index and the feature amount of the activity type index.
  • the feature amount of the physiological index is, for example, the feature amount of the “normal” sample Sa (the sample Sa having no symptom) and the “abnormal” sample Sa (symptoms) based on the degree of the symptom acquired from the sample Sa. It is divided into two stages with the feature value of a certain sample Sa).
  • the feature value of the physiological index may be divided into three or more stages, but in the following, the case of being divided into two stages will be described.
  • the feature quantity range Rn including many feature quantities of the “normal” sample Sa and the features of the “abnormal” sample Sa A feature amount range Ra including a large amount is identified.
  • an estimation reference EC including the feature amount range “normal” Rn and the feature amount range “abnormal” Ra is set.
  • the estimation device 2 measures a physiological signal of the subject Su (steps S31 and S41), derives a physiological index from the physiological signal (S32 and S42), and extracts a feature amount from the physiological index (S33 and S43).
  • the feature amount of the physiological index the feature amount of the core body temperature system index and the activity amount index which are the same as the feature amount extracted at the setting process are extracted.
  • the estimation device 2 determines which feature amount range R of the estimation criterion EC the feature amount of the physiological index of the subject Su is included (S34, S44), and estimates the symptom of the subject Su based on the determination result of the feature amount (S51). Here, it is determined which feature amount range R the feature amount of the physiological index is included in each of the feature amount of the deep body temperature system index and the feature amount of the activity amount index.
  • the feature amount of the deep body temperature system index is included in the feature amount range “abnormal” Ra
  • the feature amount of the activity amount index is included in the feature amount range “normal” Rn.
  • FIG. 4 is a block diagram showing the functional configuration of the setting device 1.
  • the setting apparatus 1 includes a signal measurement unit 11, an index derivation unit 12, a feature extraction unit 13, a sample information acquisition unit 14, a reference setting unit 15, and a storage unit 16.
  • processing is performed on each of the deep body temperature system index and the activity scale index.
  • the signal measuring unit 11 measures physiological signals of the sample Sa using various sensors. For example, the tympanic temperature and oral temperature are measured using a temperature sensor, the pulse rate and blood pressure values are measured using a pressure sensor, and the amount of activity is measured using an acceleration sensor.
  • the physiological signal is measured, for example, at a predetermined measurement interval and a predetermined duration over a measurement period such as one night. The measured physiological signal is converted to physiological data.
  • the index deriving unit 12 derives a physiological index from physiological data.
  • Physiological data may be filtered to remove specific frequency components, or may be output from an acceleration sensor to discard non-rest data.
  • Physiological data is converted to another physiological index (for example, converted from pulse wave to AI value) as needed, and derived as an average value during a predetermined duration (for example, several tens of seconds to several minutes) of the physiological index Ru.
  • the feature amount extraction unit 13 extracts a feature amount from the derived physiological index.
  • the feature values of tympanic temperature, oral temperature, pulse rate, AI value, and blood pressure value determine a function of the circadian cycle that matches the time series data of the derived physiological index, and use the determined function to determine the specific phase ( It is extracted by obtaining the appearance time of the amplitude maximum, minimum, etc.) or the amplitude amount (maximum, minimum, average, etc.).
  • the feature value of the activity is extracted by specifying the boundary between the activity state and the sleep state from the time series data of the activity which has been derived using, for example, the Cole-Kripke algorithm (reference 6) or the like. .
  • the sample information acquisition unit 14 acquires sample information indicating the degree of symptoms, life style (morning type, night type, intermediate type, etc.) from the sample Sa. Specimen information is acquired through input devices such as keys, buttons, and a touch panel.
  • the reference setting unit 15 sets an estimation reference EC used to estimate a symptom from the extracted feature amount.
  • the estimation criterion EC is set by dividing the feature quantities of the physiological index according to the degree of symptoms and specifying the feature quantity range R according to the degree of symptoms for a plurality of samples Sa.
  • the feature amount range R is specified using, for example, a machine learning algorithm such as a support vector machine or boosting.
  • the estimation criterion EC may be set, for example, as an estimation criterion EC for each of a morning type, a night type, and an intermediate type subject based on sample information indicating a life form.
  • the storage unit 16 stores physiological signals of the sample Sa, time-series data of physiological indexes, feature amounts, sample information, estimation reference EC, and the like.
  • the estimation criterion EC may be shared with the estimation device 2 or may be provided to the estimation device 2 through a communication line or a recording medium.
  • FIG. 5 is a block diagram showing a functional configuration of the estimation device 2.
  • the estimation device 2 includes a signal measurement unit 21, an index derivation unit 22, a feature extraction unit 23, a subject information acquisition unit 24, a symptom estimation unit 25, a symptom notification unit 26, a storage unit 27, and a time difference condition.
  • An acquisition unit 28, a history storage unit 29, and a history extraction unit 30 are included.
  • the signal measuring unit 21, the index deriving unit 22, and the feature extracting unit 23 perform processing on each of the deep body temperature system index and the activity amount index.
  • the signal measurement unit 21, the index derivation unit 22, and the feature quantity extraction unit 23 function in the same manner as the corresponding components of the setting device 1.
  • the feature amount extraction unit 23 extracts a feature amount from the physiological index derived for the subject Su.
  • the subject information acquisition unit 24 acquires subject information indicating a life form and the like from the subject Su.
  • the symptom estimation unit 25 determines which feature amount range R of the estimation criterion EC the feature amount of the physiological index of the subject Su is included. Then, the symptom estimation unit 25 estimates the symptoms of the subject Su based on the determination results of the respective feature amounts of the deep body temperature system index and the activity scale index. The determination of the feature amount may be performed using the estimation reference EC corresponding to the life style of the subject Su obtained as the subject information.
  • the symptom notification unit 26 notifies the user of a symptom estimation result ER, a symptom estimation history to be described later, and the like.
  • the notification of symptoms is notified as visual information and / or auditory information.
  • the storage unit 27 stores the estimation reference EC, the measurement value of the physiological index of the subject Su, time series data of the derived value, the feature amount, and the like.
  • the storage unit 27 stores an estimation reference EC shared with the setting device 1 or an estimation reference EC acquired from the setting device 1 through a communication line or a recording medium.
  • the time difference condition acquisition unit 28 acquires a time difference condition indicating the time difference caused by the cause of the jet lag (movement, shift working, etc.), and the number of days elapsed from the generation of the cause of the jet lag.
  • the time difference condition may be acquired from the subject Su, or may be acquired through a GPS device, an acceleration sensor or the like.
  • the history storage unit 29 stores time series data of derived values of physiological index, feature amount, determination result of feature amount, estimation result of symptom ER etc. in relation to the time difference condition acquired from the subject Su as a symptom estimation history Be done.
  • FIG. 5 shows that the information is stored through the storage unit 27, the information may be stored without through the storage unit 27.
  • the history extraction unit 30 extracts the estimation result ER satisfying the prediction condition from the estimation history of the symptoms based on the prediction condition (time difference condition) specified by the subject Su and supplies the extraction result ER to the symptom notification unit 26.
  • Each component of setting device 1 and estimation device 2 is configured as hardware such as circuit logic and / or software such as a program.
  • the components configured as software are realized by executing a program on a CPU (not shown).
  • the estimation system may be configured by integrating the setting device 1 and the estimation device 2.
  • the estimation system or estimation device 2 may be configured as, for example, a portable music player, a portable telephone, a portable information terminal or the like.
  • a sensor such as a temperature sensor or an acceleration sensor may be mounted on the earphone in order to measure a physiological index.
  • the estimation result ER of the symptom may be notified to the subject Su via an output device such as a display device such as a liquid crystal display or a speaker instead of the estimation device 2.
  • the estimation system may be configured separately from the setting device 1 and the estimation device 2.
  • the estimation reference EC used for estimation of the symptom or the estimation result ER of the symptom may be transmitted and received through the communication line or the recording medium.
  • the estimation device 2 may be configured to output the symptom estimation result ER to another user terminal or the like.
  • the feature amount of the physiological index is the feature amount of the “normal” sample Sa and the feature amount of the “abnormal” sample Sa, using the tympanic membrane temperature and the activity amount as the deep body temperature system index and the activity level index The case where it divides into two steps of and is demonstrated.
  • FIG. 6 is a flow chart showing the operation of the setting device 1.
  • the setting apparatus 1 measures a physiological signal of the sample Sa for each of a plurality of samples Sa having different degrees of symptoms, derives a physiological index from the physiological signal, and extracts a feature quantity from the physiological index. Acquire subject information that indicates the degree of symptoms. Below, the operation
  • the signal measuring unit 11 measures the tympanic temperature and the activity of the sample Sa at a predetermined measurement interval and a predetermined duration over a predetermined measurement time (step S61).
  • measured values of the tympanic membrane temperature and the amount of activity are stored as time-series data.
  • the measurement time is, for example, one night
  • the measurement interval is, for example, several minutes to several tens of minutes
  • the duration is, for example, several tens of seconds to several minutes.
  • the measurement is performed in a state where the sample Sa is at rest while avoiding one to two hours after a meal.
  • the tympanic temperature and the activity may be measured at the same time, or may be measured at different times.
  • the index deriving unit 12 uses the time series data of the measured values to derive the average value of the tympanic temperature during the continuous time and the average value of the amount of activity as a physiological index (S62).
  • the storage unit 16 stores derived values of the tympanic membrane temperature and the amount of activity as time-series data.
  • the time-series data of the measured values is rejected as abnormal values such as discontinuous data and data at non-rest.
  • the time-series data of the measurement value is subjected to data conversion such as converting the pulse wave into an AI value (corresponding to an inflection point of a second derivative of the pulse wave) according to a physiological index.
  • the feature quantity extraction unit 13 extracts the feature quantity of the tympanic temperature and the feature quantity of the activity as the feature quantity of the physiological index, using the time-series data of the derived values (S63).
  • the storage unit 16 stores the feature values of the tympanic membrane temperature and the activity amount in association with the sample Sa.
  • FIG. 7 is a diagram showing an example of the feature value of the tympanic membrane temperature and the feature value of the activity.
  • the feature value of the tympanic temperature is obtained as a combination of the appearance time (phase) of the minimum amplitude and its amplitude amount, for example, by obtaining a function of the circadian period that matches the time series data of derived values.
  • Ru The feature amount may be extracted as, for example, a combination of two or more values of an appearance time of a specific phase (amplitude maximum, minimum, etc.) or an amplitude amount (maximum, minimum, average, etc.).
  • the feature quantity of the amount of activity is extracted from the time-series data of the derived values by specifying the boundary between the activity state and the sleep state as the time of sleep onset based on the distribution state of the amount of activity.
  • the sample information acquisition unit 14 acquires sample information indicating the degree of symptoms from the sample person Sa using a questionnaire for the sample person Sa (S64).
  • the storage unit 16 stores sample information in association with the sample Sa.
  • sample information indicating a life form may be acquired from the sample person Sa.
  • the life pattern of the sample Sa is designated as any of morning type, night type, intermediate type, and the like by using a questionnaire for the sample Sa.
  • FIG. 8 is a diagram showing an example of a questionnaire for the sampler Sa.
  • sample information is acquired using a questionnaire including items of sleeplessness, sleepiness, work efficiency, motivation, appetite, feeling of fatigue, and activity.
  • the sampler Sa performs five-point evaluation for each item, with five good states and one bad state.
  • the normal sleep onset time of the sampler Sa is acquired as sample information.
  • the setting device 1 classifies the feature quantities of the physiological index of the plurality of samples Sa according to the degree of the symptom, and specifies the feature quantity range R according to the class of the degree of the symptom to estimate the estimation criterion EC.
  • the reference setting unit 15 uses the feature amounts of the physiological indexes of the plurality of samples Sa according to the degree of the symptoms based on the sample information indicating the degree of the symptoms, the feature amounts of the “normal” sample Sa and the “abnormal” sample Sa. (S65)
  • the storage unit 16 stores the classification result of the feature amount in association with the sample Sa.
  • the total score of 16 points or more is divided into the feature amount of the “normal” sample Sa, and the total score of 15 points or less is “abnormal” It divides into the feature-value of sample Sa.
  • the feature amount of the sample Sa in which the difference between the normal sleep onset time (sample information) and the actual sleep onset time (feature amount of activity) is “normal” within a predetermined time (for example, 1 hour) And the outside of the predetermined time is divided into the feature quantities of the "abnormal” sample Sa.
  • the reference setting unit 15 sets the estimation reference EC by specifying the feature amount range “normal” Rn and the feature amount range “abnormal” Ra based on the classification result of the feature amount (S66) (S67). Note that the estimation criterion EC according to life style is set using the feature amount space for each life style. In the storage unit 16, the specification result of the feature amount range R is stored as an estimation reference EC.
  • FIG. 9 is a diagram showing an example of specifying the feature amount range R for the feature amount of the tympanic membrane temperature and the feature amount of the activity.
  • FIG. 9 shows an example of the feature space including the distribution of the feature, the shape of the feature range R, and the like.
  • a feature amount space (plane) in which an appearance time of a specific phase is a first dimension and an amplitude amount is a second dimension is The estimation criterion EC is determined using this.
  • the estimation criterion EC is obtained using a feature amount space (line) having the sleep onset time as a first dimension.
  • the estimation reference EC is obtained using the feature amount space of the first, second,..., N-th dimension.
  • FIG. 9 shows a feature amount space (plane, line) in which the feature amounts of the “normal” sample Sa are plotted as circle marks and the feature amounts of the “abnormal” sample Sa are plotted as crosses .
  • a boundary B between a range Rn including many feature amounts of “normal” sample Sa and a range Ra including many feature amounts of “abnormal” sample Sa is identified. Ru.
  • an estimation reference EC including the feature amount range “normal” Rn and the feature amount range “abnormal” Ra is set.
  • FIG. 10 is a flow chart showing the operation of the estimation device 2.
  • the estimation device 2 measures a physiological signal of the subject Su, derives a physiological index from the physiological signal, and extracts a feature amount from the physiological index.
  • the signal measuring unit 21 measures the tympanic temperature and the activity of the subject Su at a predetermined measurement interval and a predetermined duration over a predetermined measurement time (step S71).
  • the storage unit 27 stores measurement values of the tympanic membrane temperature and the amount of activity as time-series data.
  • the index deriving unit 22 derives the average value of the tympanic membrane temperature and the average value of the amount of activity as the physiological index using the time series data of the measured values (S72).
  • the storage unit 27 stores derived values of the tympanic membrane temperature and the amount of activity as time-series data.
  • the feature quantity extraction unit 23 uses the time-series data of derived values to identify the feature quantity of tympanic membrane temperature (for example, the appearance time of the minimum amplitude and its amplitude value) and the feature quantity of the activity quantity (sleep onset time) It extracts as a quantity (S73).
  • the storage unit 27 stores feature amounts of tympanic membrane temperature and activity.
  • the subject information acquisition unit 24 may acquire sample information indicating a life form from the sample Sa.
  • the living form is designated as any of morning type, night type, intermediate type and the like using a questionnaire for the subject Su.
  • the estimation device 2 determines which feature amount range R of the estimation criterion EC the feature amount of the physiological index of the subject Su is included in, and estimates the symptom of the subject Su based on the determination result of the feature amount.
  • the symptom estimation unit 25 reads out from the storage unit 27 the estimation reference EC of the feature amount of the tympanic temperature and the estimation reference EC of the feature amount of the activity.
  • the symptom estimating unit 25 determines which of the feature amount range “normal” Rn and the feature amount range “abnormal” Ra the feature amount of the subject Su is included for each of the tympanic temperature and the activity amount (S 74).
  • FIG. 11 is a diagram showing an example of feature value determination for the feature value of tympanic temperature and the feature value of activity.
  • the feature amount of the subject A is included in the feature amount range “normal” Rn of the estimation reference EC of the tympanic temperature
  • the feature amounts of the subjects B and C are included in the feature amount range “abnormal” Ra
  • the feature amounts of the subjects A and B are included in the feature amount range “normal” Rn of the estimation reference EC of the activity amount
  • the feature amounts of the subject C are included in the feature amount range “abnormal” Ra. Ru.
  • the symptom estimation unit 25 estimates the degree of symptoms of the subject Su using, for example, Table 1 based on the determination results of the eardrum temperature and the feature amount of the activity amount (S75).
  • the feature of the tympanic temperature is determined to be "abnormal” and the activity of the feature is determined to be “normal”.
  • Severe symptoms are estimated.
  • the feature amount of the tympanic temperature is determined to be "abnormal” and the feature amount of the activity is determined to be “abnormal”
  • the moderate symptom is estimated, and the feature amount of the tympanic temperature is determined as "normal” If it is determined that the feature quantity of the activity amount is "normal”, it is estimated that mild symptoms or no symptoms are present. This enables an objective assessment of the presence or absence of a symptom, as well as a quantitative assessment of the degree of the symptom.
  • the symptom notification unit 26 notifies the subject Su of the estimation result ER of the symptom (S76).
  • the estimation result ER of the condition includes at least information indicating the degree of the condition, such as "severe”, “moderate”, “mild”, and may further include the determination result of the feature value of the tympanic membrane temperature and the activity.
  • the estimation result ER of the condition may include time-series data of the measurement values and derived values of the tympanic membrane temperature and the amount of activity used for estimation of the condition, a feature amount, and the like.
  • the symptom estimation result ER may be displayed in comparison with an estimation history of a specific date and time, an average estimation history, and the like.
  • FIG. 12 is a diagram showing an example of notification of a symptom estimation result ER.
  • the feature amount of tympanic temperature is determined to be "abnormal”
  • the feature amount of activity is determined to be "normal”
  • the degree of symptoms is estimated to be "severe”.
  • the feature amount of the core body temperature system index is included in the feature amount range “abnormal” Ra
  • the feature amount of the activity amount index is included in the feature amount range “normal” Rn.
  • the estimation reference EC is set by dividing the feature quantity of the physiological index into two levels of the feature quantity of the “normal” sample Sa and the feature quantity of the “abnormal” sample Sa.
  • the estimation criterion EC may be set by further dividing the feature value of the physiological index into three or more stages. As a result, the symptoms of the subject Su can be estimated in more detail than in the case of division into two stages. Below, the case where the feature-value of a physiological index is divided into four steps based on sample information or statistical processing is explained.
  • the sample information acquisition unit 14 acquires sample information indicating the degree of symptoms from each sample Sa using the above-described questionnaire.
  • the reference setting unit 15 classifies feature quantities of physiological indexes of a plurality of samples Sa based on sample information.
  • the reference setting unit 15 sets the estimation reference EC using the feature space for each physiological index based on the classification result of the feature. That is, the feature amount ranges Rn1, Rn2, Ra1, and Ra2 of “normal degree large”, “normal degree small”, “abnormal degree small”, and “abnormal degree” are specified using a machine learning algorithm.
  • FIG. 13 is a diagram showing an example of an estimation reference EC of the feature value (time, amplitude) of the eardrum temperature based on sample information. Note that FIG. 13 illustrates an example of the feature amount space including the shape of the feature amount range R and the like.
  • the sample information acquisition unit 14 acquires sample information indicating the degree of symptoms from each sample Sa using the above-described questionnaire, as in the case based on the sample information.
  • the reference setting unit 15 divides the feature quantities of the physiological index of the plurality of samples Sa into two stages, that is, “normal” and “abnormal” based on the sample information.
  • the reference setting unit 15 sets the estimation reference EC using the feature space for each physiological index based on the classification result of the feature. First, using a machine learning algorithm, the range Rn of the feature amount of the “normal” sample Sa (the range Rn of the “normal” and the range Ra of the feature amount of the “abnormal” sample Sa (the range Ra of the “abnormal”) And the boundary B with the Next, the center of gravity G of the “normal” range Rn is determined.
  • FIG. 14 shows an example of the estimation reference EC of the feature value (time, amplitude) of tympanic membrane temperature based on statistical processing.
  • FIG. 14 shows an example of the feature amount space including the shape of the feature amount range R and the like.
  • the degree of symptoms of the subject Su is estimated based on, for example, Table 2 from the determination result of the feature value for each physiological index.
  • the degree of symptoms indicates that the smaller the value, the more severe the symptoms. This enables quantitative evaluation to be performed in more detail by classifying the degree of symptoms in more detail.
  • the degree of symptoms of the subject Su is estimated based on, for example, the following table from the determination result of the feature amount for each physiological index.
  • Table 3 is an example in the case of setting the four-step estimation reference EC to the deep body temperature system index and setting the two-step estimation reference EC to the activity-based index.
  • Table 4 is an example in which a two-stage estimation reference EC is set in the deep body temperature system index, and a four-stage estimation reference EC is set in the activity scale.
  • the time difference condition acquisition unit 28 acquires a time difference condition that indicates the time difference that has occurred due to the cause of the jet lag, and the number of days elapsed since the cause of the jet lag has occurred.
  • the time difference caused by jet lag is the movement time difference, shift time, etc.
  • the number of days elapsed after the cause of jet lag is the elapsed days after movement with shift, shift working, etc. is there.
  • the estimation device 2 as described above, the measurement of the physiological signal of the subject Su, the derivation of the physiological index, and the extraction of the characteristic quantity are performed, the characteristic quantity of the physiological index is determined, and the symptom is estimated.
  • the history storage unit 29 associates time series data of derived values of physiological indexes, feature quantities, determination results of feature quantities, estimation results of symptoms ER, etc. in relation to a subject Su and a time difference condition as estimation history of symptoms. It is memorized. As a result, the estimated history of symptoms is accumulated in the history storage unit 29 in a state in which reference can be made based on the subject Su and the time difference condition.
  • the jet lag condition acquiring unit 28 acquires, from the subject Su, a jet lag condition causing a jet lag, and a jet lag condition specifying the number of days elapsed from the cause of jet lag as a symptom prediction condition.
  • FIG. 15 is a diagram showing an example of a questionnaire for acquiring a condition prediction condition (time difference condition) of a symptom.
  • FIG. 16 is a diagram showing an example of the prediction result of symptoms.
  • severe symptoms are predicted three days after moving with a 5-hour time lag.
  • the subject Su can predict symptoms according to the number of days elapsed from the movement before or after moving with a time difference.
  • a modification of the estimation system relates to a method of setting the estimation criterion EC and a method of using the estimation history.
  • the estimation criterion EC is set by dividing the feature amount of the physiological index into two steps of the feature amount of the “normal” sample Sa and the feature amount of the “abnormal” sample Sa.
  • the estimation reference EC may be set in accordance with the other setting example 1 or setting example 2 of the estimation standard described above.
  • FIG. 17 is a diagram showing an example of an estimation system capable of setting the estimation reference EC in consideration of the feature quantities of a plurality of subjects Su.
  • the estimation device 2a acquires information indicating the degree of the symptom of the subject Sua in the same manner as in the setting process of the estimation reference EC, and uses, for example, the feature amount of the physiological index of the examiner Sua. It divides into the feature value of "normal” or "abnormal” sample Sa. Then, the estimation device 2a transmits the classification result of the feature amount and the feature amount to the management device 3 as feature amount information.
  • the management device 3 manages the feature amount information of the subject Sua on the database together with the feature amount information of another subject Su or the sampler Sa.
  • the estimation device 2b when estimating the condition of the subject Sub, requests the management device 3 to transmit the estimation criterion EC.
  • the management device 3 In response to the transmission request for the estimation criterion EC, the management device 3 newly sets the estimation criterion EC based on the managed feature amount information, and transmits the estimation criterion EC to the estimation device 2b.
  • the estimation criterion EC is set by plotting the feature amount on the feature amount space according to the classification result of the feature amount and specifying the feature amount range R, as in the case of the estimation process.
  • the estimation device 2b extracts the feature amount of the physiological index of the subject Sub, and estimates the symptoms of the subject Sub using the latest estimation criterion EC.
  • estimation reference EC using the feature amount information of a plurality of subjects Su, estimation can be made that the symptom of the subject Su can be estimated without using the estimation reference EC stored in advance in the estimation device 2 A system is realized.
  • FIG. 18 is a diagram showing an example of an estimation system capable of setting the estimation reference EC in consideration of the feature amount of the subject Su.
  • the estimation device 2 acquires information indicating the degree of the symptom of the subject Su and classifies the feature amount of the physiological index of the subject Su as in the process of setting the estimation reference EC. . Then, the estimation device 2 newly sets the estimation reference EC in consideration of the feature amount information acquired in the previous estimation process, as well as the feature amount information of the other samplers Sa. Note that the estimation reference EC may be set using only the feature amount information of the subject Su. In another estimation process, the estimation device 2 extracts the feature amount of the physiological index of the subject Su that is the same as the estimation process described above, and estimates the symptom of the subject Su using the latest estimation criterion EC.
  • FIG. 19 is a diagram showing an example of an estimation system capable of sharing the estimation history of symptoms among a plurality of estimation devices 2a and 2b.
  • the estimation device 2 acquires information indicating the degree of the symptom of the subject Sua in the estimation process of the condition of the subject Sua, as in the case of the process of setting the estimation reference EC, and the feature value of the physiological index of the subject Sua Section Then, the estimation device 2a transmits the classification result of the feature amount and the feature amount to the management device 3 as feature amount information.
  • the estimation device 2a estimates the symptoms of the subject Sua, and transmits the estimation result ER to the management device 3 together with the time difference condition acquired from the subject Sua.
  • the management device 3 manages the information of the subject Sua together with the information of the other subjects Su.
  • the management device 3 manages estimation results ER of a plurality of subjects Su on a database in association with feature amount information and a time difference condition.
  • the estimation device 2b transmits the time difference condition and the feature amount information of the subject Sub to the management device 3 together with the transmission request of the estimation history in the prediction processing of the symptom of the subject Sub.
  • the feature amount information (the classification result of the feature amount and the feature amount) of the subject Sub is assumed to be obtained in advance in the symptom estimation process for the subject Sub.
  • the management device 3 extracts the estimation history stored in association with the time difference condition and the feature amount information similar to the time difference condition of the subject Sub and the feature amount information, in response to the transmission request of the estimation history. Then, the management device 3 transmits the extracted estimation history to the estimation device 2b, and the estimation device 2b notifies the user of the estimation history acquired by the estimation history acquisition unit (not shown).
  • an estimation system capable of appropriately predicting the symptoms of the subject Su is realized by sharing the estimation history of the symptoms with another subject Su having similar feature amount information.
  • Reference 1 O'Connor, P .; J. Morgan, W .; P. Athletic Performance Following Rapid Traversal of Multiple Time Zones-A Review. Sports Med. 1990, 10, 20-30.
  • Reference 2 Klein, K. et al. E. Wegmann, H .; -M. The Resynchronization of Human Circadian Rhythms After Transmitteridian Flight as a Result of Flight Direction and Mode of Activity. In Chronobiology; Scheving, L. E. , Halberg, F .; , Pauly, J. E. , Eds. Thieme Publ. Stuttgart, 1974; 564-570.
  • Reference 3 Winget, C. et al. M. De Roshia, C .; M.

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