+

WO2018105459A1 - Dispositif d'estimation de somnolence - Google Patents

Dispositif d'estimation de somnolence Download PDF

Info

Publication number
WO2018105459A1
WO2018105459A1 PCT/JP2017/042776 JP2017042776W WO2018105459A1 WO 2018105459 A1 WO2018105459 A1 WO 2018105459A1 JP 2017042776 W JP2017042776 W JP 2017042776W WO 2018105459 A1 WO2018105459 A1 WO 2018105459A1
Authority
WO
WIPO (PCT)
Prior art keywords
heart rate
sleepiness
drowsiness
user
cumulative average
Prior art date
Application number
PCT/JP2017/042776
Other languages
English (en)
Japanese (ja)
Inventor
広行 鹿又
安士　光男
Original Assignee
パイオニア株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by パイオニア株式会社 filed Critical パイオニア株式会社
Priority to JP2018554948A priority Critical patent/JP6692924B2/ja
Publication of WO2018105459A1 publication Critical patent/WO2018105459A1/fr

Links

Images

Classifications

    • 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
    • A61B5/0245—Measuring pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • 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
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators

Definitions

  • the present invention relates to a drowsiness estimation apparatus that estimates the drowsiness of a user.
  • an average value of RRI (RR Interval) values of heartbeats detected by a heartbeat sensor at awakening and a predetermined multiple of an integral value of RRI values exceeding the average value are set as threshold values, and an RRI value exceeding the average value is integrated.
  • the threshold value it is described that it is determined that the patient is dozing.
  • Patent Document 2 obtains heartbeat interval data obtained by time-series RRI based on a heartbeat waveform of a subject (user) in a resting state measured by a sensor. Next, frequency analysis is performed on the heartbeat interval data, and a frequency analysis result of heartbeat fluctuation including power spectral density (PSD) and autonomic nerve total power (TP) with respect to a frequency at a certain time is obtained. Next, the frequency analysis result was set as the initial state, and based on the estimated drowsiness position and the estimated awakening position included in the initial state, the origin of the drowsiness position and the origin of the awakening position were set. Determine the drowsiness scale. And it is described that the sleepiness of the subject is determined based on the sleepiness scale.
  • PSD power spectral density
  • TP autonomic nerve total power
  • the method described in Patent Document 2 has the following problems.
  • the correspondence between heart rate information and sleepiness scale varies depending on various factors. Even based on a resting state, there are changes due to the day and changes due to time zones. For example, it is known that the time when sleepiness becomes strong is from 3 to 4 in the early morning, and from 3 to 4 in the afternoon. Therefore, it is difficult to estimate sleepiness with high accuracy only from heartbeat information.
  • driving is a kind of stress load state, and the heart rate baseline is different even in a road state (urban area or highway).
  • the state at the beginning of measurement is not constant, it has not been possible to cope with changes such as sleepiness from the beginning and sleepiness at the beginning.
  • an object of the present invention is to provide a sleepiness estimation device that can estimate sleepiness with high accuracy, for example.
  • the invention according to claim 1 is characterized in that an acquisition means for acquiring a user's heart rate and a total of the heart rate acquired by the acquisition means after the activity state of the user has changed.
  • a cumulative average heart rate calculating means for calculating a cumulative average heart rate divided by time, and a drowsiness for calculating a drowsiness reference heart rate as a reference whether or not the user feels drowsy based on the cumulative average heart rate
  • a sleepiness estimation apparatus comprising: a reference heart rate calculation means; and sleepiness estimation means for estimating the user's current sleepiness based on the sleepiness reference heart rate and the current heart rate.
  • the invention according to claim 7 is a drowsiness estimation method of a drowsiness estimation device that estimates a user's current drowsiness, an acquisition step of acquiring a user's heart rate, and an activity state of the user is changed.
  • a cumulative average heart rate calculation step of calculating a cumulative average heart rate obtained by dividing a cumulative total of the heart rate acquired by the acquisition step by time, and whether or not the user feels sleepy based on the cumulative average heart rate
  • the invention described in claim 8 is a sleepiness estimation program characterized by causing a computer to execute the sleepiness estimation method according to claim 7.
  • the cumulative average heart rate calculation unit calculates a cumulative average heart rate obtained by dividing the total of the heart rate acquired by the acquisition unit after the change in the user activity state by time.
  • the drowsiness reference heart rate calculation means calculates a drowsiness reference heart rate that serves as a reference for whether or not the user feels drowsiness based on the cumulative average heart rate.
  • the sleepiness estimation means estimates the user's current sleepiness based on the sleepiness reference heart rate and the current heart rate. By doing so, drowsiness can be estimated based on the accumulated heart rate, so the drowsiness reference heart rate can be calculated in a short time, and drowsiness can be estimated with higher accuracy.
  • the cumulative average heart rate calculating means may calculate the cumulative average heart rate after a predetermined time elapses after the activity state changes. By doing so, the influence of other activity states before the change can be reduced, and the calculation accuracy of the sleepiness reference heart rate can be improved.
  • the apparatus further comprises a minimum heart rate determination unit that determines a minimum heart rate that is a minimum value of the heart rate per predetermined time acquired by the acquisition unit, and the drowsiness reference heart rate calculation unit includes the cumulative average heart rate and the minimum heart rate. May be calculated as the drowsiness reference heart rate.
  • the drowsiness reference heart rate calculating means may calculate the cumulative average heart rate as the drowsiness reference heart rate. By doing so, it is possible to further simplify the mechanism for calculating the drowsiness reference heart rate, and to reduce the processing time and the development cost in the apparatus or the like. In addition, by setting the cumulative average heart rate as the drowsiness reference heart rate, it is possible to capture a sign of feeling drowsiness, and to take a measure such as prompting a break before recognizing drowsiness.
  • the cumulative average heart rate calculating means may calculate the cumulative average heart rate after the user has changed to an active state of driving the mobile object. In this way, when driving a moving body such as a vehicle, the drowsiness reference heart rate can be calculated in a short time, and drowsiness can be estimated with high accuracy.
  • a state monitoring apparatus includes the drowsiness estimation apparatus described above and a measurement unit that measures a user's heart rate. By doing in this way, when monitoring a user's state, a user's sleepiness can be estimated with high precision. Therefore, the monitoring accuracy can be improved.
  • the sleepiness estimation method includes a cumulative average heart rate obtained by dividing a cumulative total of heart rate acquired in the acquisition step by time after a change in the user's activity state in the cumulative average heart rate calculation step. Based on the accumulated average heart rate in the drowsiness reference heart rate calculation step, a drowsiness reference heart rate that is a reference for whether or not the user feels drowsiness is calculated.
  • the sleepiness estimation step the user's current sleepiness is estimated based on the sleepiness reference heart rate and the current heart rate. By doing so, drowsiness can be estimated based on the accumulated heart rate, so the drowsiness reference heart rate can be calculated in a short time, and drowsiness can be estimated with higher accuracy.
  • a sleepiness estimation program that causes a computer to execute the above-described sleepiness estimation method may be used. In this way, since sleepiness can be estimated based on the accumulated heart rate using a computer, the sleepiness reference heart rate can be calculated in a short time, and sleepiness can be estimated with higher accuracy.
  • the sleepiness estimation apparatus 1 includes a state determination unit 2, a parameter table 3, a sleepiness reference heart rate calculation unit 4, a sleepiness calculation unit 5, a sleepiness display unit 6, an I / F 7, an I / F F8.
  • the sleepiness estimation apparatus 1 is connected to a heart rate sensor 11.
  • the drowsiness estimation apparatus 1 having the configuration shown in FIG. 1 may be configured as an application program (app) such as a mobile device such as a smartphone or a personal computer, or installed in an in-vehicle device such as a car navigation system or a home appliance. May be.
  • the sleepiness estimation apparatus 1 may include the heart rate sensor 11.
  • the sleepiness estimation device 1 including the heart rate sensor 11 as a measurement unit is a state monitoring device 100 that monitors the state of a user (subject), and for example, by mounting the state monitoring device 100 on a vehicle, a driver monitor Can be operated as Of course, the present invention is not limited to the driver monitor, and other activity states such as desk work, study, or work in a factory can be monitored.
  • the heart rate sensor 11 any known sensor can be used as long as it is a sensor that can acquire at least a heart rate as biological information related to the heart rate.
  • various forms such as a wristwatch type, a type embedded in a vehicle seat, a type provided in an indoor chair or bed, and the like can be used.
  • the heart rate sensor 11 is not limited to one type, and a plurality of types may be used depending on an activity state described later.
  • the state determination unit 2, the sleepiness calculation unit 5, and the sleepiness reference heart rate calculation unit 4 function as an arithmetic device such as a CPU (Central Processing Unit).
  • the parameter table 3 functions as a storage medium such as a hard disk or a flash memory (the storage medium may be volatile so that it is loaded each time, but is preferably non-volatile).
  • the sleepiness display unit 6 functions as a display device such as a liquid crystal display.
  • the I / Fs 7 and 8 function as a communication control unit that communicates with the heart rate sensor 11 and the like.
  • the state discriminating unit 2 discriminates the user's activity state such as rest, driving, working, and sleep based on the user position information and user acceleration information input from the outside via the I / F 8. That is, the state determination unit 2 detects the activity state of the user. That is, the state determination unit 2 can detect a change in the activity state of the user.
  • the position information for example, latitude and longitude information obtained by GPS (Global Positioning System) is input.
  • GPS Global Positioning System
  • the acceleration information information on an acceleration sensor included in the portable device, the vehicle itself, or the vehicle-mounted device is input. Further, information may be acquired from a device that can incorporate an acceleration sensor such as an activity sensor (activity meter) or the like and can detect a human activity state.
  • FIG. 2 shows an example of determining the activity state in the state determination unit 2.
  • FIG. 2 is an example of a table for determining the activity state.
  • the content of the state sta1 indicates a resting state
  • the content of the state sta2 indicates driving
  • the content of the state sta3 indicates that the operation is in progress
  • the content of the state sta4 indicates the active state of sleep.
  • the activity state may include items other than the illustrated items such as exercise and heavy labor.
  • the state is determined to be sta1 (rest).
  • the speed may be calculated from a change in position information. Or if it is a vehicle, you may make it acquire from a speed sensor.
  • the device indicates a type of the heart rate sensor 11 or a place where the heart rate sensor 11 is installed. The device may be acquired from the heart rate sensor 11 or may be separately set by a user or the like.
  • the acceleration information is medium, the speed is large, and the device is the driver's seat, it is determined as the state sta2 (during driving).
  • the position information is the office, the acceleration information is large, the speed is medium, and the device is a chair, the state is determined as sta3 (working).
  • the position information is the bed, the acceleration information is small, the speed is small, and the device is the bed, it is determined as the state sta4 (sleep).
  • the acceleration information and the speed are examples, and specific numerical values can be arbitrarily changed on the setting as appropriate.
  • the determination is made based on the position information, the acceleration information, the speed information, and the device information. However, the determination may be made using only one item, or only two items or three items. Alternatively, the determination may be made based on the heart rate measured by the heart rate sensor 11. For example, when the change in heart rate is small, it is possible to make a rough determination such as resting or sleeping, and when the heart rate is low but there is fluctuation, driving or working.
  • the parameter table 3 includes a table in which sleepiness prediction parameters described later are set for each activity state. Based on the determination result of the state determination unit 2, the sleepiness prediction parameter corresponding to the activity state is output to the sleepiness calculation unit 5. An example of the table is shown in FIG.
  • two types of parameters d and a are set for the sleepiness prediction parameter.
  • the sleepiness prediction parameter a in the case of the state sta1 (rest), the sleepiness prediction parameter a is 15 and the sleepiness prediction parameter b is 0.2.
  • the state sta2-1 (during driving: manual operation)
  • the sleepiness prediction parameter a is 20
  • the drowsiness prediction parameter b is 0,
  • the state sta2-2 (during driving: automatic operation)
  • the drowsiness prediction parameter a is 20 and the drowsiness prediction parameter b is 0.2.
  • the sleepiness prediction parameter a In the state sta3 (working), the sleepiness prediction parameter a is 15 and the sleepiness prediction parameter b is 0.2.
  • the sleepiness prediction parameter a is 10 and the sleepiness prediction parameter b is 0. 2.
  • values in the range of 0 to 100 are set for the sleepiness prediction parameters a and b.
  • the sleepiness prediction parameters are divided into manual operation (sta2-1) and automatic operation (sta2-2) when the state is driving (sta2). This is because, as will be described later in detail, it is necessary to change the setting value of the sleepiness prediction parameter between manual operation and automatic operation.
  • the drowsiness reference heart rate calculation unit 4 calculates a drowsiness reference heart rate using a calculation formula described later.
  • the sleepiness reference heart rate is a heart rate when sleepiness occurs. That is, it is a heart rate at which a person feels sleepy below this heart rate. That is, it is a heart rate used as a reference for the user to feel sleepy.
  • Examples of the method for calculating the drowsiness reference heart rate include a method for calculating based on the average heart rate and a method for calculating based on the minimum heart rate.
  • the drowsiness reference heart rate may be separately calculated with the configuration shown in FIG. 4 and transferred to the drowsiness estimation device 1.
  • the drowsiness reference heart rate calculation unit 21 calculates the drowsiness reference heart rate using a calculation formula described later.
  • the sleepiness reference heart rate which is the heart rate when sleepiness occurs, is between the lowest heart rate and the average heart rate.
  • the cumulative average heart rate that is a time-average value of the cumulative number of heart rates acquired by the I / F 7 from the heart rate sensor 11 and the minimum value of the heart rate that the I / F 7 acquires from the heart rate sensor 11.
  • the cumulative average heart rate is preferably initialized when the activity state changes. For example, when taking a break while driving a vehicle or the like, the accumulated average heart rate is once reset at the time of the break and the accumulation is started. At this time, the minimum heart rate value is also reset.
  • the sleepiness reference heart rate calculation unit 4 calculates a cumulative average heart rate by dividing a cumulative total heart rate acquired by the I / F 7 (acquisition unit) by time after the user's activity state has changed. It functions as a number calculation means and a drowsiness reference heart rate calculation means for calculating a drowsiness reference heart rate that is a reference for whether or not the user feels drowsiness based on the cumulative average heart rate. Also, it functions as a minimum heart rate determination unit that determines the minimum heart rate that is the minimum value of the heart rate per predetermined time among the heart rates acquired by the I / F 7 (acquisition unit). Also, by functioning as these means, the sleepiness reference heart rate calculation unit 4 executes the cumulative average heart rate calculation step and the sleepiness reference heart rate calculation step.
  • the present inventors conducted an experiment to investigate the relationship between the user's average heart rate and minimum heart rate and actual sleepiness.
  • automatic type passenger cars were being driven and passengers were in the active state, and 11 healthy subjects (men aged 30 to 59) were targeted.
  • the subject wore a heart rate meter (MyBeat manufactured by Union Tool Co., Ltd.) and recorded the high frequency fluctuation component (HF) corresponding to the heart rate and respiratory fluctuation.
  • HF high frequency fluctuation component
  • a subjective value of the sleepiness intensity felt by the subject was recorded using VAS (Visual Analog Scale).
  • FIGS. 5 to 7 show a comparison between the VAS results of three subjects and the sleepiness Sp estimated from the sleepiness reference heart rate calculated by the above-described equations (1) and (2).
  • the upper part of FIGS. 5 to 7 is the result of VAS, and the lower part is the sleepiness Sp estimated from the sleepiness reference heart rate calculated by the above-described equations (1) and (2).
  • the solid line indicates the case where the drowsiness reference heart rate is calculated by the equation (1)
  • the alternate long and short dash line indicates the case where the drowsiness reference heart rate is calculated by the equation (2).
  • the drowsiness Sp is expressed by the following equation (3) when the drowsiness prediction parameters are a and b, the heart rate HR per minute currently measured, the drowsiness reference heart rate HR_ref, the high frequency fluctuation component HF, and the reference high frequency fluctuation component HF_ref. Calculated.
  • Sp a ⁇ max (HR_ref ⁇ HR, 0) + b ⁇ max (HF ⁇ HF_ref, 0) (3)
  • the function max is a function whose return value is the maximum value among arguments separated by commas in parentheses.
  • HR ⁇ HR_ref the calculation result of HR_ref ⁇ HR is HF>
  • HF_ref the calculation result of HF-HF_ref becomes a return value.
  • the reference high frequency fluctuation component HF_ref is set in the parameter table 3 for each time and activity state. Details of the high frequency fluctuation component HF and the reference high frequency fluctuation component HF_ref will be described later.
  • the formula (2) is preferably applied in the case of an active state in which the relationship between sleepiness and danger is high, such as driving a vehicle.
  • the user or the like may arbitrarily select whether to consider the sign, and the formula (1) or (2) may be switched.
  • the method for calculating the drowsiness reference heart rate described above can be applied not only during driving but also in other activity states.
  • the sleepiness reference heart rate is calculated by obtaining the cumulative average heart rate and the minimum heart rate in real time from the acquired heart rate and the change in the activity state, so the sleepiness reference heart rate is preset. There is no need. Therefore, for example, by mounting the sleepiness estimation device 1 in a rental car or the like, it is possible to accurately estimate sleepiness for an unspecified number of users according to each user.
  • the drowsiness reference heart rate obtained by the above-described equations (1) and (2) may be set as a history in a table or the like for each activity state and used as an initial value or the like.
  • the sleepiness calculator 5 is based on the heart rate measured by the heart rate sensor 11, the sleepiness prediction parameter output from the parameter table 3, and the sleepiness reference heart rate output from the sleepiness reference heart rate calculator 4.
  • the user's sleepiness is estimated by calculating (information on sleepiness).
  • the sleepiness calculated by the sleepiness calculation unit 5 is information obtained from the difference between the current heart rate and the sleepiness reference heart rate when the current heart rate is lower than the sleepiness reference heart rate.
  • the drowsiness value is calculated so as to increase in accordance with the rate of decrease from the drowsiness reference heart rate.
  • sleepiness is related to circadian rhythm of about 24 hours, circasemidian rhythm of about 12 hours, and ultradian rhythm of about 2 hours. Yes.
  • the nighttime sleepiness is consistent with a decrease in body temperature rhythm and reflects the circadian rhythm of body temperature. Afternoon sleepiness occurs about half a day after the nighttime minimum body temperature appears, and is considered to reflect circadian rhythm.
  • HF component high frequency variability component
  • LF component low frequency corresponding to Mayer wave
  • the HF component reflecting the respiratory fluctuation appears in the heartbeat fluctuation only when the parasympathetic nerve is in tension (activated).
  • the LF component also appears in heart rate variability when the sympathetic nerve is tense and when the parasympathetic nerve is tense.
  • the drowsiness calculator 5 calculates the drowsiness S by the following equation (4).
  • S Ki ⁇ Si + Kp ⁇ Sp + Kd ⁇ Sd (4)
  • Sp physiological sleepiness
  • Si an integral element of physiological sleepiness Sp
  • Sd is a differential element of physiological sleepiness Sp.
  • Ki, Kp, and Kd are coefficients for weighting each element.
  • Physiological sleepiness Sp is calculated by the above-described equation (3).
  • the sleepiness calculation unit 5 that calculates the physiological sleepiness Sp calculates a sleepiness value indicating the user's sleepiness based on at least the difference between the user's heart rate and the sleepiness reference heart rate.
  • the setting of the sleepiness prediction parameters a and b will be described.
  • drowsiness during driving tends to occur during monotonous driving (such as an expressway), resulting in functional deterioration accompanied by drowsiness.
  • Such functional degradation appears while the heart rate, blood pressure, etc. are calm in terms of physiological functions, and eye movements and abnormalities of the electroencephalogram are shaken.
  • Subjective symptoms / feeling of fatigue are greatly increased, mainly due to drowsiness and tiredness of the extremities, and a strong decline in concentration is also felt.
  • There is a large extension and variation in behavioral ability and reaction time and there is a decrease in accuracy, leading to a dangerous situation due to closed eyes and stagnation.
  • the state of drowsiness is classified as follows according to the function of the autonomic nerve. ⁇ 1. If you are not drowsy> The sympathetic nerve is enhanced and the parasympathetic nerve is suppressed. Heart rate HR is large, and high-frequency fluctuation component (also referred to as high-frequency fluctuation component of heartbeat fluctuation) HF corresponding to respiratory fluctuation is small. ⁇ 2. If you have signs of sleepiness> Due to monotonous driving and fatigue, there is little awareness of sleepiness psychologically, but the signs (predictors) appear physiologically. Since the sympathetic nerve activity changes from the enhanced state to the inhibited state, the heart rate decreases. ⁇ 3.
  • the sleepiness prediction parameter b of heartbeat fluctuation is set to 0 for calculation. That is, in the manual driving state, the sleepiness prediction parameter a for heart rate change is increased, and the sleepiness prediction parameter b for heart rate fluctuation is decreased.
  • the sleepiness prediction parameter a as an element of the heart rate change is reduced, and sleepiness prediction of heart rate fluctuation is performed. Increase the parameter b.
  • the sleepiness prediction parameters a and b are coefficients for weighting the heart rate (RR interval) and heart rate fluctuation. As described above, since the heart rate and heart rate fluctuation have different contributions to sleepiness depending on the activity state, the accuracy of the calculated sleepiness is increased by weighting each.
  • Equation (5) shows an integral element when sleepiness accumulates. That is, the integrated value of the sleepiness value (physiological sleepiness Sp) is calculated.
  • the integration element Si is set to 0 at the start of driving, and then accumulates until the driving is stopped due to arrival at the destination or a break. That is, when the activity state changes, the integrated value is reset.
  • the change in the activity state can be detected by the state determination unit 2 as described above.
  • the change in the activity state may be another activity state when it is detected that the driver has left the vehicle, or when driving is stopped when the vehicle ignition switch is turned off. What is necessary is just to determine with having changed to. Moreover, what is necessary is just to determine with having changed into the driving
  • the integration element Si is integrated indefinitely as long as the specific active state is continued as described above. In this case, depending on the user, there may be a difference between sleepiness S and his / her own sense as time passes. Accordingly, an upper limit value may be provided for the integral element Si so that the value does not exceed the upper limit value. That is, when the upper limit value is exceeded, the upper limit value may be used as the integrated value.
  • the differential element Sd of the physiological sleepiness Sp is calculated by the following equation (6).
  • Sd dSp / dt (6)
  • the differential element Sd is calculated to emphasize the change in sleepiness. That is, the differential value of the sleepiness value is calculated.
  • the increase or decrease in sleepiness in a minute interval is easily perceived psychologically (Weber's law). For example, when sleepiness is displayed as a numerical value or the like during driving, the user can be more strongly aware of the change in sleepiness by enlarging a minute change and feeding it back.
  • the coefficients Ki, Kp, and Kd can be set for each individual user.
  • the coefficient Kd of the differential element Sd is Kd> 0.
  • Sensitive users increase Kd to make the change easy to feel, and insensitive users make Kd small to make the change difficult to feel.
  • the coefficients Ki, Kp, and Kd may be changed according to the physical condition of the user acquired by the vital sensor or the user's own notification.
  • the differential element Sd influences when a display or the like is notified to the user or the like, and is not necessarily an element necessary for calculating the sleepiness S. Therefore, the drowsiness S may be calculated using only the physiological drowsiness Sp and the coefficient Kp, the integral element Si, and the coefficient Ki.
  • the sleepiness S calculated by the equation (4) is set to a numerical value range of 0 to 100 so that it can be easily compared with the subjective sleepiness evaluation axis such as VAS.
  • 0 is small drowsiness and 100 is large drowsiness.
  • the sleepiness calculation unit 5 calculates the sleepiness S based on the heart rate (biological information), the activity state, and the sleepiness reference heart rate.
  • the sleepiness display unit 6 displays the sleepiness S calculated (estimated) by the sleepiness calculation unit 5.
  • the drowsiness S may be simply displayed as a numerical value at that time, or may be displayed as a bar graph or a line graph so that a change in time series can be understood.
  • FIG. 8A is a table showing the heart rate at each time, the sleepiness reference heart rate, and the calculated sleepiness S.
  • FIG. 8 (b) is a bar graph of FIG. 8 (a). That is, when the heart rate is measured as shown in FIG. 8A, it is displayed to the user as shown in FIG. 8B.
  • the drowsiness S including the above-described differential element Sd is displayed, and the drowsiness display unit 6 displays a display indicating drowsiness based on the drowsiness estimated by the drowsiness calculation unit 5 (estimating means). Functions as display control means.
  • I / F 7 is an interface (I / F) to which the heart rate sensor 11 is connected.
  • the I / F 7 is an interface corresponding to wired connection when the heart rate sensor 11 is wired connection, and an interface corresponding to wireless connection when the heart rate sensor 11 is wireless connection. That is, the I / F 7 acquires biological information (heart rate, measurement time, etc.) related to the user's heart rate measured by the user. That is, the I / F 7 functions as an acquisition unit that acquires the heart rate and the measurement time.
  • the I / F 8 is an interface (I / F) through which position information and acceleration information are input.
  • the I / F 8 may be either a wired connection interface or a wireless connection interface. That is, the I / F 8 acquires user position information and user acceleration information.
  • FIG. 9 shows a flowchart of the operation of the sleepiness estimation apparatus 1.
  • FIG. 9A is a flowchart of the overall operation.
  • the heart rate is acquired from the heart rate sensor 11, and the state determination unit 2 determines the user's activity state.
  • step S12 based on the heart rate acquired from the heart rate sensor 11, the sleepiness prediction parameter output from the parameter table 3, and the sleepiness reference heart rate output from the sleepiness reference heart rate calculation unit 4, the sleepiness calculation unit 5 To calculate sleepiness S.
  • step S13 sleepiness S is displayed.
  • FIG. 9B is a flowchart of the drowsiness calculation operation in step S12.
  • physiological drowsiness Sp is calculated by equation (3).
  • an integral element Si of the physiological sleepiness Sp is calculated.
  • the integration element Si is integrated from the start of a specific activity such as driving until it is stopped.
  • a differential element Sd of the physiological sleepiness Sp is calculated.
  • the differential element Sd is the expression (6), but in order to simplify the calculation, the Sp calculated based on the previously measured HR or the like from the Sp calculated based on the HR or the like measured this time is used. Is calculated by subtracting. That is, it calculates based on measurement intervals, such as HR.
  • drowsiness S is calculated by equation (4). That is, FIG. 9B is a sleepiness estimation step.
  • the drowsiness estimation device 1 uses the accumulated average heart rate obtained by dividing the accumulated heart rate acquired by the I / F 7 by the time after the drowsiness reference heart rate calculation unit 4 changes the user's activity state. Based on the accumulated average heart rate, a drowsiness reference heart rate that is a criterion for whether or not the user feels drowsiness is calculated. Then, the sleepiness calculation unit 5 estimates the user's current sleepiness based on the sleepiness reference heart rate and the current heart rate. By doing so, drowsiness can be estimated based on the accumulated heart rate, so the drowsiness reference heart rate can be calculated in a short time, and drowsiness can be estimated with higher accuracy.
  • the drowsiness reference heart rate calculation unit 4 calculates the cumulative average heart rate after a predetermined time elapses after the user's activity changes. By doing so, the influence of other activity states before the change can be reduced, and the calculation accuracy of the sleepiness reference heart rate can be improved.
  • the sleepiness reference heart rate calculation unit 4 determines the lowest heart rate that is the lowest value of the heart rate per predetermined time acquired by the I / F 7, and calculates the average value of the cumulative average heart rate and the minimum heart rate as sleepiness. Calculated as the reference heart rate. By doing so, the mechanism for calculating the drowsiness reference heart rate is simplified, so that it is possible to reduce processing time and development costs in the apparatus and the like.
  • the sleepiness reference heart rate calculation unit 4 calculates the cumulative average heart rate as the sleepiness reference heart rate. By doing so, the mechanism for calculating the drowsiness reference heart rate can be further simplified, and the processing time and development cost in the drowsiness estimation device 1 can be reduced. In addition, by setting the cumulative average heart rate as the drowsiness reference heart rate, it is possible to capture a sign of feeling drowsiness, and to take a measure such as prompting a break before recognizing drowsiness.
  • the drowsiness reference heart rate calculation unit 4 initializes the accumulated heart rate when the activity state changes. By doing so, it is possible to avoid being affected by changes in sleepiness caused by a break or when the day changes.
  • the drowsiness reference heart rate calculation unit 4 may calculate the cumulative average heart rate after the user changes to the activity of driving the moving body. In this way, when driving a moving body such as a vehicle, the drowsiness reference heart rate can be calculated in a short time, and drowsiness can be estimated with high accuracy.
  • the sleepiness calculation unit 5 calculates the sleepiness S of the user based on the sleepiness prediction parameter set in advance for each activity state. In this way, the heart rate and heart rate fluctuation can be weighted according to the activity state.
  • an I / F 8 for acquiring user position information and acceleration information is provided. In this way, the activity state can be detected from the moving speed and acceleration of the user.
  • the drowsiness display unit 6 since the drowsiness display unit 6 is provided, the user can specifically perceive his / her drowsiness, and can take measures such as a break and exercise based on the displayed drowsiness.
  • the sleepiness prediction parameter is selected based on the activity state and the measurement time.
  • the sleepiness prediction parameter may be selected based only on the activity state.
  • sleepiness may be notified by voice or vibration in addition to the sleepiness display unit 6.
  • notification by voice or the like may be performed in the case of drowsiness above a certain level.
  • the sleepiness criterion is based on the subjective evaluation.
  • the heart rate HR_ref and the reference high frequency fluctuation component HF_ref may be corrected.
  • the user can subjectively evaluate the sleepiness calculated (estimated) by the sleepiness calculation unit 5.
  • the drowsiness calculation unit 5 can calculate drowsiness again by feeding back the subjective evaluation result. Therefore, it is possible to calculate information regarding sleepiness tailored to the individual user with higher accuracy.
  • the parameter table 31 is provided in the server 30 as shown in FIG.
  • the server 30 also stores individual history data 32.
  • the server 30 can collectively manage data of a plurality of persons.
  • history data 32 history data such as heart rate, measurement time, activity state, etc. received from the device 1A described later is stored for each individual.
  • the server 30 transmits a reference high-frequency fluctuation component, a drowsiness prediction parameter, and the like based on a request from the device 1A or the like.
  • the device 1A and the device 1B are devices that are installed in the vicinity of the user or worn by the user, can acquire at least history data, and can transmit the acquired history data to the server 30. It has become.
  • a wristwatch type and an in-vehicle type are shown as the device 1A and the device 1B, but these can be used by the same user and the history data acquired by each can be transmitted to the server 30.
  • history data can be stored in one place, and the parameter table 31 can be shared by a plurality of devices 1A and devices 1B. .
  • FIG. 11 shows a device 1A according to the present embodiment.
  • the device 1B basically has the same configuration.
  • the state determination unit 2, the drowsiness display unit 6, and the I / F 7 are basically the same as those in the first embodiment.
  • the state determination unit 2 is different in that the determined state is output to the communication unit 12.
  • the sleepiness reference heart rate / sleepiness calculation unit 5A calculates a sleepiness reference heart rate based on the heart rate acquired by the I / F 7, and based on the sleepiness reference heart rate and the sleepiness prediction parameter acquired by the communication unit 12. Calculate sleepiness.
  • the I / F 7 outputs the acquired heart rate and measurement time to the communication unit 12 in addition to the state determination unit 2 and the drowsiness calculation unit 5.
  • the communication unit 12 communicates with the server 30.
  • the communication unit 12 transmits the heart rate measured by the heart rate sensor 11, the measurement time, and the activity state determined by the state determination unit 2 to the server 30, and receives the sleepiness prediction parameter transmitted from the server 30.
  • the server 30 may include the state determination unit 2 and the drowsiness reference heart rate / drowsiness calculation unit 5A.
  • the communication unit 12 transmits information for determining an activity state such as position information and acceleration information. That is, the server 30 may determine the activity state and calculate the sleepiness S.
  • the server 30 may include only the state determination unit 2 or the drowsiness reference heart rate / drowsiness calculation unit 5A.
  • the sleepiness estimation apparatus includes the server 30 and the device 1A (device 1B).
  • the sleepiness estimation apparatus is configured by the server and the device 1A (device 1B), even when one user uses a plurality of devices, history data such as heart rate and sleepiness reference heartbeats are used. Numbers can be managed collectively. Further, the parameter table can be shared by a plurality of devices 1A (device 1B).

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Animal Behavior & Ethology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Cardiology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Educational Technology (AREA)
  • Developmental Disabilities (AREA)
  • Child & Adolescent Psychology (AREA)
  • Social Psychology (AREA)
  • Psychology (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)
  • Hospice & Palliative Care (AREA)
  • Physiology (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

[Problème] Fournir un dispositif de calcul de somnolence capable de calculer la somnolence avec un degré de précision élevé. [Solution] La présente invention concerne un dispositif d'estimation de somnolence 1, dans lequel une unité de calcul de fréquence cardiaque de référence de somnolence 4 calcule une fréquence cardiaque moyenne cumulée en divisant un nombre total cumulé de battements cardiaques acquis au moyen d'une interface 7, à partir du moment auquel un état d'activité d'un utilisateur change, par le temps. Une fréquence cardiaque de référence de somnolence qui sert de référence pour déterminer si l'utilisateur présente une somnolence est calculée sur la base de la fréquence cardiaque moyenne cumulée. En outre, une unité de calcul de somnolence 5 estime la somnolence actuelle de l'utilisateur sur la base de la fréquence cardiaque de référence de somnolence et de la fréquence cardiaque actuelle.
PCT/JP2017/042776 2016-12-09 2017-11-29 Dispositif d'estimation de somnolence WO2018105459A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2018554948A JP6692924B2 (ja) 2016-12-09 2017-11-29 眠気推定装置

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2016239718 2016-12-09
JP2016-239718 2016-12-09

Publications (1)

Publication Number Publication Date
WO2018105459A1 true WO2018105459A1 (fr) 2018-06-14

Family

ID=62491973

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2017/042776 WO2018105459A1 (fr) 2016-12-09 2017-11-29 Dispositif d'estimation de somnolence

Country Status (2)

Country Link
JP (1) JP6692924B2 (fr)
WO (1) WO2018105459A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021033748A (ja) * 2019-08-27 2021-03-01 クラリオン株式会社 状態推定装置、状態推定プログラムおよび状態推定方法
US20220015717A1 (en) * 2018-12-12 2022-01-20 Nippon Telegraph And Telephone Corporation Activity State Analysis Device, Activity State Analysis Method and Activity State Analysis System
JP7537208B2 (ja) 2020-09-24 2024-08-21 株式会社Ｊｖｃケンウッド 情報処理装置、情報処理方法、およびプログラム

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114283559B (zh) * 2022-03-04 2022-05-17 西南交通大学 一种驾驶员疲劳预警方法、装置、设备及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010273752A (ja) * 2009-05-27 2010-12-09 Kamata Toru 入眠判定システム
JP2011239891A (ja) * 2010-05-17 2011-12-01 Nippon Telegr & Teleph Corp <Ntt> 感情強度測定装置、感情強度測定方法、及びプログラム
JP2016523632A (ja) * 2013-06-17 2016-08-12 ニューヨーク ユニバーシティ 神経学的および眼科的機能の評価ならびに神経学的病変の位置特定のための方法およびキット
JP2016192127A (ja) * 2015-03-31 2016-11-10 パイオニア株式会社 楽曲情報更新装置

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016067811A (ja) * 2014-10-01 2016-05-09 セイコーエプソン株式会社 生体情報検出システム、生体情報検出装置及び生体情報検出システムの制御方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010273752A (ja) * 2009-05-27 2010-12-09 Kamata Toru 入眠判定システム
JP2011239891A (ja) * 2010-05-17 2011-12-01 Nippon Telegr & Teleph Corp <Ntt> 感情強度測定装置、感情強度測定方法、及びプログラム
JP2016523632A (ja) * 2013-06-17 2016-08-12 ニューヨーク ユニバーシティ 神経学的および眼科的機能の評価ならびに神経学的病変の位置特定のための方法およびキット
JP2016192127A (ja) * 2015-03-31 2016-11-10 パイオニア株式会社 楽曲情報更新装置

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220015717A1 (en) * 2018-12-12 2022-01-20 Nippon Telegraph And Telephone Corporation Activity State Analysis Device, Activity State Analysis Method and Activity State Analysis System
JP2021033748A (ja) * 2019-08-27 2021-03-01 クラリオン株式会社 状態推定装置、状態推定プログラムおよび状態推定方法
CN112441009A (zh) * 2019-08-27 2021-03-05 歌乐株式会社 状态推算装置、状态推算方法和存储介质
JP7328828B2 (ja) 2019-08-27 2023-08-17 フォルシアクラリオン・エレクトロニクス株式会社 状態推定装置、状態推定プログラムおよび状態推定方法
CN112441009B (zh) * 2019-08-27 2024-03-12 歌乐株式会社 状态推算装置、状态推算方法和存储介质
JP7537208B2 (ja) 2020-09-24 2024-08-21 株式会社Ｊｖｃケンウッド 情報処理装置、情報処理方法、およびプログラム

Also Published As

Publication number Publication date
JPWO2018105459A1 (ja) 2019-10-24
JP6692924B2 (ja) 2020-05-13

Similar Documents

Publication Publication Date Title
US10339781B2 (en) Methods and apparatus for monitoring alterness of an individual utilizing a wearable device and providing notification
JP4697185B2 (ja) 覚醒度判定装置及び覚醒度判定方法
JP4802842B2 (ja) 健康管理支援装置及び健康管理システム
US7961085B2 (en) Method to monitor manual steering of dynamic systems and device
WO2018105459A1 (fr) Dispositif d&#39;estimation de somnolence
JP2007203913A (ja) 運転支援装置及び運転支援システム
WO2007052729A1 (fr) Detecteur de l’etat d’une personne
US11712538B2 (en) Traumatic nightmare detection and intervention
US20180365961A1 (en) Providing safe and fast mobility while detecting drowsiness
JP2016182242A (ja) 眠気算出装置
JP6462855B2 (ja) 眠気算出装置
JP2016184282A (ja) 情報出力装置
JP2019093175A (ja) 眠気算出装置
JP2019069207A (ja) 眠気算出装置
JP2018033533A (ja) 眠気基準心拍数算出装置
JP2016182241A (ja) 眠気算出装置
JP2013252764A (ja) 居眠り警告装置
JP7432899B2 (ja) 漫然状態判定装置
JP6836358B2 (ja) 眠気推定装置
JP2016187430A (ja) 回復支援装置
JP2015195982A (ja) 体調判定装置
CN106974635A (zh) 一种疲劳驾驶实时监测系统
JP2019205894A (ja) 眠気算出装置
JP2023165016A (ja) 眠気算出装置
Funke et al. Monitoring Task Fatigue in Contemporary and Future Vehicles: A Review

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17877843

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2018554948

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17877843

Country of ref document: EP

Kind code of ref document: A1

点击 这是indexloc提供的php浏览器服务，不要输入任何密码和下载