WO2010032424A1 - 眠気予兆検出装置 - Google Patents
眠気予兆検出装置 Download PDFInfo
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- WO2010032424A1 WO2010032424A1 PCT/JP2009/004572 JP2009004572W WO2010032424A1 WO 2010032424 A1 WO2010032424 A1 WO 2010032424A1 JP 2009004572 W JP2009004572 W JP 2009004572W WO 2010032424 A1 WO2010032424 A1 WO 2010032424A1
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- angular velocity
- eye movement
- drowsiness
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- sign
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/113—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining or recording eye movement
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/11—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for measuring interpupillary distance or diameter of pupils
- A61B3/112—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for measuring interpupillary distance or diameter of pupils for measuring diameter of pupils
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording 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, mobility of a limb
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording 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, mobility of a limb
- A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
- A61B5/1114—Tracking parts of the body
-
- 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
- A61B5/163—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state by tracking eye movement, gaze, or pupil change
-
- 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
-
- 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
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/19—Sensors therefor
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
Definitions
- the present invention relates to a drowsiness sign detection device that detects a sign before a driver of a vehicle, an operator of a facility, or the like is aware of drowsiness by using vestibulo-oculomotor reflex induced by head movement.
- a drowsiness detection device that detects drowsiness of a driver of a vehicle
- a device that detects drowsiness by using biological signals such as brain waves, blinks, and eye movements caused by drowsiness as an index has been proposed.
- a blink having a closing time longer than the evaluation time is detected based on a blink evaluation time set from a blink closing time specific to the driver at awakening.
- a drowsiness detection device that detects drowsiness from the ratio of a long blink with respect to is disclosed. JP-A-10-272960
- LLFF large low-frequency fluctuation
- GM monotonic miosis
- an object of the present invention is to realize a drowsiness sign detection device having a wide practical condition range for detecting a sign before a driver of a vehicle, an operator of a machine, etc. is aware of drowsiness.
- a head movement detection unit that detects head movement
- an eye movement detection unit that detects eye movement
- Ideal eye movement angular velocity calculation means for calculating an ideal eye movement angular velocity based on the head movement data detected by the head movement detection means
- eyeball rotation angular velocity based on the eye movement data detected by the eye movement detection means
- a drowsiness sign determination unit that detects a vestibular eye movement reflex (VOR) from the ideal eye movement angular speed and the eyeball rotation angle speed and determines a sign of drowsiness based on the vestibular eye movement reflex.
- VOR vestibular eye movement reflex
- the head movement is detected by the head movement detection means
- the eye movement is detected by the eye movement detection means
- detected by the head movement detection means by the ideal eye movement angular velocity calculation means.
- the ideal eye movement angular velocity is calculated based on the head movement data
- the eye rotation angular velocity calculation means calculates the eye rotation angular speed based on the eye movement data detected by the eye movement detection means
- the drowsiness sign determination means calculates the ideal eyeball.
- Vestibulo-oculomotor reflexes are not easily affected by the external environment such as ambient brightness, so that the practical condition range can be widened.
- the calculation load is small, real-time measurement and determination are possible.
- the drowsiness sign determination means approximates the eyeball rotation angular velocity with the following equation which is a linear expression of the ideal eye movement angular velocity.
- a VOR gain defined by G and a decrease rate of the VOR gain are calculated, and when the VOR gain decrease rate exceeds a preset threshold value, a technical means is used to determine a sign of drowsiness. .
- e (t) G h (t- ⁇ ) + dc + ⁇ (t) e (t): Eye rotation angular velocity, G: VOR gain, h (t): Ideal eye movement angular velocity, ⁇ : Delay time of eye movement relative to head movement, dc: Constant term, ⁇ (t): Remaining regression model difference
- the drowsiness sign determination means is preferably used for determination of a drowsiness sign in order to determine a drowsiness sign when the decrease rate of the VOR gain exceeds a preset threshold. be able to.
- the drowsiness sign determination means uses the following equation that is a primary expression of the ideal eye movement angular velocity. Approximate residual and residual standard deviation defined by ⁇ (t) when approximated are calculated, and when the rate of increase of the residual standard deviation exceeds a preset threshold, it is determined as a sign of drowsiness The technical means to do is used.
- e (t) G h (t- ⁇ ) + dc + ⁇ (t) e (t): Eye rotation angular velocity, G: VOR gain, h (t): Ideal eye movement angular velocity, ⁇ : Delay time of eye movement relative to head movement, dc: Constant term, ⁇ (t): Remaining regression model difference
- the drowsiness sign determination means is determined to be a drowsiness sign when the rate of increase in the residual standard deviation exceeds a preset threshold value. Can be used. In particular, when used together with the invention according to claim 2, it is possible to improve the determination accuracy of the drowsiness sign.
- the drowsiness sign detection device according to any one of the first to third aspects, wherein the drowsiness sign detection device is mounted on a vehicle, wherein the head movement detection means includes: The technical means of calculating the ideal eye movement angular velocity using the output of the acceleration sensor and the gyro sensor provided in the vehicle is used.
- the ideal eyeball is detected by using the output of the acceleration sensor and gyro sensor provided in the vehicle as the head movement detection means.
- the angular velocity of motion can be calculated. Thereby, the structural member of the drowsiness sign detection apparatus can be decreased.
- FIG. 1 is a configuration diagram of a drowsiness sign detection apparatus.
- FIG. 2 is a flowchart showing a drowsiness sign detection method.
- the drowsiness sign detection device 10 is detected by a head movement detection unit 11 that detects head movement, an eye movement detection unit 12 that detects eye movement, and a head movement detection unit 11.
- Ideal eye movement angular velocity calculation means 13 for calculating ideal eye movement angular speed based on head movement data
- eye rotation angular speed calculation means 14 for calculating eye rotation angular speed based on eye movement data detected by eye movement detection means 12.
- a drowsiness sign determination means 15 for detecting vestibular eye movement reflex (VOR) from the ideal eye movement angular velocity and eyeball rotation angle speed, and for determining a sign of drowsiness based on the vestibular eye movement reflex.
- VOR vestibular eye movement reflex
- the head movement detection means 11 detects linear acceleration and rotational angular velocity generated in the head, and a three-axis acceleration sensor that detects linear acceleration and a gyroscope that detects rotational angular velocity can be used.
- a three-axis acceleration sensor that detects linear acceleration and a gyroscope that detects rotational angular velocity
- the driver when mounted on a vehicle such as an automobile, the driver is configured to be able to detect the linear acceleration in the traveling direction, the vertical direction, and the horizontal direction of the driver, and the rotational angular velocity in the rolling, pitching, and yawing directions of the driver. .
- the head movement is detected, and the acceleration data and the rotational angular velocity data are transmitted to the ideal eye movement angular velocity calculating means 13.
- the eye movement detection means 12 captures an eyeball image, and for example, a digital photographing device such as a CCD camera can be suitably used.
- a digital photographing device such as a CCD camera
- the drowsiness sign detection device 10 is mounted on a vehicle such as an automobile, for example, it can be placed in a place where it is easy to capture the eyeball image of the driver, such as in the vicinity of the instrument panel.
- the ideal eye movement angular velocity calculation means 13 is connected to the head movement detection means 11 and is an ideal eyeball for compensating for head movement based on the head movement data detected by the head movement detection means 11.
- the ideal eye movement angular velocity which is the angular velocity of the movement, is calculated.
- a personal computer (PC) for calculation processing can be used as the ideal eye movement angular velocity calculation means 13, for example, a personal computer (PC) for calculation processing can be used.
- the eyeball rotation angular velocity calculation means 14 is connected to the eyeball movement detection means 12 and calculates the eyeball rotation angular speed based on the eye movement data detected by the eye movement detection means 12.
- the eyeball rotation angular velocity calculation means 14 for example, a personal computer (PC) for calculation processing can be used in the same manner as the ideal eye movement angular velocity calculation means 13.
- PC personal computer
- the drowsiness sign determination means 15 is connected to the ideal eye movement angular velocity calculation means 13 and the eyeball rotation angular speed calculation means 14, and parameters related to vestibular oculomotor reflex (VOR) from the ideal eye movement angular velocity and the eyeball rotation angular velocity. As described below, a VOR gain G and a residual standard deviation SDres, which will be described later, are calculated, and a sign of drowsiness is determined based on at least one parameter.
- a personal computer (PC) for arithmetic processing can be used in the same manner as the ideal eye movement angular velocity calculation means 13 and eyeball rotation angular velocity calculation means 14.
- the ideal eye movement angular velocity calculation means 13, the eyeball rotation angular velocity calculation means 14, and the drowsiness sign determination means 15 can be configured as the same computing personal computer. Further, when the drowsiness sign detection device 10 is mounted on a vehicle such as an automobile, for example, the engine control unit (ECU) can be shared as a computing personal computer. Thereby, the structural member of the drowsiness sign detection apparatus 10 can be decreased.
- ECU engine control unit
- step S ⁇ b> 1 the linear acceleration and the rotational angular velocity generated in the head are detected by the head movement detection unit 11.
- the linear acceleration in the traveling direction, the vertical direction, and the horizontal direction of the driver, and the rotational angular velocity in each of the rolling, pitching, and yawing directions of the driver are detected.
- the linear acceleration data and the rotational angular velocity data are transmitted to the ideal eye movement angular velocity calculating means 13.
- the vertical acceleration data in the vertical direction and the rotational angular velocity with respect to the pitching direction are used, but other components can also be used.
- step S2 the ideal eye movement angular velocity calculation means 13 calculates the ideal eye movement angular velocity based on the head movement detected in step S1, and transmits the ideal eye movement angular velocity data to the drowsiness sign determination means 15. .
- step S3 the eye movement detection means 12 captures the eye movement, measures the movement amount of the pupil center coordinates, and stores the eye movement data for each of the rolling, pitching, and yawing directions of the driver to the eye rotation angular velocity calculation means 14. Send.
- step S4 the eyeball rotation angular velocity calculation unit 14 calculates the eyeball rotation angular velocity based on the eye movement data photographed in step S3, and transmits the eyeball rotation angular velocity data to the drowsiness sign determination unit 15.
- step S5 the drowsiness sign determination means 15 obtains the following VOR gain G and residual standard deviation SDres as parameters relating to the vestibulo-oculomotor reflex based on the ideal eye movement angular velocity data and eyeball rotation angular velocity data.
- the vestibulo-ocular reflex is a reflective eye movement for rotating the eyeball in the opposite direction at substantially the same speed as the head movement to obtain a blur-free field of view.
- the VOR gain is obtained by least-squares estimation as a regression model coefficient G in which the objective variable is an eyeball rotation angular velocity e (t) and the explanatory variable is an ideal eye movement angular velocity h (t) and a constant term dc.
- ⁇ (t) is the residual of the regression model
- ⁇ is the delay time of the eye movement with respect to the ideal eye movement.
- the VOR gain may be calculated in at least one direction of the driver's traveling direction, vertical direction, and horizontal direction.
- the residual standard deviation SDres is calculated by the following equation.
- N is the number of data points to be measured.
- step S6 in order to quantify the increase in the VOR gain and the increase in the residual standard deviation, the following index is calculated by the drowsiness sign determination means 15. That is, since the initial value and the amount of change of the VOR gain and the residual standard deviation SDres differ depending on individual differences, the average value of the high arousal state is obtained and the rate of change with respect to the average value is calculated.
- the VOR gain decrease rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) at a certain time are respectively defined and calculated as follows.
- step S7 the drowsiness sign determination means 15 compares the VOR gain decrease rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) obtained in step S6 with threshold values th1 and th2, respectively set in advance. If both the VOR gain decrease rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) exceed the threshold (S7: YES), it is determined that a sign of sleepiness has been detected, and the process proceeds to the subsequent step S8. To do.
- VOR gain decrease rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) is equal to or less than the threshold (S7: NO)
- step S8 the drowsiness sign determination means 15 outputs a drowsiness sign detection signal, and the series of processing ends.
- the drowsiness sign detection device 10 of the present invention it is possible to determine a sign of drowsiness before the driver of the vehicle, the operator of the machine, etc. are aware of drowsiness based on the vestibulo-oculomotor reflex. . Vestibulo-oculomotor reflexes are not easily affected by the external environment such as ambient brightness, so that the practical condition range can be widened. In addition, since the calculation load is small, real-time measurement and determination are possible.
- steps S1 and S2 and steps S3 and S4 are arbitrary.
- FIG. 3 shows a schematic diagram of an experimental system that simulates driving of a vehicle.
- the driving simulator (DS) system 21 includes a projector that projects a DS image, and a driver seat that includes a screen, a steering, an accelerator, and a brake.
- the driver seat was placed at the position where the subject's head and the center of the screen face each other.
- the distance between the front surface of the subject's head and the screen is 2470 mm, and the screen size is 100 inches (left and right viewing angles ⁇ 39.1 deg, vertical viewing angles ⁇ 26.3 deg).
- a vibration device 22 for inducing vestibulo-oculomotor reflexes was installed below the driver seat.
- the brightness and contrast of the projector were adjusted so that the pupil diameter of the subject was approximately the middle diameter of the pupil movable range (diameter of about 6 mm).
- the eyeball image was measured at 29.97 fps by the eyeball rotation imaging device 23.
- a three-axis acceleration sensor (not shown) and three gyroscopes were mounted on the eyeball rotation imaging device 23, and the linear acceleration and rotation angular velocity in the three-axis directions were measured respectively.
- the head movement data was synchronized with the eyeball image, AD-converted by the AD / DA converter 24 at a sampling frequency of 1 kHz, and recorded in the PC 25 for storing eyelid data. Further, the eyeball image and head movement data were branched and input to the arithmetic processing PC 26, and the head movement, eye movement and pupil diameter change were observed in real time.
- the triaxial acceleration sensor and the gyroscope are the head movement detecting means
- the eyeball rotation imaging device 23 is the eyeball movement detecting means
- the arithmetic processing PC 26 is the ideal eye movement angular velocity calculating means, eyeball rotation angular velocity calculating means, and sleepiness. Each corresponds to a sign determination means.
- test subject was seated on the driver seat in a natural posture after wearing the eyeball rotation imaging device 23, the acceleration sensor, and the gyroscope, and was subjected to the experimental task described below.
- the ideal eye movement angular velocity h (t) can be calculated from the linear acceleration and rotational angular velocity generated in the head. In this example, since the contribution of linear acceleration was small, it was calculated based on the rotational angular velocity of the head.
- the ideal eye movement angular velocity h (t) was calculated by the arithmetic processing PC 26 after resampling at a sampling frequency of 29.97 Hz in order to match the eye movement data and the number of data points after removing noise by a digital bandpass filter.
- the extraction of the eye movement angle and the pupil diameter was calculated by calculating the eyeball image by the eyeball rotation imaging device 23 by the calculation processing PC 26.
- the eyeball rotation angular velocity e (t) was obtained by differentiating the eyeball movement angle. Note that the ideal eye movement angular velocity h (t) can also be calculated taking into account the linear acceleration generated in the head.
- the VOR gain and the residual standard deviation (SDres) were calculated by the calculation processing PC 26.
- the VOR gain and residual standard deviation (SDres) are 40-second data that can be obtained with sufficient estimation accuracy even after rapid phase removal from eye movement data, and each segment is 10 seconds with an overlap of 30 seconds. The value in each segment was calculated.
- FIG. 4 is an explanatory diagram showing test results.
- FIG. 4A is a plot in which the ideal eye movement angular velocity and the eyeball rotation angular velocity at the time of a typical subject's arousal (during mental calculation task) are overlapped. It can be confirmed that the normal VOR in which the eye movement reversely occurs at almost the same speed with respect to the head movement is induced.
- FIG. 4B shows the rotation of the eyeball with the data of FIG. The plot is made by plotting the angular velocity and the ideal eye movement angular velocity on the horizontal axis, and the straight line is obtained by fitting the regression line of Equation 1.
- the VOR gain was 0.802 and SDres was 1.017.
- FIGS. 4 (c) corresponds to FIG. 4 (a)
- FIG. 4 (d) corresponds to FIG. 4 (b).
- the VOR gain was 0.673 and the SDres was 2.964.
- the VOR gain was decreased and the SDres was increased as compared with the awake state.
- FIG. 5 is an explanatory diagram comparing the VOR gain and SDres with changes in pupil diameter that have been confirmed to be effective as sleepiness and its predictive detection index. From 1 minute to about 2 minutes after the mental arithmetic task is stopped, the VOR gain gradually decreases, and SDres gradually increases in contrast. Furthermore, a monotonic miosis (GM) can be confirmed in the pupil diameter change in a section where the VOR gain and SDres start to decrease and increase, respectively. In this GM section, it has been shown from previous studies that the subject is not yet aware of drowsiness. That is, a characteristic change such as a decrease in VOR gain or an increase in SDres in this interval is a sign signal of sleepiness.
- GM miosis
- LLFF large low frequency fluctuation
- FIG. 6 is an explanatory diagram showing the relationship between the VOR gain reduction rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) and the signs of drowsiness.
- FIG. 6A shows the VOR gain reduction in the six experimental data.
- the rate ⁇ VOR (t) and the increase rate of SDres ⁇ SDres (t) are plotted on the vertical axis and the horizontal axis, respectively. Black circles indicate data of an awake state, and X indicates data of a section where sleepiness is present. In the upper part and the right part of the figure, the distribution of each state is plotted with weighting so that the area becomes 1.
- a threshold value is set for each of ⁇ VOR (t) and ⁇ SDres (t) so as to include all the data of the arousal state, an area below the two threshold values is set as an arousal area, and an area above the threshold value is set as a sleepiness area.
- FIG. 6B is a plot of ⁇ VOR (t) and ⁇ SDres (t) from the end of the mental arithmetic task to the perception of sleepiness.
- FIG. 6A shows that 95% of the data reported as having sleepiness is plotted in the sleepiness area, and it can be confirmed that the presence or absence of sleepiness can be separated. As can be seen from the respective distributions shown in FIG. 6 (b), most of the data exists in the arousal area, while some data also extends to the sleepiness area.
- a signal that is plotted in the sleepiness area and appears continuously for 40 seconds or more is defined as a predictive signal of sleepiness. This is because if the duration is less than 30 seconds, there is a possibility that the influence of wrinkle overlap may be included. As a result, 5 data (83.3%) showed signs of sleepiness. One data in which no sign of drowsiness was observed was also plotted in the sleepiness area, but was not recognized as a sign of drowsiness because it did not appear continuously for more than 40 seconds.
- the drowsiness symptom can be easily detected in most subjects by setting a threshold value that does not depend on the subject for the high arousal state of each subject defined in advance.
- the head movement is detected by the head movement detection means 11, the eye movement is detected by the eye movement detection means 12, and the head movement is detected by the ideal eye movement angular velocity calculation means 13.
- the ideal eye movement angular velocity is calculated based on the head movement data detected by the head movement detection means 11, and the eye rotation angular velocity is calculated based on the eye movement data detected by the eye movement detection means 12 by the eyeball rotation angular speed calculation means 14.
- the vestibular oculomotor reflex (VOR) is detected from the ideal eye movement angular velocity and the eyeball rotational angular velocity by the drowsiness sign determination means 15 and the driver of the vehicle, the machine operator, etc.
- the decrease in the VOR gain and the increase in the residual standard deviation are effective as an index indicating a sign of sleepiness because they occur before the sleepiness is recognized.
- the drowsiness sign determination means 15 calculates the VOR gain decrease rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t), respectively, and compares them with thresholds L1 and L2 set in advance, respectively, and VOR gain decrease rate ⁇ VOR (t ) And SDres increase rate ⁇ SDres (t) both exceed the threshold, it can be determined that a sign of sleepiness has been detected. As a result, it was confirmed that drowsiness signs can be easily detected in most subjects.
- the ideal eye movement angular velocity can be calculated using the output of an acceleration sensor and a gyro sensor included in the vehicle as head movement detection means. . Thereby, the structural member of the drowsiness sign detection apparatus can be decreased.
- step S7 it is determined whether or not both the VOR gain reduction rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) exceed the threshold value. For simplicity, only one of them is calculated. The sign of sleepiness may be determined based on whether or not the threshold is exceeded.
- step S8 By inputting the drowsiness sign detection signal output in step S8 to the warning device, the driver is awakened by a method such as vibrating the seat by the warning device, fastening the seat belt, or warning by sound. be able to.
- the head movement detection unit 11 is not limited to this as long as the head movement can be detected.
- a configuration is adopted in which a head image is detected by photographing a facial image of a driver or an operator, analyzing the image in the eyeball rotation angular velocity calculation means 14, and detecting the direction of the face direction for each frame. You can also.
- the head movement detection means 11 can also serve as the CCD camera used as the eye movement detection means 12.
- FIG. 4A is a plot in which the ideal eye movement angular velocity and the eyeball rotation angular velocity at the time of a typical subject's arousal (during mental calculation task) are overlapped.
- FIG. 4B is a plot of the data of FIG. 4A with the eyeball rotation angular velocity on the vertical axis and the ideal eye movement angular velocity on the horizontal axis.
- 4 (c) and 4 (d) are test results in a section in which the subject felt sleepy.
- FIG. 4 (c) is shown in FIG. 4 (a), and FIG. 4 (d) is shown in FIG. 4 (b). It corresponds. It is explanatory drawing which compared VOR gain and SDres with the pupil diameter change. It is explanatory drawing which shows the relationship between the VOR gain reduction
- FIG. 6A is a plot of the VOR gain decrease rate ⁇ VOR (t) and the SDres increase rate ⁇ SDres (t) in the six experimental data with respect to the vertical axis and the horizontal axis, respectively.
- FIG. 6B is a plot of ⁇ VOR (t) and ⁇ SDres (t) from the end of the mental arithmetic task to the perception of sleepiness.
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Abstract
Description
e(t) = G h(t-τ) + dc +ε(t)
e(t):眼球回転角速度、G:VORゲイン、h(t):理想眼球運動角速度、τ:頭部運動に対する眼球運動の遅れ時間、dc:定数項、ε(t):回帰モデルの残差
e(t) = G h(t-τ) + dc +ε(t)
e(t):眼球回転角速度、G:VORゲイン、h(t):理想眼球運動角速度、τ:頭部運動に対する眼球運動の遅れ時間、dc:定数項、ε(t):回帰モデルの残差
e(t) = G h(t-τ) + dc +ε(t)
以下に、本発明の実施例を示す。図3に、自動車運転時を模擬した実験システムの概略図を示す。ドライビングシミュレータ(DS)システム21は、DS映像を投影するプロジェクターとスクリーン、ステアリング、アクセル、ブレーキを備えたドライバーシートから構成される。ドライバーシートは、被験者の頭部とスクリーンの中心が正対する位置に設置した。被験者頭部前面-スクリーン間距離は2470mm、スクリーンサイズは100inch(左右視野角±39.1deg、上下視野角±26.3deg)である。ドライバーシート下部には前庭動眼反射を誘発するための振動装置22を設置した。被験者の瞳孔径が瞳孔可動範囲のほぼ中間径(直径6mm程度)となるように、プロジェクターの輝度ならびにコントラストを調節した。
理想眼球運動角速度h(t)は、頭部に生じる直線加速度及び回転角速度から算出することができる。本実施例では、直線加速度の寄与が小さかったため、頭部の回転角速度に基づいて算出した。理想眼球運動角速度h(t)は、デジタルバンドパスフィルタによりノイズを除去した後、眼球運動データとデータ点数をあわせるため、サンプリング周波数29.97Hzでリサンプリングして、演算処理用PC26により算出した。眼球運動角及び瞳孔径の抽出は、眼球回旋撮影装置23による眼球映像を演算処理用PC26により演算処理して算出した。眼球回転角速度e(t)は、眼球運動角を微分処理することにより求めた。なお、理想眼球運動角速度h(t)は、頭部に生じる直線加速度を考慮に入れて算出することもできる。
図4は、試験結果を示す説明図である。図4(a)は、典型的な被験者の覚醒時(暗算課題中)の理想眼球運動角速度と眼球回転角速度を重ねてプロットしたものである。頭部運動に対し,眼球運動がほぼ同じ速さで逆向に生じる通常のVORが誘発されていることが確認できる. 図4(b)は、図4(a)のデータを縦軸に眼球回転角速度、横軸に理想眼球運動角速度をとってプロットしたものであり,直線は式1の回帰直線を当てはめたものである。この例では、VORゲインは0.802、SDresは1.017であった。
上述の実験と同条件の実験タスクにおいて、2分ごとに眠気に関する内省を被験者に報告させ、VORゲイン、SDresの変化から眠気の予兆を判定した。判定には、式3及び式4で定義したVORゲイン減少率ΔVOR(t)及びSDresの増加率ΔSDres(t)を用いた。図6は、VORゲイン減少率ΔVOR(t)及びSDresの増加率ΔSDres(t)と眠気の予兆との関係を示す説明図であり、図6(a)は、6つの実験データにおけるVORゲイン減少率ΔVOR(t)及びSDresの増加率ΔSDres(t)をそれぞれ縦軸, 横軸にとってプロットしたものである。黒丸印は覚醒状態、×印は眠気有りとした区間のデータである。また、図中上部及び右部には各状態の分布を面積が1になるように重み付けを行いプロットした。ここで, 覚醒状態のデータ全てを含むように、ΔVOR(t)、ΔSDres(t)それぞれに閾値を設定し、2つの閾値以下のエリアをarousal area、閾値以上をsleepiness areaとした。図6(b)は、暗算課題終了後から眠気を知覚するまでのΔVOR(t)、ΔSDres(t)をプロットしたものである。図6(a)より眠気有りと報告したデータの内95%がsleepiness areaにプロットされ、眠気の有無を分離できていることが確認できる。図6(b)に示したそれぞれの分布からわかるように, ほとんどのデータはarousal area内に存在する一方、いくつかのデータはsleepiness areaにも及んでいる。
(1)本発明の眠気予兆検出装置10によれば、頭部運動検出手段11により頭部運動を検出し、眼球運動検出手段12により眼球運動を検出し、理想眼球運動角速度算出手段13により頭部運動検出手段11により検出された頭部運動データに基づいて理想眼球運動角速度を算出し、眼球回転角速度算出手段14により眼球運動検出手段12により検出された眼球運動データに基づいて眼球回転角速度を算出し、眠気予兆判定手段15により理想眼球運動角速度と眼球回転角速度とから前庭動眼反射(VOR)を検出し、この前庭動眼反射に基づいて、車両の運転者、機械の操作者などが眠気を自覚する前の眠気の予兆を判定することができる。前庭動眼反射は、周囲の明るさなどの外部環境の影響を受けにくいので、実用条件範囲を広くすることができる。また、演算負荷が小さいため、リアルタイム測定及び判定が可能である。
(1)ステップS7では、VORゲイン減少率ΔVOR(t)及びSDresの増加率ΔSDres(t)がともに閾値を超えているか否かを判断したが、簡易的には、どちらか一方だけ算出して、閾値を超えているか否かで眠気の予兆を判定してもよい。
11 頭部運動検出手段
12 眼球運動検出手段
13 理想眼球運動角速度算出手段
14 眼球回転角速度算出手段
15 眠気予兆判定手段
21 ドライビングシミュレータ(DS)システム
22 振動装置
23 眼球回旋撮影装置(眼球運動検出手段)
26 演算処理用PC(理想眼球運動角速度算出手段、眼球回転角速度算出手段、眠気予兆判定手段)
Claims (4)
- 頭部運動を検出する頭部運動検出手段と、
眼球運動を検出する眼球運動検出手段と、
前記頭部運動検出手段により検出された頭部運動データに基づいて理想眼球運動角速度を算出する理想眼球運動角速度算出手段と、
前記眼球運動検出手段により検出された眼球運動データに基づいて眼球回転角速度を算出する眼球回転角速度算出手段と、
前記理想眼球運動角速度と前記眼球回転角速度とから前庭動眼反射(VOR)を検出し、この前庭動眼反射に基づいて眠気の予兆を判定する眠気予兆判定手段と、を備えたことを特徴とする眠気予兆検出装置。 - 前記眠気予兆判定手段は、前記眼球回転角速度を前記理想眼球運動角速度の一次式である下式で近似した際のGで定義されるVORゲインとそのVORゲインの減少率とを算出し、前記VORゲインの減少率があらかじめ設定された閾値を超えた場合に、眠気の予兆と判定することを特徴とする請求項1に記載の眠気予兆検出装置。
e(t) = G h(t-τ) + dc +ε(t)
e(t):眼球回転角速度、G:VORゲイン、h(t):理想眼球運動角速度、τ:頭部運動に対する眼球運動の遅れ時間、dc:定数項、ε(t):回帰モデルの残差 - 前記眠気予兆判定手段は、前記眼球回転角速度を前記理想眼球運動角速度の一次式である下式で近似した際のε(t)で定義される近似残差と残差標準偏差とを算出し、前記残差標準偏差の増加率があらかじめ設定された閾値を超えた場合に、眠気の予兆と判定することを特徴とする請求項1または請求項2に記載の眠気予兆検出装置。
e(t) = G h(t-τ) + dc +ε(t)
e(t):眼球回転角速度、G:VORゲイン、h(t):理想眼球運動角速度、τ:頭部運動に対する眼球運動の遅れ時間、dc:定数項、ε(t):回帰モデルの残差 - 車両に搭載される眠気予兆検出装置であって、前記頭部運動検出手段において、前記車両が備えている加速度センサ及びジャイロセンサの出力を用いて理想眼球運動角速度を算出することを特徴とする請求項1ないし請求項3のいずれか1つに記載の眠気予兆検出装置。
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CN102159136B (zh) | 2013-09-04 |
EP2359748B1 (en) | 2020-04-08 |
EP2359748A1 (en) | 2011-08-24 |
US20120069301A1 (en) | 2012-03-22 |
CN102159136A (zh) | 2011-08-17 |
JPWO2010032424A1 (ja) | 2012-02-02 |
KR20110060932A (ko) | 2011-06-08 |
KR101576319B1 (ko) | 2015-12-09 |
US8356899B2 (en) | 2013-01-22 |
JP5255063B2 (ja) | 2013-08-07 |
EP2359748A4 (en) | 2014-02-19 |
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