JPH08182667A - Awakening-degree judgement device - Google Patents
Awakening-degree judgement deviceInfo
- Publication number
- JPH08182667A JPH08182667A JP32887194A JP32887194A JPH08182667A JP H08182667 A JPH08182667 A JP H08182667A JP 32887194 A JP32887194 A JP 32887194A JP 32887194 A JP32887194 A JP 32887194A JP H08182667 A JPH08182667 A JP H08182667A
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- awakening
- rate
- variance
- heart rate
- samples
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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
- A61B5/16—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
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- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Pathology (AREA)
- Heart & Thoracic Surgery (AREA)
- Hospice & Palliative Care (AREA)
- Psychiatry (AREA)
- Psychology (AREA)
- Social Psychology (AREA)
- Physics & Mathematics (AREA)
- Developmental Disabilities (AREA)
- Biophysics (AREA)
- Child & Adolescent Psychology (AREA)
- Biomedical Technology (AREA)
- Educational Technology (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Auxiliary Drives, Propulsion Controls, And Safety Devices (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、覚醒度判定装置に関
し、特に、車両の走行中、船の航行中、航空機の飛行中
などのような乗物の運行中に、乗物の運転者の覚醒度が
低下した状態、すなわち居眠り状態を判定する装置に関
する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a wakefulness determination device, and more particularly, to a wakefulness of a driver of a vehicle while the vehicle is in operation, such as running a vehicle, sailing a ship, or flying an aircraft. The present invention relates to a device for determining a state in which the power consumption is lowered, that is, a dozing state.
【０００２】[0002]
【従来の技術】例えば、車両の運転者が、走行中に不意
に居眠り状態になることによる危険を避けるため、運転
者の覚醒度を判定し、居眠り状態になったとき、運転者
に警報を発するものとして、高速フーリェ変換（ＦＦ
Ｔ）による分析を使用したものがある（特開平１−１３
１６４８号公報）。これは、心拍間隔の時系列データを
ＦＦＴ分析し、０．１〔１／beat〕近傍のピークの大き
さを覚醒度判定の指標とし、この指標が初期値よりも一
定以上大きくなったとき、居眠り状態であると判断する
ものである。2. Description of the Related Art For example, in order to avoid the danger of the driver of a vehicle suddenly falling asleep while driving, the driver's awakening level is determined and an alarm is given to the driver when the driver falls asleep. Fast Fourier transform (FF)
Some of them use the analysis by T) (Japanese Patent Laid-Open No. 1-13
1648). This is an FFT analysis of time-series data of heartbeat intervals, and the size of a peak near 0.1 [1 / beat] is used as an index for awakening level determination, and when this index becomes larger than an initial value by a certain amount or more, It is determined to be in a dozing state.
【０００３】前記ＦＦＴ分析の場合、通常数十拍、少な
くとも十数拍のデータを用いないと、前記０．１〔１／
beat〕近傍のピークを検出できないことから、この拍数
よりも短い瞬間的な意識低下に対処できない。そこで、
より少ない拍数で覚醒度を判定する方法が開発され、別
途特許出願された（特願平２−４１７３０号）。In the case of the FFT analysis, if the data of several tens of beats, or at least a few dozens of beats is not used, the above-mentioned
It is impossible to deal with the momentary loss of consciousness that is shorter than this number of beats, because the peak in the vicinity of beat] cannot be detected. Therefore,
A method for determining the arousal level with a smaller number of beats has been developed, and a patent application has been separately filed (Japanese Patent Application No. 2-41730).
【０００４】前記開発に係る覚醒度の判定方法は、心拍
のＲ波間の拍間時間（R-RInterval:以下、ＲＲＩという
こともある。）の周波数スペクトルに、安静時に呼吸性
変動と血圧性変動とが現われるという事実と、ＲＲＩの
スペクトルの標準偏差の２乗の値である分散が、サンプ
ル数を少なくした場合、低周波の振動の影響を実質的に
受けない、という発明者の知見とに基づく。The method for determining the arousal level according to the above-mentioned development is based on the frequency spectrum of the inter-beat time (R-R Interval: hereinafter also referred to as RRI) between the R waves of the heartbeat, and the respiratory and blood pressure fluctuations at rest. And the finding that the variance, which is the square of the standard deviation of the RRI spectrum, is not substantially affected by low-frequency vibrations when the number of samples is small. Based on.
【０００５】横軸を心拍数の逆数である心拍周波数〔１
／beat〕で、また縦軸を心拍のＲＲＩの変動強度を示す
振幅比〔％／max 〕で示した、図１３乃至図１５のスペ
クトルを参照するに、図１３及び図１４には、図１５に
現われていないピークが現われている。すなわち、図１
３では、心拍周波数が０．２〔１／beat〕の近傍に、ま
た図１４では、０．２５〔１／beat〕の近傍にピークが
現われているのに対し、図１５にはこのようなピークは
現われていない。The horizontal axis represents the heartbeat frequency which is the reciprocal of the heartbeat rate [1
/ Beat], and the vertical axis represents the amplitude ratio [% / max] indicating the fluctuation intensity of the RRI of the heartbeat. Referring to the spectra of FIGS. 13 to 15, FIGS. There is a peak that does not appear in. That is, FIG.
In FIG. 3, a peak appears near the heart rate frequency of 0.2 [1 / beat], and in FIG. 14, near the peak of 0.25 [1 / beat], whereas in FIG. The peak has not appeared.
【０００６】前記ピークは、被測定者の心身の状態、す
なわち安静状態であるか、緊張状態であるかに関連し
て、現われたり、現われなかったりするもので、呼吸性
変動と呼ばれる。この呼吸性変動は、安静状態に現われ
るが、覚醒度の低下した状態は安静状態であるから、呼
吸性変動を検出すれば、覚醒度が低下しているか否かを
判定できる。The peak appears or does not appear in relation to the physical and mental condition of the subject, that is, whether the subject is in a resting state or a tense state, and is called respiratory variation. This respiratory variation appears in a resting state, but a state in which the degree of arousal is lowered is a resting state. Therefore, by detecting the respiratory variation, it is possible to determine whether or not the degree of arousal is lowering.
【０００７】図１４には、さらに、０．１〔１／beat〕
近傍にピークが現われている。このピークは血圧性変動
と呼ばれる。血圧性変動もまた、安静状態である覚醒度
の低下した状態のとき現われるが、これは呼吸性変動に
比べれば、現われ方が少ないことが確認されている。In FIG. 14, 0.1 [1 / beat] is further added.
A peak appears in the vicinity. This peak is called blood pressure fluctuation. It has been confirmed that blood pressure fluctuations also appear when the patient is in a resting state with low arousal level, but they appear less frequently than respiratory fluctuations.
【０００８】前記スペクトルは、図１６（Ａ）のように
図示的に表わすことができる。この図における呼吸性変
動の周波数ｆ1 を検出するのに、次の統計的手法を用い
る。変動の大きさの指標である分散ＲＲＶは、ＲＲＩを
データとすると、The spectrum can be graphically represented as shown in FIG. The following statistical method is used to detect the frequency f 1 of respiratory variation in this figure. The variance RRV, which is an index of the magnitude of fluctuation, has the RRI as data,
【０００９】[0009]
【数１】 [Equation 1]
【００１０】で表わされる。ここで、ｎはサンプル数、
ＲＲＩ* はｎ個のＲＲＩの平均値である。ＲＲＩデータ
には、血圧性変動に基づく低周波の振動と、呼吸に基づ
く高周波の振動とが混じっている。サンプル数ｎが多い
場合、図１７（Ａ）に示すようにｎ個のＲＲＩの平均値
ＲＲＩ* が、低周波振動の平均値となることから、低周
波の変動の影響が、分散の演算結果に混入する。これに
対して、サンプル数ｎが少ない場合、図１７（Ｂ）に示
すようにｎ個のＲＲＩの平均値ＲＲＩ* が、低周波振動
の振幅の影響を受けにくくなるため、低周波の変動の影
響が分散の演算結果に入らなくなり、図１７（Ｃ）に示
すような、あたかもハイパスフィルタを通した波形の分
散を求めたものと同じ結果になることが期待される。It is represented by Where n is the number of samples,
RRI * is the average value of n RRIs. The RRI data contains low-frequency vibrations due to blood pressure fluctuations and high-frequency vibrations due to respiration. When the number of samples n is large, the average value RRI * of n RRIs becomes the average value of low-frequency vibrations as shown in FIG. Mix in. On the other hand, when the sample number n is small, the average value RRI * of n RRIs is less likely to be affected by the amplitude of the low frequency vibration as shown in FIG. It is expected that the influence will not be included in the dispersion calculation result, and the result will be the same as that when the dispersion of the waveform passed through the high-pass filter as shown in FIG. 17C is obtained.
【００１１】そこで、模擬ＲＲＩデータをコンピュータ
で作成し、分散の演算サンプル数ｎを、３，４，１６と
変化させて特性を調べたところ、図１６（Ｂ）のように
なった。これは、サンプル数ｎに応じてカットオフ周波
数が変化するハイパスフィルタの特性と同じである。そ
して、それぞれのサンプル数における分散を図示したと
ころ、例えば、サンプル数３の分散は図１６（Ｃ）のよ
うになり、カットオフ周波数ｆ1 を抽出することが確認
できた。Then, simulated RRI data was created by a computer, and the characteristic was examined by changing the number n of calculation samples of dispersion to 3, 4, 16 and the result was as shown in FIG. 16 (B). This is the same as the characteristic of the high-pass filter in which the cutoff frequency changes according to the number of samples n. Then, when the variances of the respective sample numbers are illustrated, for example, the variance of the sample number 3 is as shown in FIG. 16C, and it was confirmed that the cutoff frequency f 1 was extracted.
【００１２】前記事実と知見とに基づいて開発された前
記覚醒度判定方法は、サンプル数を定めること、このサ
ンプル数による拍間時間（ＲＲＩ）の分散を求めるこ
と、分散に基づいて判定値を求めること、前記サンプル
数による拍間時間の走行中の分散を求めること、走行中
の分散と前記判定値とを比較することを含む。The awakening degree determination method developed based on the above facts and findings determines the number of samples, obtains the variance of inter-beat time (RRI) by the number of samples, and determines the determination value based on the variance. The steps include obtaining, calculating the running variance of the beat-to-beat time by the number of samples, and comparing the running variance with the determination value.
【００１３】この判定方法では、サンプル数を適当に定
めるが、呼吸性変動および（または）血圧性変動は、個
人差によって出現する周波数領域が多少ずれているた
め、覚醒度を正確に判定できない場合が生じうる。例え
ば、図１８（Ａ）に示すように、呼吸性変動のピークが
ｆ2 にあるとき、ｆ1 に対応するサンプル数ｎ1 を選定
してしまった場合、ｎ1 の分散ＲＲＶ（ｎ 1 ）は、同図
（Ｂ）の面積に比例した値となってしまい、呼吸性変動
をうまく抽出できない。次に、覚醒度が低下し、同図
（Ｃ）のように、呼吸性変動が増加したとき、サンプル
数ｎがずれていると、抽出した結果は同図（Ｄ）のよう
になり、結果として、ＲＲＶ（ｎ1 ）の値はわずかに増
加するだけである。そのため、実際には覚醒度が低下
し、ほぼ居眠り状態であっても、これを見逃してしまう
ことになる。In this determination method, the number of samples is appropriately determined.
However, respiratory and / or blood pressure fluctuations
The frequency range that appears due to human differences has shifted slightly
Therefore, it may happen that the arousal level cannot be accurately determined. example
For example, as shown in FIG. 18 (A), the peak of respiratory variation is
f2F1Number of samples corresponding to1Selected
If you do, n1Variance of RRV (n 1) Is the same figure
The value becomes proportional to the area of (B), and respiratory variation
Can not be extracted well. Next, the alertness decreased and the figure
As in (C), when respiratory variability increased, sample
If the number n is deviated, the extracted result is as shown in FIG.
And as a result, RRV (n1) Value is slightly increased
Just add. Therefore, the alertness actually decreases
And even if you're almost dozing, you'll miss this
Will be.
【００１４】これを解決するため、本出願人は特開平５
−４２１２９７号（特願平３−２２３５４９号）によ
り、検出した心拍数と呼吸数の周期の比からサンプル数
を求め、このサンプル数による拍間時間の分散に一定の
増加率を乗算して判定値を求め、その後の走行中のサン
プル数による拍間時間の分散を上記判定値と比較して覚
醒度を判定する装置を提案した。In order to solve this, the applicant of the present invention has disclosed Japanese Patent Laid-Open No.
No. 4221297 (Japanese Patent Application No. 3-223549), the number of samples is obtained from the ratio of the detected heart rate to the cycle of the respiratory rate, and the determination is made by multiplying the variance of the interbeat time by the number of samples by a certain increase rate. We proposed a device for determining the value and comparing the variance of the interbeat time depending on the number of samples during running with the above determination value to determine the arousal level.
【００１５】[0015]
【発明が解決しようとする課題】上記の従来装置では、
拍間時間の分散に一定の増加率を乗算して判定値として
いるため、運転者の個人差によって、覚醒状態であるに
も拘らず居眠りと判定したり、ほぼ居眠りしているにも
拘らず覚醒状態と判定したりする場合が生じ、正確に覚
醒度を判定できないという問題があった。In the above conventional device,
Since the variance is calculated by multiplying the variance of the beat-to-beat time by a certain rate of increase, it is determined by the driver's individual differences that he / she is dozing despite being awake, or almost dozing. In some cases, it may be determined that the person is awake, and the degree of awakening cannot be accurately determined.
【００１６】本発明は、上記の点に鑑みなされたもの
で、判定値を算出するための増加率を、増加率との関係
が明らかな個人の心拍数に応じて算出することにより、
被験者の個人差に拘らず精度良く覚醒度を判定できる覚
醒度判定装置を提供することを目的とする。The present invention has been made in view of the above points, and by calculating the increase rate for calculating the determination value according to the heart rate of an individual whose relationship with the increase rate is clear,
An object of the present invention is to provide a wakefulness determination device capable of accurately determining the wakefulness regardless of individual differences among test subjects.
【００１７】[0017]
【課題を解決するための手段】本発明は、図１に示す如
く、被験者の心拍数を検出する心拍数検出手段Ｍ１と、
上記被験者の呼吸数を検出する呼吸数検出手段Ｍ２と、
覚醒時に検出された心拍数と呼吸数とに基づいて覚醒時
のサンプル数を算出する覚醒時サンプル数算出手段Ｍ３
と、上記覚醒時のサンプル数による拍間時間の分散を算
出する覚醒時分散算出手段Ｍ４と、検出された心拍数に
応じて増加率を算出する増加率算出手段Ｍ５と、覚醒時
の分散に上記増加率を乗算して判定値を算出する判定値
算出手段Ｍ６と、運行中に検出された心拍数と呼吸数と
に基づいて運行中のサンプル数を算出する運行中サンプ
ル数算出手段Ｍ７と、上記運行中のサンプル数による拍
間時間の分散を算出する運行中分散算出手段Ｍ８と、上
記運行中の分散と上記判定値との比較により被験者の覚
醒度を判定する比較手段Ｍ９とを有する。The present invention, as shown in FIG. 1, includes heart rate detecting means M1 for detecting the heart rate of a subject,
Respiratory rate detection means M2 for detecting the respiratory rate of the subject,
Awakening sample number calculation means M3 for calculating the number of samples during awakening based on the heart rate and the respiratory rate detected during awakening
And an awakening time variance calculating means M4 for calculating the variance of the beat-to-beat time according to the number of samples during awakening, an increase rate calculating means M5 for calculating an increase rate according to the detected heart rate, and a variance during awakening. A judgment value calculating means M6 for calculating a judgment value by multiplying the increase rate, and a running sample number calculating means M7 for calculating the number of running samples based on the heart rate and the respiratory rate detected during running. And a dispersion calculating means M8 during operation for calculating the variance of the beat-to-beat time according to the number of samples during operation, and a comparing means M9 for determining the arousal level of the subject by comparing the dispersion during operation and the determination value. .
【００１８】[0018]
【作用】本発明においては、増加率との関係が明らかな
被験者の心拍数に基づいて増加率を算出し、この増加率
を覚醒時の分散に乗算して判定値を得るため、被験者に
応じた判定値を設定することができ、覚醒時の分散と居
眠り時の分散との変化率に個人差があっても、この個人
差の影響をなくし、精度良く覚醒度を判定できる。In the present invention, the rate of increase is calculated based on the heart rate of the subject whose relationship with the rate of increase is clear, and this rate of increase is multiplied by the variance at the time of awakening to obtain the judgment value. It is possible to set the judgment value, and even if there is an individual difference in the rate of change between the variance when awakening and the variance when asleep, it is possible to eliminate the influence of this individual difference and accurately determine the awakening degree.
【００１９】[0019]
【実施例】覚醒度判定装置は、心拍の拍間時間の呼吸性
変動を検出し、覚醒度を判定するものであって、図２に
示すように、被験者（例えば運転者）の心拍数を検出す
る手段１０（Ｍ１）と、被験者の呼吸数を検出する手段
１２（Ｍ２）と、これら手段から信号を受ける制御器１
４とを含む。覚醒度判定装置は、図示の実施例では、車
両に搭載され、運転者の覚醒度を判定している。EXAMPLE A wakefulness determination device detects respiratory fluctuations in the interbeat time of the heartbeat to determine the wakefulness. As shown in FIG. 2, the heart rate of the subject (for example, a driver) is determined. Means 10 (M1) for detecting, means 12 (M2) for detecting the respiration rate of the subject, and controller 1 receiving signals from these means
4 is included. In the illustrated embodiment, the awakening degree determination device is mounted on a vehicle and determines the awakening degree of the driver.
【００２０】図２に示す実施例では、心拍数を検出する
手段１０の出力は、アンプ１６で増幅され、フィルタ１
８によって雑音を除いた後、コンパレータ２０でパルス
信号に変換され、マイコンからなる制御器１４に入力し
ている。制御器１４は、覚醒度が低下したと判定する
と、ブザーその他の覚醒手段２２に信号を出力する。In the embodiment shown in FIG. 2, the output of the means 10 for detecting the heart rate is amplified by the amplifier 16 and the filter 1
After removing noise by 8, it is converted into a pulse signal by the comparator 20 and input to the controller 14 composed of a microcomputer. When the controller 14 determines that the awakening level has decreased, it outputs a signal to the buzzer and other awakening means 22.
【００２１】心拍数を検出する手段１０は、図示の実施
例では、発振器２４の出力の心臓で反射派（脈動）を出
力する超音波センサであり、シート２６に内蔵されてい
る。心拍数を検出する手段１０は、シート内蔵型の超音
波センサに限らず、公知の心拍または脈波検出手段を使
用できる。In the illustrated embodiment, the means 10 for detecting the heart rate is an ultrasonic sensor which outputs a reflection wave (pulsation) at the heart of the oscillator 24, and is incorporated in the seat 26. The means 10 for detecting the heart rate is not limited to the seat built-in type ultrasonic sensor, and a known heartbeat or pulse wave detecting means can be used.
【００２２】呼吸数を検出する手段１２は、シートベル
ト２８内に組み込まれた呼吸センサで、車両の走行中の
運転者の呼吸による胸の動きを計測して呼吸を計る。歪
ゲージ、圧力センサなどにより実現できる。図示の実施
例では、さらに、連続式血圧センサ３０が設けられてい
る。これは、車両のインストルメントパネルなどに取り
付けるもので、運転者が指先を挿入することによって、
連続的な血圧変化を計測できる。The breathing rate detecting means 12 is a breathing sensor incorporated in the seat belt 28, and measures the movement of the chest due to the breathing of the driver while the vehicle is running to measure the breathing. It can be realized by strain gauges, pressure sensors, etc. In the illustrated embodiment, a continuous blood pressure sensor 30 is further provided. This is to be attached to the instrument panel of the vehicle, etc.By inserting the fingertip of the driver,
Can measure continuous blood pressure changes.
【００２３】制御器１４は、図３に示すような制御をす
る。初期化（１００）の後、心拍数検出手段１０および
呼吸数検出手段１２から、心拍数と呼吸数との初期値を
入力し（１０１）、検出した心拍数と検出した呼吸数と
に基づいてサンプル数ｎ1 を求める（１０２）。The controller 14 controls as shown in FIG. After initialization (100), initial values of the heart rate and the respiratory rate are input from the heart rate detecting means 10 and the respiratory rate detecting means 12 (101), and based on the detected heart rate and the detected respiratory rate. The number of samples n 1 is calculated (102).
【００２４】既に述べたように、呼吸性変動は心拍数と
呼吸数との比に対応した周波数に現われる。図４
（Ａ），（Ｂ）に示すように心拍および呼吸がそれぞれ
変動しているとき、心拍の１周期の時間（波長）を
λ1 、呼吸のそれをλ2 とすれば、 呼吸性変動の周期＝λ2 ／λ1 〔beat〕 （呼吸性変動の周波数＝λ1 ／λ2 〔１／beat〕）…（２） にピークが現われる。そこで、 ｎ1 ＝（λ2 ／λ1 ）ｋ …（３） のように、サンプル数ｎ1 を設定する。ここで、ｋは、
定数であって、例えば、０．８程度に定める。また、サ
ンプル数ｎ1 は整数でなければならないから、小数部は
四捨五入する。As already mentioned, respiratory fluctuations appear at frequencies corresponding to the ratio of heart rate to respiratory rate. FIG.
As shown in (A) and (B), when the heartbeat and respiration are changing, if the time (wavelength) of one cycle of the heartbeat is λ 1 and that of the respiration is λ 2 , the respiratory fluctuation cycle = Λ 2 / λ 1 [beat] (frequency of respiratory fluctuation = λ 1 / λ 2 [1 / beat]) (2) A peak appears. Therefore, the number of samples n 1 is set as in n 1 = (λ 2 / λ 1 ) k (3). Where k is
It is a constant and is set to about 0.8, for example. Also, since the sample number n 1 must be an integer, the decimal part is rounded off.
【００２５】サンプル数ｎ1 を求めた後、（１）式によ
り、サンプル数ｎ1 による拍間時間の分散つまり呼吸性
変動による分散ＲＲＶ（ｎ1 ）を求め（１０３）、この
分散に基づいて判定値を求める（１０４）。車両の走行
中、心拍数の検出手段１０によって心拍数を検出し、ま
た呼吸数の検出手段１２によって呼吸数を検出し、それ
ぞれを入力する（１０５）。そして（３）式により、走
行中の心拍数と呼吸数とに基づいて走行中のサンプル数
を求め（１０６）、新たなサンプル数ｎ1 による拍間時
間の走行中のＲＲＶ（ｎ1 ）分散を求める（１０７）。After obtaining the number of samples n 1 , the variance of the interbeat time according to the number of samples n 1, that is, the variance RRV (n 1 ) due to the respiratory variation is obtained from the equation (1) (103), and based on this variance A judgment value is obtained (104). While the vehicle is traveling, the heart rate detecting means 10 detects the heart rate, and the respiration rate detecting means 12 detects the respiration rate, and inputs each of them (105). Then, the number of running samples is calculated based on the running heart rate and the breathing rate by the equation (3) (106), and the RRV (n 1 ) variance during running of the inter-beat time based on the new sample number n 1 Is calculated (107).
【００２６】判定値と直前に求めた分散とを比較し（１
０８）、直前の分散が判定値以下であるとき、心拍数と
呼吸数との入力、サンプル数の演算などを、心拍の１拍
づつ移動させて繰り返す（１０５〜１０８）。そして、
直前の分散が判定値より大きくなったとき、覚醒手段２
２に信号を出力し（１０９）、運転者を覚醒させる。上
記の１０２が覚醒時サンプル数算出手段Ｍ３に対応し、
１０３が覚醒時分散算出手段Ｍ４に対応し、１０４ａ，
１０４ｂが増加率算出手段Ｍ５に対応し、１０４ｃが判
定値算出手段Ｍ６に対応し、１０６が運転中サンプル数
算出手段Ｍ７に対応し、１０７が運行中分散算出手段Ｍ
８に対応し、１０８が比較手段Ｍ９に対応する。The judgment value is compared with the variance obtained immediately before (1
08), when the immediately preceding variance is less than or equal to the determination value, the input of the heart rate and the respiration rate, the calculation of the number of samples, and the like are repeated by moving one beat of the heart beat (105 to 108). And
When the immediately preceding variance becomes larger than the judgment value, the awakening means 2
A signal is output to 2 (109) to awaken the driver. The above 102 corresponds to the awakening sample number calculation means M3,
103 corresponds to the awakening variance calculation means M4, and 104a,
104b corresponds to the increase rate calculating means M5, 104c corresponds to the judgment value calculating means M6, 106 corresponds to the operating sample number calculating means M7, and 107 is the operating variance calculating means M.
8, and 108 corresponds to the comparison means M9.
【００２７】ここで、通常の運転者がリラックスした覚
醒状態で車両を運転している時、毎分の心拍数ＨＲは６
０〜９０（拍／分）程度であり、かなりの個人差があ
る。長時間運転などで居眠り状態となったとき、上記の
呼吸性変動による分散ＲＲＶ（ｎ1 ）は増加するが、上
記ＲＲＶ（ｎ1 ）の増加率は図５に示す如く覚醒時の心
拍数の低い運転者の方が覚醒時の心拍数の高い運転者に
比べて明らかに高くなる。つまり心拍間隔の長い運転者
のバラツキは変化しやすいことを示している。Here, when the normal driver is driving the vehicle in a relaxed and awake state, the heart rate HR per minute is 6
It is about 0 to 90 (beats / minute), and there are considerable individual differences. When the person falls asleep for a long time, the variance RRV (n 1 ) due to the respiratory fluctuation increases, but the rate of increase of the RRV (n 1 ) is as shown in FIG. A driver with low heart rate is clearly higher than a driver with a high heart rate during awakening. That is, it shows that the variation of the driver with a long heartbeat interval is likely to change.
【００２８】このため、１０４の判定値設定のステップ
では図６に示す処理を行う。まず、拍間時間の平均値Ｒ
ＲＩ* で６０を割算し毎分の心拍数ＨＲを算出する（１
０４ａ）。次に、この心拍数ＨＲを用いて図７に示すマ
ップを参照して増加率α1 を設定する（１０４ｂ）。図
７のマップは図５に基づいて予め設定されたもので、覚
醒時の心拍数が低いとき増加率α1 は大となり、心拍数
が高いとき増加率α1は小となる。なお、α1 は例えば
１．５〜１３．０程度の値である。この後、分散ＲＲＶ
（ｎ1 ）に増加率α1 を乗算して判定値とする（１０４
ｃ）。Therefore, the process shown in FIG. 6 is performed in the step of setting the judgment value 104. First, the average value of the time between beats R
Divide 60 by RI * to calculate the heart rate HR per minute (1
04a). Next, using this heart rate HR, the increase rate α 1 is set with reference to the map shown in FIG. 7 (104b). The map of FIG. 7 is preset based on FIG. 5, and the increase rate α 1 is large when the heart rate during awakening is low, and the increase rate α 1 is small when the heart rate is high. Note that α 1 has a value of, for example, about 1.5 to 13.0. After this, distributed RRV
(N 1 ) is multiplied by the increase rate α 1 to obtain a determination value (104
c).
【００２９】このように、増加率との関係が明らかな被
験者の心拍数に基づいて増加率α1を算出し、この増加
率を覚醒時の分散に乗算して判定値を得るため、被験者
に応じた判定値を設定することができ、覚醒時の分散と
居眠り時の分散との変化率に個人差があっても、この個
人差の影響をなくし、精度良く覚醒度を判定できる。In this way, the rate of increase α 1 is calculated based on the heart rate of the subject whose relationship with the rate of increase is clear, and this rate of increase is multiplied by the variance at the time of awakening to obtain the judgment value. It is possible to set a determination value according to it, and even if there is an individual difference in the rate of change between the variance when awake and the variance when asleep, it is possible to eliminate the effect of this individual difference and accurately determine the awakening degree.
【００３０】図８に示す実施例では、呼吸性変動と血圧
性変動とを検出し、覚醒度を判定している。初期化（１
１０）の後、心拍数検出手段１０から心拍数の初期値を
入力し（１１１）、呼吸数検出手段１２および血圧セン
サ３０からそれぞれ、呼吸数と血圧とを入力し（１１
２）、検出した心拍数と検出した呼吸数とに基づいてサ
ンプル数ｎ1 を、また検出した心拍数と検出した血圧と
に基づいてサンプル数ｎ 2 を求める（１１３）。In the embodiment shown in FIG. 8, respiratory variation and blood pressure are
Sexual fluctuations are detected to determine the degree of arousal. Initialization (1
After 10), the initial value of the heart rate is obtained from the heart rate detecting means 10.
Input (111), breath rate detection means 12 and blood pressure sensor
Respiratory rate and blood pressure are entered from the service 30 (11
2), based on the detected heart rate and detected respiratory rate
Number of samples n1And the detected heart rate and detected blood pressure
Number of samples based on 2Is calculated (113).
【００３１】ここで、サンプル数ｎ1 は、（３）式から
求めることができる。血圧の大きなうねりは、１分当り
５〜７回程度であり、このうねりによる血圧性変動は、
心拍と血圧のうねりとの比に対応した周波数に現われ
る。図４（Ａ），（Ｃ）に示すように、心拍および血圧
がそれぞれ変動しているとき、心拍の１周期の時間（波
長）をλ1 、血圧のうねりのそれをλ3 とすれば、 血圧性変動の周期＝λ3 ／λ1 〔beat〕 （血圧性変動の周波数＝λ1 ／λ3 〔１／〔beat〕） …（４） にピークが現われる。そこで、 ｎ2 ＝（λ3 ／λ1 ）ｋ …（５） のように、サンプル数ｎ2 を設定する。ここで、ｋはサ
ンプル数ｎ1 と同じ定数であり、サンプル数ｎ2 も整数
である。そして、サンプル数ｎ2 はサンプル数ｎ 1 より
大きい。Here, the number of samples n1Is from equation (3)
You can ask. Large swell of blood pressure per minute
It is about 5 to 7 times, and blood pressure fluctuation due to this swell is
Appears at a frequency that corresponds to the ratio of heartbeat to blood pressure swell
It As shown in FIGS. 4A and 4C, heartbeat and blood pressure
, Respectively, the time of one cycle of the heartbeat (wave
Length) to λ1Λ that of the swell of blood pressure3Then, the period of blood pressure fluctuation = λ3/ Λ1[Beat] (Frequency of blood pressure fluctuation = λ1/ Λ3[1 / [beat]) ... A peak appears at (4). Therefore, n2= (Λ3/ Λ1) K ... (5), the number of samples n2To set. Where k is the
Number of samples n1Is the same constant as the number of samples n2Is also an integer
Is. And the number of samples n2Is the number of samples n 1Than
large.
【００３２】サンプル数ｎ1 ，ｎ2 を求めた（１）式に
より、サンプル数ｎ1 による拍間時間の分散ＲＲＶ（ｎ
1 ）を求め（１１４）、サンプル数ｎ2 による拍間時間
の分散ＲＲＶ（ｎ2 ）を求める（１１５）。そして、サ
ンプル数ｎ2 による分散からサンプル数ｎ1 による分散
を引いて差の分散ＲＲＶＢを求める（１１６）。[0032] The number of samples n 1, n 2 was determined (1), between beats by sample number n 1 time dispersion RRV (n
1 ) is obtained (114), and the inter-beat time variance RRV (n 2 ) according to the number of samples n 2 is obtained (115). Then, the variance RRVB of the difference is obtained by subtracting the variance of the sample number n 1 from the variance of the sample number n 2 (116).
【００３３】差の分散ＲＲＶＢの特性は、図９（Ａ）に
示すようなものとなり、これを図示すると、同図（Ｂ）
となる。すなわち、差の分散では、サンプル数ｎ2 のカ
ットオフ周波数ｆ2 を抽出することが分る。サンプル数
ｎ1 ，ｎ2 の分散と、差の分散とを求めた後、サンプル
数ｎ1 の分散に基づいて判定値を求める（１１７）。The characteristic of the difference variance RRVB is as shown in FIG. 9 (A).
Becomes That is, in the dispersion of the differences, it can be seen that extracts a cutoff frequency f 2 of the sample number n 2. After obtaining the variance of the sample numbers n 1 and n 2 and the variance of the difference, a determination value is obtained based on the variance of the sample number n 1 (117).
【００３４】車両の走行中、心拍数の検出手段１０によ
って心拍数を検出し、また呼吸数の検出手段１２によっ
て呼吸数を検出し、それぞれを入力する（１１８）。そ
して、（３）式により、走行中の心拍数と呼吸数とに基
づいて走行中のサンプル数を求め（１１９）、新たなサ
ンプル数ｎ1 による拍間時間の走行中の分散ＲＲＶ（ｎ
1 ）を求める（１２０）。ここで、血圧の大きなうねり
により定まるサンプル数ｎ2 は、当初に設定したままで
あるため、それによる分散ＲＲＶ（ｎ2 ）には変化がな
いが、サンプル数ｎ1 による分散が異なっていることか
ら、走行中の血圧性変動による差の分散ＲＲＶＢを求め
る。While the vehicle is traveling, the heart rate detecting means 10 detects the heart rate, and the respiration rate detecting means 12 detects the respiration rate, which are input (118). Then, the number of running samples is calculated based on the running heart rate and the respiration rate by the equation (3) (119), and the variance RRV (n during running of the inter-beat time based on the new sample number n 1 is calculated.
1 ) is calculated (120). Here, since the number of samples n 2 determined by the large swell of blood pressure remains set at the beginning, there is no change in the variance RRV (n 2 ) by that, but the variance by the number of samples n 1 is different. From this, the variance RRVB of the difference due to the blood pressure fluctuation during traveling is obtained.
【００３５】判定値と直前に求めた分散ＲＲＶ（ｎ1 ）
とを比較し（１２１）、直前の分散が判定値以下である
とき、心拍数と呼吸数との入力、サンプル数ｎ1 の演算
などを、心拍の１拍づつ移動させて繰り返す（１１８〜
１２１）。直前の分散ＲＲＶ（ｎ1 ）が判定値より大き
くなったとき、差の分散ＲＲＶＢと判定値とを比較し
（１２２）、差の分散が判定値以下であるとき、心拍数
と呼吸数との入力、サンプル数ｎ1 の演算などを、心拍
数の１拍づつ移動させて繰り返す（１１８〜１２２）。
そして、差の分散が判定値より大きくなったとき、覚醒
手段２２に信号を出力し（１２３）、運転者を覚醒させ
る。The judgment value and the variance RRV (n 1 ) obtained immediately before
(121) and when the immediately preceding variance is less than or equal to the determination value, the input of the heart rate and the respiratory rate, the calculation of the sample number n 1 and the like are repeated by moving one beat of the heart beat (118-
121). When the immediately preceding variance RRV (n 1 ) becomes larger than the determination value, the difference variance RRVB is compared with the determination value (122). When the difference variance is less than or equal to the determination value, the heart rate and the respiratory rate are The input, the calculation of the sample number n 1 and the like are repeated by moving one beat of the heart rate (118 to 122).
When the variance of the difference becomes larger than the determination value, a signal is output to the awakening means 22 (123) to awaken the driver.
【００３６】上記の１１３がが覚醒時サンプル数算出手
段Ｍ３に対応し、１１４，１１５が覚醒時分散手段Ｍ４
に対応し、１１７ａ，１１７ｂが増加率算出手段Ｍ５に
対応し、１１７ｃ，１１７ｄが判定値算出手段Ｍ６に対
応し、１１９が運行中サンプル数算出手段Ｍ７に対応
し、１２０が運行中分散算出手段Ｍ８に対応し、１２
１，１２２が比較手段Ｍ９に対応する。Reference numeral 113 corresponds to the awakening sample number calculation means M3, and 114 and 115 are awakening dispersion means M4.
117a and 117b correspond to the increase rate calculation means M5, 117c and 117d correspond to the judgment value calculation means M6, 119 corresponds to the running sample number calculation means M7, and 120 is the running variance calculation means. 12 corresponding to M8
1, 122 correspond to the comparison means M9.
【００３７】ここで、長時間運転などの居眠り状態とな
ったとき、上記の呼吸性変動による分散ＲＲＶ（ｎ1 ）
及び血圧性変動による差の分散ＲＲＶＢ夫々は増加する
が、上記ＲＲＶ（ｎ1 ），ＲＲＶＢ夫々の増加率は共に
図５に示す如く覚醒時の心拍数の低い運転者の方が覚醒
時の心拍数の高い運転者に比べて明らかに高くなる。つ
まり心拍間隔の長い運転者のバラツキは変化しやすいこ
とを示している。Here, when the vehicle is in a dozing state such as driving for a long time, the distributed RRV (n 1 ) due to the above-mentioned respiratory fluctuation.
And the variance RRVB of the difference due to the blood pressure fluctuation increases, but the rate of increase of each of RRV (n 1 ) and RRVB is as shown in FIG. It will be significantly higher than the higher number of drivers. That is, it shows that the variation of the driver with a long heartbeat interval is likely to change.
【００３８】このため、１１７の判定値設定のステップ
では図１０に示す処理を行う。まず、拍間時間の平均値
ＲＲＩ* で６０を割算し毎分の心拍数ＨＲを算出する
（１１７ａ）。次に、この心拍数ＨＲを用いて図７に示
すマップを参照して増加率α1，α2 を設定する（１１
７ｂ）。図７のマップは図５に基づいて予め設定された
もので覚醒時の心拍数が低いとき増加率α1 ，α2 夫々
は大となり、心拍数が高いとき増加率α1 ，α2 は小と
なる。勿論、α1 ，α2 は異なる値である。この後、分
散ＲＲＶ（ｎ1 ）に増加率α1 を乗算して分散の判定値
とし（１１７ｃ）、差の分散ＲＲＶＢに増加率α2 を乗
算して差の分散の判定値とする（１１７ｄ）。Therefore, the processing shown in FIG. 10 is performed in the step 117 of setting the judgment value. First, 60 is divided by the average value RRI * between beats to calculate the heart rate HR per minute (117a). Next, using the heart rate HR, the increase rates α 1 and α 2 are set with reference to the map shown in FIG. 7 (11
7b). The map in FIG. 7 is preset based on FIG. 5, and the increase rates α 1 and α 2 are large when the heart rate during awakening is low, and the increase rates α 1 and α 2 are small when the heart rate is high. Becomes Of course, α 1 and α 2 have different values. Thereafter, the variance RRV (n 1 ) is multiplied by the increase rate α 1 to obtain the variance determination value (117c), and the difference variance RRVB is multiplied by the increase rate α 2 to obtain the difference variance determination value (117d). ).
【００３９】図１１（Ａ），（Ｂ），（Ｃ）夫々は覚醒
時の心拍数が低い運転者の走行時における高い自己申告
覚醒状態、血圧性変動による差の分散ＲＲＶＢ、心拍数
夫々の時間変化を示す。この運転者の場合、ＲＲＶＢの
変動が大きく、居眠り状態ではＲＲＶＢの値がかなり大
きくなるが、図７のマップから得られるα2 の値が大き
いために、覚醒時のＲＲＶＢの値ａ2 に対してＲＲＶＢ
の判定値ｂ2 （ａ2 ×α2 ）が大きくなり、算出された
ＲＲＶＢが判定値ｂ2 を越え、居眠りと判定される時点
が自己申告とほとんど重なり、正確な覚醒度の判定を行
うことができる。11 (A), (B), and (C) respectively show a high self-reported awakening state when the driver has a low heart rate during awakening, a variance RRVB of differences due to blood pressure fluctuation, and a heart rate respectively. It shows the change over time. In this driver, large fluctuations in RRVB, becomes considerably large value of RRVB the doze state, due to the large alpha 2 values obtained from the map of FIG. 7, with respect to the value a 2 of RRVB awake RRVB
Judgment value b 2 (a 2 × α 2 ) becomes larger, the calculated RRVB exceeds the judgment value b 2 , and the time when it is judged to be dozing almost overlaps with the self-report, and the awakening degree can be accurately judged. You can
【００４０】図１２（Ａ），（Ｂ），（Ｃ）夫々は覚醒
時の心拍数が高い運転者の走行時における自己申告覚醒
状態、血圧性変動による差の分散ＲＲＶＢ、心拍数夫々
の時間変化を示す。この運転者の場合、ＲＲＶＢの変動
が小さく、居眠り状態でもＲＥＶＢの値はそれほど大き
くならないが、図７のマップから得られるα2 の値が小
さいために、覚醒時のＲＲＶＢの値ａ1 に対してＲＲＶ
Ｂの判定値ｂ1 （ａ1×α2 ）がそれほど大きくなら
ず、算出されたＲＲＶＢが判定値ｂ1 を越え、居眠り判
定される時点が自己申告とほとんど重なり、正確な覚醒
度の判定を行うことができる。FIGS. 12A, 12B, and 12C are self-reported awake states when a driver with a high heart rate during awakening, variance RRVB of difference due to blood pressure fluctuation, and time of heart rate, respectively. Show changes. In the case of this driver, the fluctuation of RRVB is small, and the value of REVB does not become so large even in the state of dozing. However, since the value of α 2 obtained from the map of FIG. 7 is small, the value of a1 of RRVB at the time of awakening is smaller than the value a 1. RRV
The judgment value b 1 (a 1 × α 2 ) of B does not become so large, the calculated RRVB exceeds the judgment value b 1, and the time when the drowsiness judgment is made almost overlaps with the self-report, and the accurate judgment of the awakening degree is made. It can be carried out.
【００４１】[0041]
【発明の効果】上述の如く、本発明によれば、増加率と
の関係が明らかな被験者の心拍数に基づいて増加率を算
出し、この増加率を覚醒時の分散に乗算して判定値を得
るため、被験者に応じた判定値を設定することができ、
覚醒時の分散と居眠り時の分散との変化率に個人差があ
っても、この個人差の影響をなくし、精度良く覚醒度を
判定でき、実用上きわめて有用である。As described above, according to the present invention, the rate of increase is calculated based on the heart rate of the subject whose relationship with the rate of increase is clear, and this rate of increase is multiplied by the variance at the time of awakening to obtain a judgment value. In order to obtain, you can set the judgment value according to the subject,
Even if there is an individual difference in the rate of change between the variance when awake and the variance when asleep, it is possible to eliminate the effect of this individual difference and accurately determine the arousal level, which is extremely useful in practice.
【図１】本発明の原理図である。FIG. 1 is a principle diagram of the present invention.
【図２】本発明の装置の概略図である。FIG. 2 is a schematic diagram of the device of the present invention.
【図３】本発明の覚醒度判定処理のフローチャートであ
る。FIG. 3 is a flowchart of awakening degree determination processing of the present invention.
【図４】心拍、呼吸数、血圧の周期的な変動を示す図で
ある。FIG. 4 is a diagram showing periodic fluctuations in heart rate, respiratory rate, and blood pressure.
【図５】覚醒時心拍数とＲＲＶ（ｎ1 ），ＲＲＶＢの増
加率との関係を示す図である。FIG. 5 is a diagram showing a relationship between an awake heart rate and an increase rate of RRV (n 1 ) and RRVB.
【図６】判定値設定処理のフローチャートである。FIG. 6 is a flowchart of a judgment value setting process.
【図７】α1 ，α2 のマップを示す図である。FIG. 7 is a diagram showing maps of α 1 and α 2 .
【図８】本発明の覚醒度判定処理のフローチャートであ
る。FIG. 8 is a flowchart of awakening degree determination processing of the present invention.
【図９】呼吸性変動と血圧性変動が表われる場合の特性
図である。FIG. 9 is a characteristic diagram when respiratory changes and blood pressure changes appear.
【図１０】判定値設定処理のフローチャートである。FIG. 10 is a flowchart of determination value setting processing.
【図１１】覚醒時心拍数の低い人の自己申告覚醒度、Ｒ
ＲＶＢ、心拍数の時間変化を示す図である。FIG. 11: Self-reported arousal level of a person with a low awake heart rate, R
It is a figure which shows RVB and the time change of a heart rate.
【図１２】覚醒時心拍数の高い人の自己申告覚醒度、Ｒ
ＲＶＢ、心拍数の時間変化を示す図である。[Fig. 12] Self-reported arousal level of a person with a high wakeful heart rate, R
It is a figure which shows RVB and the time change of a heart rate.
【図１３】安静時のＲＲＩ変動強度の周波数スヘクトル
図である。FIG. 13 is a frequency spectrum diagram of RRI fluctuation intensity at rest.
【図１４】安静時のＲＲＩ変動強度の周波数スヘクトル
図である。FIG. 14 is a frequency spectrum diagram of RRI fluctuation intensity at rest.
【図１５】緊張時のＲＲＩ変動強度の周波数スへクトル
図である。FIG. 15 is a frequency spectrum diagram of RRI fluctuation strength during tension.
【図１６】呼吸性変動の抽出原理を説明するための図で
ある。FIG. 16 is a diagram for explaining the principle of extracting respiratory variation.
【図１７】呼吸性変動の抽出原理を説明するための図で
ある。FIG. 17 is a diagram for explaining the principle of extracting respiratory variation.
【図１８】先願における判定誤差を説明するための図で
ある。FIG. 18 is a diagram for explaining a determination error in the prior application.
１０ 心拍検出手段 １２ 呼吸数検出手段 １４ 制御器 ２２ 覚醒手段 ３０ 血圧センサ Ｍ１ 心拍数検出手段 Ｍ２ 呼吸数検出手段 Ｍ３ 覚醒時サンプル数算出手段 Ｍ４ 覚醒時分散算出手段 Ｍ５ 増加率算出手段 Ｍ６ 判定値算出手段 Ｍ７ 運行中サンプル数算出手段 Ｍ８ 運行中分散手段 Ｍ９ 比較手段 10 Heartbeat detecting means 12 Respiratory rate detecting means 14 Controller 22 Awakening means 30 Blood pressure sensor M1 Heart rate detecting means M2 Respiratory rate detecting means M3 Awakening sample number calculating means M4 Awakening variance calculating means M5 Increase rate calculating means M6 Judgment value calculation Means M7 Running sample number calculation means M8 Running dispersion means M9 Comparison means
Claims (1)
段と、 上記被験者の呼吸数を検出する呼吸数検出手段と、 覚醒時に検出された心拍数と呼吸数とに基づいて覚醒時
のサンプル数を算出する覚醒時サンプル数算出手段と、 上記覚醒時のサンプル数による拍間時間の分散を算出す
る覚醒時分散算出手段と、 検出された心拍数に応じて増加率を算出する増加率算出
手段と、 覚醒時の分散に上記増加率を乗算して判定値を算出する
判定値算出手段と、 運行中に検出された心拍数と呼吸数とに基づいて運行中
のサンプル数を算出する運行中サンプル数算出手段と、 上記運行中のサンプル数による拍間時間の分散を算出す
る運行中分散算出手段と、 上記運行中の分散と上記判定値との比較により被験者の
覚醒度を判定する比較手段とを有することを特徴とする
覚醒度判定装置。1. A heart rate detecting means for detecting a heart rate of a subject, a respiration rate detecting means for detecting a respiration rate of the subject, and a sample at the time of awakening based on the heart rate and the respiration rate detected at the time of awakening. Awakening sample number calculating means for calculating the number, awakening time variance calculating means for calculating the variance of the beat-to-beat time by the number of samples during the awakening, increase rate calculation for calculating an increase rate according to the detected heart rate Means, a judgment value calculating means for calculating a judgment value by multiplying the variance at the time of awakening by the above-mentioned increase rate, and an operation for calculating the number of samples during operation based on the heart rate and the respiratory rate detected during operation Medium sample number calculation means, in-operation variance calculation means for calculating the variance of the beat-to-beat time by the number of samples in operation, and comparison of determining the arousal level of the subject by comparing the in-operation dispersion and the determination value Having means Awareness determination apparatus according to claim.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP32887194A JPH08182667A (en) | 1994-12-28 | 1994-12-28 | Awakening-degree judgement device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP32887194A JPH08182667A (en) | 1994-12-28 | 1994-12-28 | Awakening-degree judgement device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH08182667A true JPH08182667A (en) | 1996-07-16 |
Family
ID=18215031
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP32887194A Pending JPH08182667A (en) | 1994-12-28 | 1994-12-28 | Awakening-degree judgement device |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH08182667A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005342188A (en) * | 2004-06-02 | 2005-12-15 | Delta Tooling Co Ltd | System for deciding mental and physical condition |
| JP2007195615A (en) * | 2006-01-24 | 2007-08-09 | Toyota Motor Corp | System and device for vigilance estimation |
| EP2441387A1 (en) | 2009-06-08 | 2012-04-18 | Nagoya City University | Sleepiness assessment device |
| WO2020255514A1 (en) * | 2019-06-18 | 2020-12-24 | 株式会社デンソー | State estimation device and state estimation method |
-
1994
- 1994-12-28 JP JP32887194A patent/JPH08182667A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005342188A (en) * | 2004-06-02 | 2005-12-15 | Delta Tooling Co Ltd | System for deciding mental and physical condition |
| JP4502712B2 (en) * | 2004-06-02 | 2010-07-14 | 株式会社デルタツーリング | Psychosomatic state judgment system |
| JP2007195615A (en) * | 2006-01-24 | 2007-08-09 | Toyota Motor Corp | System and device for vigilance estimation |
| EP2441387A1 (en) | 2009-06-08 | 2012-04-18 | Nagoya City University | Sleepiness assessment device |
| JPWO2010143535A1 (en) * | 2009-06-08 | 2012-11-22 | 公立大学法人名古屋市立大学 | Sleepiness determination device |
| EP2441387A4 (en) * | 2009-06-08 | 2014-12-31 | Univ Nagoya City | Sleepiness assessment device |
| US8979761B2 (en) | 2009-06-08 | 2015-03-17 | Nagoya City University | Sleepiness assessment apparatus |
| JP5704612B2 (en) * | 2009-06-08 | 2015-04-22 | 公立大学法人名古屋市立大学 | Sleepiness determination device |
| WO2020255514A1 (en) * | 2019-06-18 | 2020-12-24 | 株式会社デンソー | State estimation device and state estimation method |
| JP2020202986A (en) * | 2019-06-18 | 2020-12-24 | 株式会社デンソー | State estimation apparatus and state estimation method |
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