Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning

Chen, Lan-lan; Zhao, Yu; Zhang, Jian; Zou, Junzhong

doi:10.1016/j.eswa.2015.05.028

Cited by 160 publications

(87 citation statements)

References 53 publications

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“…This decline in activity has been demonstrated by several studies [17]. However, the decrease in β activity in case of driver drowsiness must be qualified following the work of [18]. Indeed, their study has shown that the decrease in alertness can be accompanied by an increase in β activity due to an increase in concentration to try to compensate for the appearance of driver drowsiness.…”

Section: Manifestations Of Driver Drowsiness In the Eegmentioning

confidence: 99%

“…The EOG has therefore naturally been subject to automatic driver drowsiness detection systems. Authors of [37] proposed a vigilance level detection system based on blink duration and frequency parameters as well as amplitude-velocity ratio. The average level of these variables is learned at the beginning of the recording, the system detects blinks that are too far away from these levels.…”

Section: Driver Drowsiness Detection Systems By Blinking Analysismentioning

confidence: 99%

“…The β activity corresponds to the frequency band [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz. It is present on both sides of the brain symmetrically.…”

Section: Betamentioning

confidence: 99%

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Analysis of Driver Drowsiness Detection using EEG and EOG

Firke¹,

Jain²

2018

IJET

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This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.

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Section: Manifestations Of Driver Drowsiness In the Eegmentioning

confidence: 99%

Section: Driver Drowsiness Detection Systems By Blinking Analysismentioning

confidence: 99%

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Analysis of Driver Drowsiness Detection using EEG and EOG

Firke¹,

Jain²

2018

IJET

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“…Basit matematiksel fonksiyonlar ile başarılı bir şekilde çıktı parametrelerini modelleyebilen doğrusal regresyon analizinin endüstride oldukça geniş bir kullanım alanı vardır. Örneğin, müşteri taleplerini [2], baca gazı CO2 emisyonunu [3], iyonik sıvıların termodinamik özeliklerini [4], çeşitli biyodizellerin kullanıldığı sıkıştırmalı ateşlemeli motorun gürültü ve ses özeliklerini [5], Alzheimer hastalığının ilerleme sürecini [6], binalarda elektrik tüketimini [7], nano-silika içeren yüksek dayanımlı betonun basınç dayanımını [8]tahmin etmede oldukça başarılıdır. Literatürdeki çalışmalar dikkate alındığında, doğrusal regresyonun, yeterince yüksek ilişkililik katsayına sahip olması bakımından, birçok sistem veya ürün özeliklerini tahmin etmede yeterli olduğu sonucuna varılabilir [9].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Polimerik Atıklar İçeren Betonun Ultrason Hızının ve Dinamik Elastisite Modülünün Çok Yanıtlı Eniyilenmesi

Şimşek¹,

Uygunoğlu²

2017

Journal of Polytechnic

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Polimerik atıklar içeren betonun ultrason hızının ve dinamik elastisite modülünün çok yanıtlı eniyilenmesi Politeknik Dergisi, 2017; 20 (4) : 1009-1017 Journal of Polytechnic, 2017; 20 (4) ÖZ Bu çalışmada polimerik atıklar içeren betonun ultrason hızı ve elastisite modülü özelikleri üzerindeki etken etkileri çok değişkenli regresyon çözümlemesi ile çözümlendi. Her biri üç seviyeye sahip yedi polimer; yüksek yoğunluklu polietilen, düşük yoğunluklu polietilen, polipropilen, termoplastik elastomer, dimetil teraftalat, polietilen teraftalat, polietilen naftalat ve deneylerin tasarımında (L27) ortogonal dizini seçildi. Ultrason hızı üzerinde azaltıcı etkiye sahip polimerler sırası ile 0.000, 0.009, 0.007, 0.008 ve 0.001 p-değerleri ile yüksek yoğunluklu polietilen, düşük yoğunluklu polietilen, termoplastik elastomer, polietilen teraftalat ve polietilen naftalat olarak belirlendi. Dinamik elastisite modülü üzerinde azaltıcı etkiye sahip polimerler ise, sırasıyla,0,001, 0.002 ve 0.001 p-değerleri ile yüksek yoğunluklu polietilen, polipropilen ve polietilen naftalat olarak belirlendi. Polimerik atıklar içeren betonun optimal karışım oranları doğrusal regresyon modelleri tabanlı çok yanıtlı doğrusal programlama yöntemiyle belirlendi. Optimal karışım oranları ile gerçekleştirilen doğrulama deneyi, çok yanıtlı doğrusal programlama yönteminin beton bileşimi eniyileme problemlerini çözmede etkili biçimde kullanılabileceğini göstermektedir.Anahtar Kelimeler: Beton, regresyon analizi, çok yanıtlı doğrusal programlama, taguchi tasarımı, polimerik atıklar. Multi-Response Optimization of Ultrasonic Pulse Velocity and the Modulus of Elasticity of ConcreteContaining Polymeric Wastes ABSTRACT In this study, the effect of the factors on ultrasonic velocity and elasticity modulus properties of concrete containing polymeric wastes was solved by multivariable regression analysis. Seven polymers, namely, low density polyethylene, high density polyethylene, polypropylene, thermoplastic elastomer, dimethyl terephthalate, polyethylene terephthalate, polyethylene naphthalate, each at three levels were selected, and an orthogonal array (L27) was used to carry out the experimental runs. Polymers with reducing effect on ultrasound pulse velocity were identified as high density polyethylene, low density polyethylene, thermoplastic elastomer, and polyethylene terephthalate and polyethylene naphthalate with p-values of 0.000, 0.009, 0.007, 0.008 and 0.001, respectively. Polymers having a reducing effect on the dynamic elasticity modulus were identified as high density polyethylene, polypropylene and polyethylene naphthalate with p-values of 0.001, 0.002 and 0.001, respectively. Optimal mixing ratios of concrete containing polymeric waste were determined by multi-response linear programming method based on linear regression models. Verification experiment with optimum mixture ratios shows that the multi-response linear programming method can be used effectively in solving the concrete compound optimization problems.

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“…Other methods were qualitative such as the Recurrence Plots (RPs) and their corresponding Recurrence Quantification Analysis (RQA) [2,3,10]. Chen et al showed that RQA measures are more accurate than the standard entropy analysis techniques in detecting biomedical time series states [12]. Recurrence Plots introduced in our previous work [2,3] have shown their usefulness in detecting fetal distress due to IUGR.…”

Section: Introductionmentioning

confidence: 99%