Electrocardiogram based classifier for driver drowsiness detection

Kwok Tai Chui; Kim Fung Tsang; Hao Ran Chi; Chung Kit Wu; Bingo Wing Kuen Ling

doi:10.1109/INDIN.2015.7281802

Electrocardiogram based classifier for driver drowsiness detection

Kwok Tai Chui, Kim Fung Tsang, Hao Ran Chi, Chung Kit Wu, Bingo Wing Kuen Ling

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

Original language	English
Title of host publication	Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015
Pages	600-603
Number of pages	4
ISBN (Electronic)	9781479966493
DOIs	https://doi.org/10.1109/INDIN.2015.7281802
Publication status	Published - 28 Sept 2015
Externally published	Yes
Event	13th International Conference on Industrial Informatics, INDIN 2015 - Cambridge, United Kingdom Duration: 22 Jul 2015 → 24 Jul 2015

Publication series

Name	Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015

Conference

Conference	13th International Conference on Industrial Informatics, INDIN 2015
Country/Territory	United Kingdom
City	Cambridge
Period	22/07/15 → 24/07/15

Keywords

drowsiness detection
electrocardiogram
machine learning
support vector machine
transportation

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10.1109/INDIN.2015.7281802

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Chui, K. T., Tsang, K. F., Chi, H. R., Wu, C. K., & Ling, B. W. K. (2015). Electrocardiogram based classifier for driver drowsiness detection. In Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015 (pp. 600-603). Article 7281802 (Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015). https://doi.org/10.1109/INDIN.2015.7281802

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title = "Electrocardiogram based classifier for driver drowsiness detection",

abstract = "Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.",

keywords = "drowsiness detection, electrocardiogram, machine learning, support vector machine, transportation",

author = "Chui, {Kwok Tai} and Tsang, {Kim Fung} and Chi, {Hao Ran} and Wu, {Chung Kit} and Ling, {Bingo Wing Kuen}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 13th International Conference on Industrial Informatics, INDIN 2015 ; Conference date: 22-07-2015 Through 24-07-2015",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/INDIN.2015.7281802",

language = "English",

series = "Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015",

pages = "600--603",

booktitle = "Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015",

}

Chui, KT, Tsang, KF, Chi, HR, Wu, CK & Ling, BWK 2015, Electrocardiogram based classifier for driver drowsiness detection. in Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015., 7281802, Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, pp. 600-603, 13th International Conference on Industrial Informatics, INDIN 2015, Cambridge, United Kingdom, 22/07/15. https://doi.org/10.1109/INDIN.2015.7281802

Electrocardiogram based classifier for driver drowsiness detection. / Chui, Kwok Tai; Tsang, Kim Fung; Chi, Hao Ran et al.
Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015. 2015. p. 600-603 7281802 (Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Electrocardiogram based classifier for driver drowsiness detection

AU - Chui, Kwok Tai

AU - Tsang, Kim Fung

AU - Chi, Hao Ran

AU - Wu, Chung Kit

AU - Ling, Bingo Wing Kuen

PY - 2015/9/28

Y1 - 2015/9/28

N2 - Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

AB - Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

KW - drowsiness detection

KW - electrocardiogram

KW - machine learning

KW - support vector machine

KW - transportation

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U2 - 10.1109/INDIN.2015.7281802

DO - 10.1109/INDIN.2015.7281802

M3 - Conference contribution

AN - SCOPUS:84949482352

T3 - Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015

SP - 600

EP - 603

BT - Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015

T2 - 13th International Conference on Industrial Informatics, INDIN 2015

Y2 - 22 July 2015 through 24 July 2015

ER -

Electrocardiogram based classifier for driver drowsiness detection

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