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

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

24 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
Publisher	IEEE
Pages	600-603
ISBN (Print)	9781479966493
DOIs	https://doi.org/10.1109/INDIN.2015.7281802
Publication status	Published - 28 Sept 2015
Event	13th International Conference on Industrial Informatics (INDIN 2015) - Robinson College, Cambridge, United Kingdom Duration: 22 Jul 2015 → 24 Jul 2015

Conference

Conference	13th International Conference on Industrial Informatics (INDIN 2015)
Place	United Kingdom
City	Cambridge
Period	22/07/15 → 24/07/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Research Keywords

drowsiness detection
electrocardiogram
machine learning
support vector machine
transportation

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10.1109/INDIN.2015.7281802

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@inproceedings{d76f2ce0cf7e4408bcc4a92d717073c8,

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\}",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/INDIN.2015.7281802",

language = "English",

isbn = "9781479966493",

pages = "600--603",

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

publisher = "IEEE",

address = "United States",

note = "13th International Conference on Industrial Informatics (INDIN 2015) ; Conference date: 22-07-2015 Through 24-07-2015",

}

Chui, KT , Tsang, KF , Chi, HR , Wu, CK & Ling, BW-K 2015, Electrocardiogram based classifier for driver drowsiness detection. in Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015., 7281802, IEEE, 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. IEEE, 2015. p. 600-603 7281802.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › 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

UR - https://www.scopus.com/pages/publications/84949482352

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84949482352&origin=recordpage

U2 - 10.1109/INDIN.2015.7281802

DO - 10.1109/INDIN.2015.7281802

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479966493

SP - 600

EP - 603

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

PB - IEEE

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

Abstract

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UN SDGs

Research Keywords

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