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Driver drowsiness detection using different classification algorithms

Capability of electrocardiogram (ECG) signal in contributing to the daily application keeps developing days by days. As technology advances, ECG marks the possibility as a potential mechanism towards the drowsiness detection system. Driver drowsiness is a state between sleeping and being awake due t...

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Main Authors: Nor Shahrudin, Nur Shahirah, Sidek, Khairul Azami
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Physics Publishing 2020
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/81981/1/81981_Driver%20Drowsiness%20Detection%20using%20Different%20Classification%20Algorithms.pdf
http://irep.iium.edu.my/81981/2/81981_Driver%20Drowsiness%20Detection%20using%20Different%20Classification%20Algorithms_SCOPUS.pdf
http://irep.iium.edu.my/81981/
https://iopscience.iop.org/article/10.1088/1742-6596/1502/1/012037/meta
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spelling my.iium.irep.819812020-08-06T00:54:43Z http://irep.iium.edu.my/81981/ Driver drowsiness detection using different classification algorithms Nor Shahrudin, Nur Shahirah Sidek, Khairul Azami TK Electrical engineering. Electronics Nuclear engineering TK7885 Computer engineering Capability of electrocardiogram (ECG) signal in contributing to the daily application keeps developing days by days. As technology advances, ECG marks the possibility as a potential mechanism towards the drowsiness detection system. Driver drowsiness is a state between sleeping and being awake due to body fatigue while driving. This condition has become a common issue that leads to road accidents and death. It is proven in previous studies that biological signals are closely related to a person's reaction. Electrocardiogram (ECG) is an electrical indicator of the heart, provides such criteria as it reflects the heart activity that can detect changes in human response which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. This study obtained ECG data from the ULg multimodality drowsiness database to simulate the different stages of sleep, which are PVT1 as early sleep while PVT2 as deep sleep. The signals are later processed in MATLAB using Savitzky-Golay filter to remove artifacts in the signal. Then, QRS complexes are extracted from the acquired ECG signal. The process was followed by classifying the ECG signal using Machine Learning (ML) tools. The classification techniques that include Multilayer Perceptron (MLP), k-Nearest Neighbour (IBk) and Bayes Network (BN) algorithms proved to support the argument made in both PVT1 and PVT2 to measure the accuracy of the data acquired. As a result, PVT1 and PVT2 are correctly classified as the result shown with higher percentage accuracy on each PVTs. Hence, this paper present and prove the reliability of ECG signal for drowsiness detection in classifying high accuracy ECG data using different classification algorithms. Institute of Physics Publishing 2020-06-17 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/81981/1/81981_Driver%20Drowsiness%20Detection%20using%20Different%20Classification%20Algorithms.pdf application/pdf en http://irep.iium.edu.my/81981/2/81981_Driver%20Drowsiness%20Detection%20using%20Different%20Classification%20Algorithms_SCOPUS.pdf Nor Shahrudin, Nur Shahirah and Sidek, Khairul Azami (2020) Driver drowsiness detection using different classification algorithms. In: International Conference on Telecommunication, Electronic and Computer Engineering 2019, ICTEC 2019, 22nd - 24th Oct. 2019, Melaka, Malaysia.. https://iopscience.iop.org/article/10.1088/1742-6596/1502/1/012037/meta 10.1088/1742-6596/1502/1/012037
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
TK7885 Computer engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
TK7885 Computer engineering
Nor Shahrudin, Nur Shahirah
Sidek, Khairul Azami
Driver drowsiness detection using different classification algorithms
description Capability of electrocardiogram (ECG) signal in contributing to the daily application keeps developing days by days. As technology advances, ECG marks the possibility as a potential mechanism towards the drowsiness detection system. Driver drowsiness is a state between sleeping and being awake due to body fatigue while driving. This condition has become a common issue that leads to road accidents and death. It is proven in previous studies that biological signals are closely related to a person's reaction. Electrocardiogram (ECG) is an electrical indicator of the heart, provides such criteria as it reflects the heart activity that can detect changes in human response which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. This study obtained ECG data from the ULg multimodality drowsiness database to simulate the different stages of sleep, which are PVT1 as early sleep while PVT2 as deep sleep. The signals are later processed in MATLAB using Savitzky-Golay filter to remove artifacts in the signal. Then, QRS complexes are extracted from the acquired ECG signal. The process was followed by classifying the ECG signal using Machine Learning (ML) tools. The classification techniques that include Multilayer Perceptron (MLP), k-Nearest Neighbour (IBk) and Bayes Network (BN) algorithms proved to support the argument made in both PVT1 and PVT2 to measure the accuracy of the data acquired. As a result, PVT1 and PVT2 are correctly classified as the result shown with higher percentage accuracy on each PVTs. Hence, this paper present and prove the reliability of ECG signal for drowsiness detection in classifying high accuracy ECG data using different classification algorithms.
format Conference or Workshop Item
author Nor Shahrudin, Nur Shahirah
Sidek, Khairul Azami
author_facet Nor Shahrudin, Nur Shahirah
Sidek, Khairul Azami
author_sort Nor Shahrudin, Nur Shahirah
title Driver drowsiness detection using different classification algorithms
title_short Driver drowsiness detection using different classification algorithms
title_full Driver drowsiness detection using different classification algorithms
title_fullStr Driver drowsiness detection using different classification algorithms
title_full_unstemmed Driver drowsiness detection using different classification algorithms
title_sort driver drowsiness detection using different classification algorithms
publisher Institute of Physics Publishing
publishDate 2020
url http://irep.iium.edu.my/81981/1/81981_Driver%20Drowsiness%20Detection%20using%20Different%20Classification%20Algorithms.pdf
http://irep.iium.edu.my/81981/2/81981_Driver%20Drowsiness%20Detection%20using%20Different%20Classification%20Algorithms_SCOPUS.pdf
http://irep.iium.edu.my/81981/
https://iopscience.iop.org/article/10.1088/1742-6596/1502/1/012037/meta
_version_ 1675327169825341440
score 13.211869

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