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An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models

Driver drowsiness is a well known problem that depreciates road safety that could cause road accidents, worldwide. Researchers are increasingly using the eye/eyelid images or the electroencephalogram's (EEG) spectral information to detect drowsiness in drivers. However, no attempt has been made...

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Main Authors: Egambaram, A., Badruddin, N.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:http://scholars.utp.edu.my/id/eprint/37626/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152437223&doi=10.1109%2fIECBES54088.2022.10079592&partnerID=40&md5=2df3b78840bb6741cbdbf67d0a1e6d65
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spelling oai:scholars.utp.edu.my:376262023-10-17T02:16:33Z http://scholars.utp.edu.my/id/eprint/37626/ An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models Egambaram, A. Badruddin, N. Driver drowsiness is a well known problem that depreciates road safety that could cause road accidents, worldwide. Researchers are increasingly using the eye/eyelid images or the electroencephalogram's (EEG) spectral information to detect drowsiness in drivers. However, no attempt has been made to detect drowsiness using the eye blink artifact features that contaminates EEG signals, which are typically regarded noise and undesired. Therefore, in this study, we have investigated whether the eye blink artifacts that were originally intended to be eliminated from EEG signals could be used to detect drowsiness among drivers. The eye blink artifacts and their features are extracted from EEG signals via the BLINKER algorithm. The deep learning classifiers, multilayer perceptron (MLP) and Recurrent Neural Network with Long-Short-Term-Memory (RNN-LSTM) are trained, validated, and tested to confirm if the eye blink artifacts can be used as an indicator of drowsiness. The investigation has demonstrated that using eye blink artifacts as an indicator of drowsiness is viable, with a classification accuracy of 94.91 achieved through RNN-LSTM. © 2022 IEEE. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item NonPeerReviewed Egambaram, A. and Badruddin, N. (2022) An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models. In: UNSPECIFIED. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152437223&doi=10.1109%2fIECBES54088.2022.10079592&partnerID=40&md5=2df3b78840bb6741cbdbf67d0a1e6d65 10.1109/IECBES54088.2022.10079592 10.1109/IECBES54088.2022.10079592 10.1109/IECBES54088.2022.10079592
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Driver drowsiness is a well known problem that depreciates road safety that could cause road accidents, worldwide. Researchers are increasingly using the eye/eyelid images or the electroencephalogram's (EEG) spectral information to detect drowsiness in drivers. However, no attempt has been made to detect drowsiness using the eye blink artifact features that contaminates EEG signals, which are typically regarded noise and undesired. Therefore, in this study, we have investigated whether the eye blink artifacts that were originally intended to be eliminated from EEG signals could be used to detect drowsiness among drivers. The eye blink artifacts and their features are extracted from EEG signals via the BLINKER algorithm. The deep learning classifiers, multilayer perceptron (MLP) and Recurrent Neural Network with Long-Short-Term-Memory (RNN-LSTM) are trained, validated, and tested to confirm if the eye blink artifacts can be used as an indicator of drowsiness. The investigation has demonstrated that using eye blink artifacts as an indicator of drowsiness is viable, with a classification accuracy of 94.91 achieved through RNN-LSTM. © 2022 IEEE.
format Conference or Workshop Item
author Egambaram, A.
Badruddin, N.
spellingShingle Egambaram, A.
Badruddin, N.
An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
author_facet Egambaram, A.
Badruddin, N.
author_sort Egambaram, A.
title An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
title_short An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
title_full An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
title_fullStr An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
title_full_unstemmed An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
title_sort investigation to detect driver drowsiness from eye blink artifacts using deep learning models
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/37626/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152437223&doi=10.1109%2fIECBES54088.2022.10079592&partnerID=40&md5=2df3b78840bb6741cbdbf67d0a1e6d65
_version_ 1781707934376394752
score 13.222552

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