relation: https://khub.utp.edu.my/scholars/17197/
title: An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models
creator: Egambaram, A.
creator: Badruddin, N.
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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Egambaram, A. and Badruddin, N.  (2022) An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152437223&doi=10.1109%2fIECBES54088.2022.10079592&partnerID=40&md5=2df3b78840bb6741cbdbf67d0a1e6d65
relation: 10.1109/IECBES54088.2022.10079592
identifier: 10.1109/IECBES54088.2022.10079592