Citation
Abstract
One of the major reasons for road accidents is driver’s fatigue which causes several fatalities every year. Various studies on road accidents have proved that 20% of the accidents are caused mainly due to fatigue among drivers while driving. This paper presents the use of deep learning technique in classifying fatigue in drivers. By using deep neural networks, features are extracted automatically from preprocessed data of physiological signals such as electrocardiogram, heart rate, skin conductance response and body temperature. Public dataset HciLAB was used to train and validate the classification model. In this work, a comparative analysis of using Recurrent Neural Network - Long Short-term Memory (RNN-LSTM) deep learning architecture and the standard artificial neural network (ANN) was proposed and developed to classify fatigue based on the physiological features of the driver. The results revealed the superiority RNN-LSTM (98%) over standard ANN (80%), for driver fatigue classification. The proposed methods, based on RNN-LSTM deep learning architecture introduced elevated average accuracy in comparison with the standard artificial neural network.
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Official URL or Download Paper: https://ieeexplore.ieee.org/document/10079443
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Additional Metadata
Item Type: | Conference or Workshop Item (Paper) |
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Divisions: | Faculty of Engineering Malaysian Research Institute on Ageing |
DOI Number: | https://doi.org/10.1109/IECBES54088.2022.10079443 |
Publisher: | IEEE |
Keywords: | Driver fatigue classification; Deep learning technique; RNN-LSTM |
Depositing User: | Ms. Nuraida Ibrahim |
Date Deposited: | 06 Nov 2023 10:21 |
Last Modified: | 06 Nov 2023 10:21 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/IECBES54088.2022.10079443 |
URI: | http://psasir.upm.edu.my/id/eprint/37788 |
Statistic Details: | View Download Statistic |
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