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Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.

World Health Organization (WHO) states that approximately 1.3 million of people die every year because of road traffic crashes. Microsleep is one of the factors of traffic crashes and the number of accidents caused by microsleep increase rapidly each day. This leads to a major accident resulting to...

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Main Authors: H. S., Zaleha, Abdul Wahab, Nur Haliza, Ithnin, Norafida
Format: Conference or Workshop Item
Published: 2022
Subjects:
T58.6-58.62 Management information systems
Online Access:http://eprints.utm.my/108793/
https://doi.org/10.1063/5.0199018
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spelling my.utm.1087932024-12-09T06:22:55Z http://eprints.utm.my/108793/ Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention. H. S., Zaleha Abdul Wahab, Nur Haliza Ithnin, Norafida T58.6-58.62 Management information systems World Health Organization (WHO) states that approximately 1.3 million of people die every year because of road traffic crashes. Microsleep is one of the factors of traffic crashes and the number of accidents caused by microsleep increase rapidly each day. This leads to a major accident resulting to higher number of deaths, injuries, demolition of properties and permanent disabilities. To overcome this situation, microsleep detection approach of smart vehicle needs to be studied in detail. Hence, in this project work, we use Convolution Neural Network (CNN) algorithm for this purpose. This proposed work implies face features as input data to evaluate the accuracy of microsleep traits while driving the smart vehicle. Face features data are capable to predict microsleep. CNN plays a significant role in this project due to its capability in performing both generative and descriptive tasks especially in image and video recognition. Thus, the model will analyze between open, closed, no yawn and yawn expression. This integration can be evaluated by detecting the change in face features from normal expression to microsleep symptoms such as opened eyes degree, closed eyes degree and mouth yawning. Results of the preliminary stages of this research are included and the input dataset is gained from Kaggle platform. The preliminary results show the microsleep model’s prediction with 72% accuracy by using CNN algorithm. 2022-06-07 Conference or Workshop Item PeerReviewed H. S., Zaleha and Abdul Wahab, Nur Haliza and Ithnin, Norafida (2022) Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention. In: Proceedings Of The International Conference On Green Engineering & Technology 2022 (ICONGETECH 2022), 17 November 2022 - 18 November 2022, Seoul, South Korea. https://doi.org/10.1063/5.0199018
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T58.6-58.62 Management information systems
spellingShingle T58.6-58.62 Management information systems
H. S., Zaleha
Abdul Wahab, Nur Haliza
Ithnin, Norafida
Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.
description World Health Organization (WHO) states that approximately 1.3 million of people die every year because of road traffic crashes. Microsleep is one of the factors of traffic crashes and the number of accidents caused by microsleep increase rapidly each day. This leads to a major accident resulting to higher number of deaths, injuries, demolition of properties and permanent disabilities. To overcome this situation, microsleep detection approach of smart vehicle needs to be studied in detail. Hence, in this project work, we use Convolution Neural Network (CNN) algorithm for this purpose. This proposed work implies face features as input data to evaluate the accuracy of microsleep traits while driving the smart vehicle. Face features data are capable to predict microsleep. CNN plays a significant role in this project due to its capability in performing both generative and descriptive tasks especially in image and video recognition. Thus, the model will analyze between open, closed, no yawn and yawn expression. This integration can be evaluated by detecting the change in face features from normal expression to microsleep symptoms such as opened eyes degree, closed eyes degree and mouth yawning. Results of the preliminary stages of this research are included and the input dataset is gained from Kaggle platform. The preliminary results show the microsleep model’s prediction with 72% accuracy by using CNN algorithm.
format Conference or Workshop Item
author H. S., Zaleha
Abdul Wahab, Nur Haliza
Ithnin, Norafida
author_facet H. S., Zaleha
Abdul Wahab, Nur Haliza
Ithnin, Norafida
author_sort H. S., Zaleha
title Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.
title_short Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.
title_full Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.
title_fullStr Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.
title_full_unstemmed Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.
title_sort microsleep detection using convolution neural network (cnn) in deep learning (dl) for accident prevention.
publishDate 2022
url http://eprints.utm.my/108793/
https://doi.org/10.1063/5.0199018
_version_ 1818834049760755712
score 13.222552

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