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ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods

The incidence of maritime accidents can be significantly reduced by identifying the deck officer’s fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the...

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Autores principales: Li, Chenghao, Fu, Yuhui, Ouyang, Ruihong, Liu, Yu, Hou, Xinwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460432/
https://www.ncbi.nlm.nih.gov/pubmed/36080966
http://dx.doi.org/10.3390/s22176506
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author Li, Chenghao
Fu, Yuhui
Ouyang, Ruihong
Liu, Yu
Hou, Xinwen
author_facet Li, Chenghao
Fu, Yuhui
Ouyang, Ruihong
Liu, Yu
Hou, Xinwen
author_sort Li, Chenghao
collection PubMed
description The incidence of maritime accidents can be significantly reduced by identifying the deck officer’s fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver’s fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers’ eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers’ fatigue level, which combines a video-based approach to record the officer’s eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of “fatigue” at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers’ fatigue levels finally reaches 95.74 percent.
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spelling pubmed-94604322022-09-10 ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods Li, Chenghao Fu, Yuhui Ouyang, Ruihong Liu, Yu Hou, Xinwen Sensors (Basel) Article The incidence of maritime accidents can be significantly reduced by identifying the deck officer’s fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver’s fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers’ eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers’ fatigue level, which combines a video-based approach to record the officer’s eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of “fatigue” at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers’ fatigue levels finally reaches 95.74 percent. MDPI 2022-08-29 /pmc/articles/PMC9460432/ /pubmed/36080966 http://dx.doi.org/10.3390/s22176506 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Chenghao
Fu, Yuhui
Ouyang, Ruihong
Liu, Yu
Hou, Xinwen
ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
title ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
title_full ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
title_fullStr ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
title_full_unstemmed ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
title_short ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
title_sort adtido: detecting the tired deck officer with fusion feature methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460432/
https://www.ncbi.nlm.nih.gov/pubmed/36080966
http://dx.doi.org/10.3390/s22176506
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