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ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
Li, Chenghao1; Fu, Yuhui1; Ouyang, Ruihong2; Liu, Yu3,4; Hou, Xinwen3,4
Source PublicationSENSORS
Corresponding AuthorFu, Yuhui(
AbstractThe 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.
KeywordEEG deck officer fatigue detection ECD-EEG fusion features Bi-GRU neural network classifier
Indexed BySCI
WOS Research AreaChemistry ; Engineering ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000851974300001
Citation statistics
Document Type期刊论文
Corresponding AuthorFu, Yuhui
Affiliation1.Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China
2.Harbin Engn Univ, Sch Comp Sci & Technol, Harbin 150009, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
Recommended Citation
GB/T 7714
Li, Chenghao,Fu, Yuhui,Ouyang, Ruihong,et al. ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods[J]. SENSORS,2022,22(17):16.
APA Li, Chenghao,Fu, Yuhui,Ouyang, Ruihong,Liu, Yu,&Hou, Xinwen.(2022).ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods.SENSORS,22(17),16.
MLA Li, Chenghao,et al."ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods".SENSORS 22.17(2022):16.
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