ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods

doi:10.3390/s22176506

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 脑机融合与认知评估

	ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
	Li, Chenghao 1; Fu, Yuhui 1; Ouyang, Ruihong 2; Liu, Yu3,4 ; Hou, Xinwen3,4
发表期刊	SENSORS
	2022-09-01
卷号	22 期号:17 页码:16
通讯作者	Fu, Yuhui(hhhgfyh@dlmu.edu.cn)
摘要	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.
关键词	EEG deck officer fatigue detection ECD-EEG fusion features Bi-GRU neural network classifier
DOI	10.3390/s22176506
关键词[WOS]	EEG ; FATIGUE
收录类别	SCI
语种	英语
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000851974300001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50090
专题	多模态人工智能系统全国重点实验室_脑机融合与认知评估
通讯作者	Fu, Yuhui
作者单位	1.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
推荐引用方式 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.