ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods
文献类型：期刊论文
| 作者 | Li, Chenghao1; Fu, Yuhui1; Ouyang, Ruihong2; Liu, Yu3,4 ; Hou, Xinwen3,4
|
| 刊名 | SENSORS
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 22期号:17页码:16 |
| 关键词 | EEG deck officer fatigue detection ECD-EEG fusion features Bi-GRU neural network classifier |
| DOI | 10.3390/s22176506 |
| 通讯作者 | Fu, Yuhui(hhhgfyh@dlmu.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | EEG ; FATIGUE |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000851974300001 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50089]  |
| 专题 | 综合信息系统研究中心_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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。