College of Navigation, Dalian Maritime University, Dalian 116026, China.
School of Computer Science and Technology, Harbin Engineering University, Harbin 150009, China.
Sensors (Basel). 2022 Aug 29;22(17):6506. doi: 10.3390/s22176506.
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.
通过识别甲板人员的疲劳水平，可以显著降低海上事故的发生率。近年来，基于脑电图（EEG）技术的汽车驾驶员疲劳检测仪器得到了发展，并能够快速准确地确定驾驶员的疲劳程度。然而，个体差异和 EEG 信号的敏感性降低了检测精度。最近，还探索了另一种基于视频的技术，通过记录驾驶员眼睛特征的变化来检测驾驶员疲劳。为了提高基于 EEG 的方法的分类性能，本文引入了 ADTIDO（自动检测疲劳甲板人员）算法，这是一种基于 EEG 的甲板人员疲劳水平分类方法，它结合了一种基于视频的方法来记录每个时间窗口中船员的闭眼时间。本文使用离散小波变换（DWT）将 EEG 信号分解为六个子信号，从中提取各种基于 EEG 的特征，例如 MAV、SD 和 RMS。与传统的基于视频的计算眼睑闭合度（ECD）方法不同，本文从 EEG 信号中获取 ECD 值。然后创建 ECD-EEG 融合特征，并通过将 ECD 和 EEG 特征集结合起来，将其作为分类器的输入。此外，本研究还根据甲板人员的实时操作反应时间，在个体层面上定义了“疲劳”的概念。为了验证这项研究的效果，作者使用 21 名受试者采集的 EEG 信号进行了试验。结果表明，双向门控循环单元（Bi-GRU）网络的性能优于其他分类器，分类准确率达到 90.19%，比仅使用 EEG 特征作为输入的准确率高出 1.89%。通过结合 ADTIDO 通道的发现，甲板人员疲劳水平的分类准确率最终达到 95.74%。
