Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology, 6009 Ålesund, Norway.
Products and Services R&D, Oil, Gas and Chemicals, ABB AS, 0666 Oslo, Norway.
Sensors (Basel). 2020 May 2;20(9):2588. doi: 10.3390/s20092588.
Human-related issues are currently the most significant factor in maritime causalities, especially in demanding operations that require coordination between two or more vessels and/or other maritime structures. Some of these human-related issues include incorrect, incomplete, or nonexistent following of procedures; lack of situational awareness; and physical or mental fatigue. Among these, mental fatigue is especially dangerous, due to its capacity to reduce reaction time, interfere in the decision-making process, and affect situational awareness. Mental fatigue is also especially hard to identify and quantify. Self-assessment of mental fatigue may not be reliable and few studies have assessed mental fatigue in maritime operations, especially in real time. In this work we propose an integrated sensor fusion system for mental fatigue assessment using physiological sensors and convolutional neural networks. We show, by using a simulated navigation experiment, how data from different sensors can be fused into a robust mental fatigue assessment tool, capable of achieving up to 100 % detection accuracy for single-subject classification. Additionally, the use of different sensors seems to favor the representation of the transition between mental fatigue states.
人为因素是目前航海事故的最重要因素，尤其是在需要两艘或多艘船只和/或其他海上结构之间协调的高要求操作中。这些人为因素包括程序执行不正确、不完整或不存在；缺乏态势感知；身体或精神疲劳。在这些因素中，精神疲劳尤其危险，因为它会降低反应时间、干扰决策过程和影响态势感知。精神疲劳也特别难以识别和量化。自我评估的精神疲劳可能不可靠，而且很少有研究评估航海操作中的精神疲劳，尤其是实时评估。在这项工作中，我们提出了一种使用生理传感器和卷积神经网络进行精神疲劳评估的综合传感器融合系统。我们通过使用模拟导航实验，展示了如何将来自不同传感器的数据融合到一个稳健的精神疲劳评估工具中，该工具能够实现高达 100%的单主体分类检测精度。此外，使用不同的传感器似乎有利于表示精神疲劳状态之间的转变。
