Žagar Dejan
Faculty of Maritime Studies and Transport, University of Ljubljana, 1000 Ljubljana, Slovenia.
Sensors (Basel). 2025 Sep 4;25(17):5508. doi: 10.3390/s25175508.
The purpose of the study is to research emotional labor and cognitive effort in radar-based collision avoidance tasks within a nautical simulator. By assessing participants' emotional responses and mental strain, the research aimed to identify negative emotional states associated with a lack of experience, which, in the worst-case scenario, could contribute to navigational incidents. Fifteen participants engaged in multiple sessions simulating typical maritime conditions and navigation challenges. Emotional and cognitive effort were evaluated using three primary methods: heart rate monitoring, a Likert-scale questionnaire, and real-time facial expression recognition software. Heart rate data provided physiological indicators of stress, while the questionnaire and facial expressions captured subjective perceptions of difficulty and emotional strain. By correlating the measurements, the study aimed to uncover emotional patterns linked to task difficulty with insight into engagement, attention, and blink rate levels during the simulation, revealing how a lack of experience contributes to negative emotions and human factor errors. The understanding of the emotional labor and effort in maritime navigation training contributes to strategies for reducing incident risk through improved simulation training practices.
本研究的目的是在航海模拟器中研究基于雷达的避碰任务中的情绪劳动和认知努力。通过评估参与者的情绪反应和心理压力,该研究旨在识别与缺乏经验相关的负面情绪状态,在最坏的情况下,这些负面情绪状态可能导致航行事故。15名参与者进行了多次模拟典型海上条件和导航挑战的实验。使用三种主要方法评估情绪和认知努力:心率监测、李克特量表问卷和实时面部表情识别软件。心率数据提供了压力的生理指标,而问卷和面部表情则捕捉了对难度和情绪压力的主观感受。通过关联这些测量结果,该研究旨在揭示与任务难度相关的情绪模式,并深入了解模拟过程中的参与度、注意力和眨眼率水平,揭示缺乏经验如何导致负面情绪和人为因素错误。对海上航行训练中情绪劳动和努力的理解有助于通过改进模拟训练实践来制定降低事故风险的策略。