Assessment of stress levels based on biosignal during the simulator-based maritime navigation training and its impact on sailing route reliability

Abstract

Maritime training can improve safety by equipping seafarers with the knowledge and skills to manage risk. However, designing a quality training program can be challenging and stress can negatively impact performance and safety. To address this, the present study aims to investigate the relationship between stress and training outcomes, with the goal of developing more effective stress-based training systems. Two stressful scenarios were designed with varying safety factors involved during navigation tasks. The study examines the impact of stress levels on training outcomes and performance based on safety factors and the correlation between self-assessed stress levels and objective stress levels obtained from biosignal data. The study was conducted in a simulated bridge environment in Tromsø, Norway, and analyzed using statistical tests and machine learning models. The findings of this study indicate that training scenarios can be classified by stress levels, which were found to be associated with reduced visibility, equipment failures, and severe weather conditions. Additionally, the study revealed that stress levels can negatively impact performance in maritime navigation and sailing route reliability. These findings provide insights into how to improve the quality and effectiveness of maritime training programs and ultimately enhance safety at sea.

Keywords

• Maritime navigation
• Biosignal
• Machine learning
• Stress
• Simulation-based training