AI Meets Maritime Training: Precision Analytics for Enhanced Safety and Performance
Vishakha Lall, Yisi Liu

TL;DR
This paper presents an AI-driven framework that objectively assesses maritime trainees' performance using visual focus, speech, and stress analysis, enhancing safety and training effectiveness in high-risk scenarios.
Contribution
It introduces a novel AI system integrating eye tracking, speech recognition, and stress detection to improve maritime training assessments.
Findings
Achieved ~92% accuracy in visual focus detection
Achieved ~91% accuracy in maritime speech recognition
Achieved ~90% accuracy in stress detection
Abstract
Traditional simulator-based training for maritime professionals is critical for ensuring safety at sea but often depends on subjective trainer assessments of technical skills, behavioral focus, communication, and body language, posing challenges such as subjectivity, difficulty in measuring key features, and cognitive limitations. Addressing these issues, this study develops an AI-driven framework to enhance maritime training by objectively assessing trainee performance through visual focus tracking, speech recognition, and stress detection, improving readiness for high-risk scenarios. The system integrates AI techniques, including visual focus determination using eye tracking, pupil dilation analysis, and computer vision; communication analysis through a maritime-specific speech-to-text model and natural language processing; communication correctness using large language models; and…
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Taxonomy
TopicsMaritime Navigation and Safety · Human-Automation Interaction and Safety · Sleep and Work-Related Fatigue
