Design of an Emotion Recognition System Using Machine Learning for Maritime Operations: Development of a Cognitive Interface with Psychophysiological Data Analysis

In order to improve cognitive monitoring and situational awareness, this study presents a machine learning-based emotion identification system intended for maritime operations. The system classifies emotions based on psychophysiological data, such as electrodermal activity, heart rate variability, and facial expressions. This gives information about the cognitive states of maritime workers. The technology also recognizes dangerous conditions, correlates physiological reactions with performance indicators, and sends out notifications to reduce any hazards. Techniques for evaluating cognitive workload, including pupillometry and subjective rating scales, are integrated, providing a thorough grasp of mental demands. The end result of this integration is a cognitive interface that improves decision-making and overall safety in marine operations by offering real-time feedback. The findings exhibit significant implications for both cyber resilience and operational efficiency, highlighting the possibility of psychophysiological monitoring in high-stakes settings.

کلیدواژه ها:

Emotion Recognition ، Machine Learning ، Psychophysiology ، Cognitive Interface