Advancing Aviation Saftey Through Machine Learning and Psychological Data

Aviation safety is the highest priority in today’s air transportation systems. Human factors such as fatigue, stress and cognitive overload may have a strong influence on pilot performance. This study explores the use of machine learning techniques in conjunction with psychophysiological data to improve the safety of aircraft and decision-making procedures. The proposed system collects physiological indicators with wearable sensors and monitoring equipment on pilots, including pulse rate, brain activity and eye movement. The signals are then processed and analysed with machine learning algorithms to identify patterns associated with weariness, stress and poorer cognitive function. The technology anticipates potentially dangerous situations and provides early warnings to enhance safety in the air. The experimental evaluation shows the proposed approach is able to detect anomalous pilot states and improve safety monitoring. The research findings demonstrate that the integration of artificial intelligence and physiological monitoring offers a robust basis for enhancing flight safety and minimising accidents stemming from human error.