A Design Approach for the System to Detect and to Warn the Unrecognized Spatial Disorientation

Recently, Spatial Disorientation (SD) has emerged as a critical safety concern in helicopter operations, with unrecognized Type I SD posing a significant risk due to its high fatality rate. Although various methods have been proposed to detect and prevent SD, most approaches focus on recognized forms (Type II, Type III) and rely primarily on subjective, physiological, or conventional statistical analyses. This study introduces a novel system designed to detect and warn against unrecognized Type I SD by integrating flight simulation, subjective pilot assessments, and machine learning-based flight data analysis. Utilizing a helicopter flight simulator to generate realistic flight scenarios, high-quality performance data were collected and labeled through both pilot feedback and expert instructor validation. The system promptly alerts the pilot with visual LED signals, enabling rapid corrective action before critical flight deviations occur. This integrated approach demonstrates the feasibility of robust, real-time Type I SD detection and highlights the potential of advanced analytics to improve overall flight safety and operational reliability.