GC-ViT: Graph Convolution-Augmented Vision Transformer for Pilot G-LOC Detection Through AU Correlation Learning

Prolonged +Gz acceleration during high-performance flight exposes pilots to the risk of G-induced loss of consciousness (G-LOC), a dangerous condition that compromises operational safety. To enable early detection without intrusive sensors, we present a vision-based warning system that analyzes facial action units (AUs) as physiological indicators of impending G-LOC. Our approach combines computer vision with physiological modeling to capture subtle facial microexpressions associated with cerebral hypoxia using widely available RGB cameras. We propose a novel Graph Convolution-Augmented Vision Transformer (GC-ViT) network architecture that effectively captures dynamic AU variations in pilots under G-LOC conditions by integrating global context modeling with vision Transformer. The proposed framework integrates a vision–semantics collaborative Transformer for robust AU feature extraction, where EfficientNet-based spatiotemporal modeling is enhanced by Transformer attention mechanisms to maintain recognition accuracy under high-G stress. Building upon this, we develop a graph-based physiological model that dynamically tracks interactions between critical AUs during G-LOC progression by learning the characteristic patterns of AU co-activation during centrifugal training. Experimental validation on centrifuge training datasets demonstrates strong performance, achieving an AUC-ROC of 0.898 and an AP score of 0.96, confirming the system’s ability to reliably identify characteristic patterns of AU co-activation during G-LOC events. Overall, this contact-free system offers an interpretable solution for rapid G-LOC detection, or as a complementary enhancement to existing aeromedical monitoring technologies. The non-invasive design demonstrates significant potential for improving safety in aerospace physiology applications without requiring modifications to current cockpit or centrifuge setups.