Modeling Visual Fatigue in Remote Tower Air Traffic Controllers: A Multimodal Physiological Data-Based Approach

As a forward-looking development in air traffic control (ATC), remote towers rely on virtualized information presentation, which may exacerbate visual fatigue among controllers and compromise operational safety. This study proposes a visual fatigue recognition model based on multimodal physiological signals. A 60-min simulated remote tower task was conducted with 36 participants, during which eye-tracking (ET), electroencephalography (EEG), electrocardiography (ECG), and electrodermal activity (EDA) signals were collected. Subjective fatigue questionnaires and objective ophthalmic measurements were also recorded before and after the task. Statistically significant features were identified through paired t-tests, and fatigue labels were constructed by combining subjective and objective indicators. LightGBM was then employed to rank feature importance by integrating split frequency and information gain into a composite score. The top 12 features were selected and used to train a multilayer perceptron (MLP) for classification. The model achieved an average balanced accuracy of 0.92 and an F1 score of 0.90 under 12-fold cross-validation, demonstrating excellent predictive performance. The high-ranking features spanned four modalities, revealing typical physiological patterns of visual fatigue across ocular behavior, cortical activity, autonomic regulation, and arousal level. These findings validate the effectiveness of multimodal fusion in modeling visual fatigue and provide theoretical and technical support for human factor monitoring and risk mitigation in remote tower environments.