Exploring a Non-Invasive Fatigue Assessment Framework for Remote Tower Scenarios: A Simulation Study

Accurately assessing the fatigue levels of air traffic controllers is crucial for reducing human errors in ATC and ensuring the safe and orderly operation of the civil aviation transportation system. In remote tower scenarios, air traffic controllers’ work environments and task interaction modes differ significantly from those in traditional towers, and traditional fatigue detection approaches relying on physiological monitoring can cause intrusive disruptions to ATC operations. To overcome these limitations, this study proposes a scenario-based, non-invasive assessment framework for accurate and low-interference fatigue recognition. Taking three key scenario elements (traffic load, main operation screen brightness, and core work area illuminance) as the basis for measuring fatigue, the framework bridges the mapping from scenario elements to fatigue status, thereby enabling the transition of assessment inputs from physiological metrics to scenario features. In this mapping, fatigue labels are determined using a fusion strategy. Specifically, objective fatigue labels are derived from optimal wave features extracted from electroencephalogram data using one-way analysis of variance (OW-ANOVA), which are then fused with subjective labels based on the Karolinska Sleepiness Scale (KSS) self-reports through fuzzy C-means (FCM) clustering. Ultimately, a hybrid intelligent classification model integrating the Gannet optimization algorithm (GOA) and random forest (RF) is constructed to perform the primary assessment task. The experimental results indicate that the proposed framework achieves a recognition accuracy of 95.00%, outperforming six other commonly used classification or combination models. Ablation experiments and robustness tests validate the effectiveness of the fused labeling strategy and GOA modules, as well as the method’s excellent stability in resisting data noise. Furthermore, feature interpretability analysis reveals the quantitative influence of the three core fatigue drivers used. The research findings confirm the feasibility of non-invasive fatigue assessment for remote tower controllers leveraging scenario-based elements, which can offer intelligent decision support for controller shift scheduling, visual environment optimization, and targeted safety interventions.