A Real-Time Detection of Pilot Workload Using Low-Interference Devices

Abstract

Excessive pilot workload is one of the significant causes of flight accidents. The detection of flight workload can help optimize aircraft crew operation procedures, improve cockpit human–machine interface (HMIs) design, and ultimately reduce the risk of flight accidents. However, traditional detection methods often employ invasive or patch-based devices that can interfere with the pilot’s control. In addition, they generally lack real-time capabilities, while the workload of pilots actually varies continuously. Moreover, most models do not take individual physiological differences into account, leading to the poor performance of new pilots. To address these issues, this study developed a real-time pilot workload detection model based on low-interference devices, including telemetry eye trackers and a pressure-sensing seat cushion. Specifically, the Adaptive KNN-Ensemble Pilot Workload Detection (AKE-PWD) model is proposed, combining KNN in the outer layer for identifying the physiological feature cluster with the ensemble classifier corresponding to this cluster in the inner layer. The ensemble model employs random forest, gradient boosting trees, and FCN–Transformer as base learners. It utilizes soft voting for predictions, integrating the strengths of various networks and effectively extracting the sequential features from complex data. Results show that the model achieves a detection accuracy of 82.6% on the cross-pilot testing set, with a runtime of 0.1 s, surpassing most studies that use invasive or patch-based detection devices. Additionally, the model demonstrates high accuracy across different individuals, indicating good generalization. The results are expected to improve flight safety.