A human-centric model for task demand assessment based on unsupervised learning-assisted eye movement measure

Abstract

Nowadays, Industry 5.0 marks a transformative shift from the focus on efficiency to a human-centered approach, emphasizing the principles of human-AI hybrid systems. This mode prioritizes intelligent technology that supports human abilities rather than replacing them, especially in safety–critical fields like air traffic management (ATM) with human controllers playing an essential role in maintaining safe and efficient operations. Quantifying the task demand of air traffic controllers (ATCOs) is vital to ensure optimal taskload management, thereby assisting in mitigating the risk of human error and promoting sustained operational performance. To realize this aim, this research proposes a human-centric model for task demand assessment based on unsupervised learning-assisted eye movement measure. Initially, two data streams are gathered from human-in-the-loop visual tasks, capturing flight information on the radar screen and eye-tracking data. These data streams are then synchronized and merged by aligning their timestamps. Subsequently, an unsupervised learning-based clustering approach is implemented, utilizing the OPTICS model to identify areas of interest based on aircraft positions, along with the K-Means model to categorize task intensity levels using the derived eye movement data. Finally, a task demand score index is developed for each task intensity level and across all task categories, with parameter weights determined through an entropy-based method. Comprehensive results and analyses are presented to illustrate the method’s applicability and effectiveness. This research paves the way for quantitatively understanding the specific taskload placed on ATCOs.