Performance monitoring of chemical plant field operators through eye gaze tracking

• Published in: Computers & chemical engineering Vol. 198; p. 109079
• Main Authors: Suresh, Rohit, Srinivasan, Babji, Srinivasan, Rajagopalan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2025
• Subjects: Eye tracking; Field operator; Human factors; Process safety; Training; Training; Process safety; Eye tracking; Field operator; Human factors
• ISSN: 0098-1354
• DOI: 10.1016/j.compchemeng.2025.109079

•Field operators (FOPs) play a crucial role in ensuring safe and efficient plant operations.•Conventional assessment techniques for FOPs are action-based and ignore cognitive aspects.•We use eye gaze movements of FOPs to gain insights into their information acquisition.•An automated methodology is proposed to identify the Areas-of-Interest to the operator while performing field activities.•Compared to manual annotation, the proposed approach shows 99.6 % accuracy and requires a fraction of the time. Field activities performed by human operators are indispensable in process industries despite the prevalence of automation. To ensure safe and efficient plant operations, periodic training and performance assessment of field operators (FOPs) is essential. While numerous studies have focused on control room operators, relatively little attention has been directed to FOPs. Conventional training and assessment techniques for FOPs are action-based and ignore the cognitive aspects. Here, we seek to address this crucial gap in the performance assessment of FOPs. Specifically, we use eye gaze movements of FOPs to gain insights into their information acquisition patterns, a key component of cognitive behavior. As the FOPs are mobile and visit different sections of the plant, we use head-mounted eye-trackers. A major challenge in analyzing gaze information obtained from head-mounted eye trackers is that the operators’ Field of View (FoV) varies continuously as they perform different activities. Traditionally, the challenge posed by the variations in the FoV is tackled through manual annotation of the gaze on Areas of Interest (AOIs), which is knowledge- and time-intensive. Here, we propose a methodology based on Scale-Invariant-Feature-Transform to automate the AOI identification. We demonstrate our methodology with a case study involving human subjects operating a lab-scale heat exchanger setup. Our automated approach shows high accuracy (99.6 %) in gaze-AOI mapping and requires a fraction of the time, compared to manual, frame-by-frame annotation. It, therefore, offers a practical approach for performing eye tracking on FOPs, and can engender quantification of their skills and expertise and operator-specific training.