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    Dhrushti-AI: A multi-screen multi-user eye-tracking system to understand the cognitive behavior of humans in process industries
    (01-01-2023)
    Shajahan, Thasnimol Valuthottiyil
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    Madbhavi, Rahul
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    Shahab, Mohammed Aatif
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    Operator performance is key to ensuring safety in process industries. Therefore, a comprehensive assessment of their performance is critical for smooth and efficient plant operation. Traditional performance assessments are not comprehensive as these ignore cognitive aspects of performance. On the other hand, while eye-tracking-based approaches do provide a cognitive assessment during operating training, their applicability in a real-time real setting is limited. Existing eye-tracking systems come with many constraints affecting users' mobility (restricted movement in all directions) in their environment. In addition, it is beyond the scope of current eye trackers to track multiple users working in an environment, such as in control rooms. Satisfying the above requirements makes these eye-trackers expensive. In this work, we demonstrate the capabilities of an in-house developed cost-effective eye-tracking system to track users' eye movement in an unconstrained environment while giving freedom of head movement. Human subject studies are conducted to compare operators' gaze patterns and the quality of data with that obtained using commercial eye trackers. The results generally agree with data quality obtained using commercial eye trackers. Hence, the performance of the developed eye-tracking system is comparable to existing commercial eye-trackers while overcoming their limitations, such as restricted user movement, single-user tracking, and high cost.