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Continuous assessment of task difficulty and mental workload is essential inimproving the usability and accessibility of interactive systems. Eye trackingdata has often been investigated to achieve this ability, with reports on thelimited role of standard blink metrics. Here, we propose a new approach to theanalysis of eye-blink responses for automated estimation of task difficulty.The core module is a time-frequency representation of eye-blink, which aims tocapture the richness of information reflected on blinking. In our first study,we show that this method significantly improves the sensitivity to taskdifficulty. We then demonstrate how to form a framework where the representedpatterns are analyzed with multi-dimensional Long Short-Term Memory recurrentneural networks for their non-linear mapping onto difficulty-relatedparameters. This framework outperformed other methods that used hand-engineeredfeatures. This approach works with any built-in camera, without requiringspecialized devices. We conclude by discussing how Rethinking Eye-blink canbenefit real-world applications.

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking