Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking
This work addresses the need for better usability and accessibility in interactive systems by providing a more accurate method for estimating task difficulty using standard cameras, though it appears incremental in improving existing eye-tracking techniques.
The paper tackled the problem of continuous assessment of task difficulty and mental workload by proposing a new approach using time-frequency representation of eye-blink data, which significantly improved sensitivity to task difficulty and outperformed methods using hand-engineered features.
Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.