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

Cho, Y; (2021) Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking. In: CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. (pp. p. 721). ACM Green open access

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Abstract

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.

Type: Proceedings paper
Title: Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking
Event: CHI Conference on Human Factors in Computing Systems (CHI '21)
Dates: 08 May 2021 - 13 May 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3411764.3445577
Publisher version: https://doi.org/10.1145/3411764.3445577
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10121730
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