Mental fatigue prediction during eye-typing

Tanya Bafna*, Per Bækgaard, John Paulin Hansen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Mental fatigue is a common problem associated with neurological disorders. Until now, there has not been a method to assess mental fatigue on a continuous scale. Camera-based eye-typing is commonly used for communication by people with severe neurological disorders. We designed a working memory-based eye-typing experiment with 18 healthy participants, and obtained eye-tracking and typing performance data in addition to their subjective scores on perceived effort for every sentence typed and mental fatigue, to create a model of mental fatigue for eye-typing. The features of the model were the eye-based blink frequency, eye height and baseline-related pupil diameter. We predicted subjective ratings of mental fatigue on a six-point Likert scale, using random forest regression, with 22% lower mean absolute error than using simulations. When additionally including task difficulty (i.e. the difficulty of the sentences typed) as a feature, the variance explained by the model increased by 9%. This indicates that task difficulty plays an important role in modelling mental fatigue. The results demonstrate the feasibility of objective and non-intrusive measurement of fatigue on a continuous scale.

Original languageEnglish
Article numbere0246739
JournalPLOS ONE
Volume16
Issue number2
Number of pages17
ISSN1932-6203
DOIs
Publication statusPublished - Feb 2021

Bibliographical note

Publisher Copyright:
© 2021 Bafna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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