Mental fatigue prediction during eye-typing

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.

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

“…Previous studies have also used machine learning to analyze alternative fatigue detection methods based on new technologies. For instance, biological features extracted with EEG, electro-oculogram, or heart rate, and physical features such as yawning, drowsiness, or slow eye movements [45]. These approaches may be especially useful in the driving and occupational fields to reduce risks and improve workers' health and well-being [46].…”

Detection and Recuperation of Mental Fatigue

“…Pupil diameter (PD) is another eye measurement which can be used to detect workload and mental fatigue. As mental fatigue increases the pupil diameter decreases with respect to baseline measurements [20]. While pupil diameter is also sensitive to changes in arousal and fatigue, it is also sensitive to changes in mental workload.…”

Interoceptive and metacognitive facets of fatigue in multiple sclerosis

“…While numerous studies exist which, in a variety of contexts and disorders, used machine learning to predict individual fatigue levels from behavioural or physiological data (Baykaner et al, 2015;Mun & Geng, 2019;Luo et al, 2020;Bafna et al, 2021;Jiang et al, 2021;Pinto-Bernal et al, 2021;Yao et al, 2021;Zeng et al, 2021), only two of these studies have concerned MS (Ibrahim et al, 2020;.…”

“…While numerous studies exist which, in a variety of contexts and disorders, used machine learning to predict individual fatigue levels from behavioural or physiological data (Baykaner et al ., 2015; Mun & Geng, 2019; Luo et al ., 2020; Bafna et al ., 2021; Jiang et al ., 2021; Pinto-Bernal et al ., 2021; Yao et al ., 2021; Zeng et al ., 2021), only two of these studies have concerned MS (Ibrahim et al ., 2020; 2022). Additionally, like the vast majority of studies, these two MS-specific studies did not actually predict fatigue (the subjective experience) but predict fatiguability (the observable decrease in performance during physiologically or cognitively demanding tasks) (Kluger et al, 2013) (sometimes, fatigue and fatiguability are also referred to as “trait fatigue” and “state fatigue”, respectively (Cehelyk et al ., 2019)).…”