Cognitive workload level estimation based on eye tracking: A machine learning approach

Abstract

Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.

Towards an ontology to capture human attributes in human-robot collaboration

“…Human metrics, such as cognitive and physical load, stress, and other emotional indicators, can gain insight into how the human may behave and what their capabilities may be for certain tasks. Cognitive load in relation to performing a task is the amount of mental effort or processing capacity (Skaramagkas et al, 2021). This is commonly measured using self-reporting subjective measures or from physiological data such as heart rate variability (HRV), eye blink rate, skin conductance and electroencephalography (EEG).…”