ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning
This work addresses the problem of monitoring student engagement in e-learning platforms using non-invasive face analysis, though it is incremental as it builds on existing blink detection and attention estimation research.
The study investigated the relationship between eye blink frequency and student attention levels during online learning, finding an inverse correlation and proposing a method (ALEBk) that estimates attention as the inverse of blink frequency.
This work presents a feasibility study of remote attention level estimation based on eye blink frequency. We first propose an eye blink detection system based on Convolutional Neural Networks (CNNs), very competitive with respect to related works. Using this detector, we experimentally evaluate the relationship between the eye blink rate and the attention level of students captured during online sessions. The experimental framework is carried out using a public multimodal database for eye blink detection and attention level estimation called mEBAL, which comprises data from 38 students and multiples acquisition sensors, in particular, i) an electroencephalogram (EEG) band which provides the time signals coming from the student's cognitive information, and ii) RGB and NIR cameras to capture the students face gestures. The results achieved suggest an inverse correlation between the eye blink frequency and the attention level. This relation is used in our proposed method called ALEBk for estimating the attention level as the inverse of the eye blink frequency. Our results open a new research line to introduce this technology for attention level estimation on future e-learning platforms, among other applications of this kind of behavioral biometrics based on face analysis.