Engagement Analysis of ADHD Students using Visual Cues from Eye Tracker
Harshit Chauhan, Anmol Prasad, Jainendra Shukla · Companion Publication of the 2020 International Conference on Multimodal Interaction · 2020
In this paper, we focus on finding the correlation between visual attention and engagement of ADHD students in one-on-one sessions with specialized educators using visual cues and eye-tracking data. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion in the context of ADHD affected children. This is a novel problem since no previous studies have aimed to identify markers of attentiveness in the context of students affected with ADHD. The experiment has been designed to collect data in a controlled environment and later on can be used to generate Machine Learning models to assist real-world educators. Additionally, we propose a novel approach for AOI (Area of Interest) detection for eye-tracking analysis in dynamic scenarios using existing deep learning-based saliency prediction and fixation prediction models. We aim to use the processed data to extract the features from a subject's eye-movement patterns and use Machine Learning models to classify the attention levels.