Publication
A review of machine learning in scanpath analysis for passive gaze-based interaction
Abdulrahman Mohamed Selim; Michael Barz; Omair Shahzad Bhatti; Hasan Md Tusfiqur Alam; Daniel Sonntag
In: Maria Chiara Caschera (Hrsg.). Frontiers in Artificial Intelligence (Front. Artif. Intell.), Vol. 7, Pages 1-28, Frontiers Media SA, Avenue du Tribunal-Fédéral 34 1005 Lausanne Switze, 6/2024.
Abstract
The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.
Projects
- MASTER - MASTER: Mixed reality ecosystem for teaching robotics in manufacturing
- No-IDLE - Interactive Deep Learning Enterprise