Machine Learning in Gaze-Based Interaction: A Survey of Eye Movements Events Detection

Nuraini, Annis and Murnani, Suatmi and Ardiyanto, Igi and Wibirama, Sunu (2021) Machine Learning in Gaze-Based Interaction: A Survey of Eye Movements Events Detection. In: 2021 International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), 20-21 October 2021, Banda Aceh.

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Abstract

Spontaneous gaze-based input offers faster and more intuitive human-computer interaction as people naturally look at their desired destination. Developing a spontaneous gaze-based application faces many challenges, one of them is detecting events of eye movements. Most events detection methods are based on velocity or dispersion threshold. Unfortunately, these approaches depend on manual setting of threshold parameters. Despite previous attempts to review various techniques used in spontaneous gaze-based interaction, there is no survey paper that takes into account various machine learning techniques used for events detection. Here we present a brief overview of spontaneous gaze-based interactive applications and some machine learning approaches used for eye movement events detection. First, we explored major development of spontaneous gaze-based interaction by reviewing some examples of its applications. Next, we presented some state-of-the-art of threshold-based and machine learning-based events detection techniques. Finally, we discussed future research on spontaneous gaze-based interaction. Our brief survey paper maybe used by entry level researchers interested in developing uncalibrated interactive applications based on eye tracking. © 2021 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 3
Uncontrolled Keywords: Eye movements; Human computer interaction; Interactive devices; Machine learning; Surveys; Detection methods; Events detection; Eye-tracking; Gaze-based interaction; Interactive applications; Machine learning approaches; Machine learning techniques; Machine-learning; Smooth pursuit; Threshold parameters; Eye tracking
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Electrical and Information Technology Department
Depositing User: Sri JUNANDI
Date Deposited: 25 Oct 2024 00:40
Last Modified: 25 Oct 2024 00:40
URI: https://ir.lib.ugm.ac.id/id/eprint/8612

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