A Review on Opportunities and Challenges of Machine Learning and Deep Learning for Eye Movements Classification

Fikri, Muhammad Ainul and Santosa, Paulus Insap and Wibirama, Sunu (2021) A Review on Opportunities and Challenges of Machine Learning and Deep Learning for Eye Movements Classification. In: 2021 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC), 20 - 21 October 2021, Yogyakarta.

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Abstract

Eye tracking has been used in touchless and assistive technologies to support disabled people as well as to provide more intuitive user interfaces. In this case, classification of events in eye tracking data is important to achieve higher object selection accuracy. Machine learning and deep learning have been used in events classification due to their ability to automatically learn patterns in eye tracking data. To the best knowledge of authors, however, there is no study that investigates opportunities and challenges on implementing various machine learning and deep learning techniques for events classification in eye tracking data. Here we present a systematical review to examine the use of machine learning and deep learning in events classification. We observed how machine learning and deep learning were used in development of reliable eye movements classification. At the same time, we summarized various challenges faced by previous researchers. In future, this paper may be used as a reference for entry level researchers interested in applying machine learning and deep learning for events classification in eye tracking data. © 2021 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 6
Uncontrolled Keywords: Classification (of information); Deep learning; Eye movements; User interfaces; Assistive technology; Deep learning; Disabled people; Events classification; Events detection; Eye movement classifications; Eye-tracking; Machine-learning; Touchless; Tracking data; Eye tracking
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TK Electrical engineering. Electronics Nuclear engineering > Electronics
T Technology > TK Electrical engineering. Electronics Nuclear engineering > Electronics > Computer engineering. Computer hardware
Divisions: Faculty of Engineering > Electronics Engineering Department
Depositing User: Sri JUNANDI
Date Deposited: 23 Sep 2024 07:28
Last Modified: 23 Sep 2024 07:28
URI: https://ir.lib.ugm.ac.id/id/eprint/4749

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