Improving Deep Learning-Based Eye Movements Classification Using Bayesian Optimization

Fristiana, Ayuningtyas Hari and Alfarozi, Syukron Abu Ishaq and Permanasari, Adhistya Erna and Wibirama, Sunu (2023) Improving Deep Learning-Based Eye Movements Classification Using Bayesian Optimization. In: 2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC), 09-10 November 2023, Yogyakarta.

[thumbnail of Improving_Deep_Learning-Based_Eye_Movements_Classification_Using_Bayesian_Optimization.pdf] Text
Improving_Deep_Learning-Based_Eye_Movements_Classification_Using_Bayesian_Optimization.pdf
Restricted to Registered users only

Download (874kB) | Request a copy

Abstract

Eye tracking technology has emerged as a touchless solution for gaze-based object selection, holding immense promise in the field of assistive technology. Despite its potential, accurately classifying various eye movements remains a formidable challenge, critical for dependable object selection. This paper introduces an innovative approach to enhance the performance of deep learning-based eye movements classification. We leveraged Bayesian Optimization (BO) to optimize the Temporal Convolutional Networks (TCNs), addressing a critical gap in prior research by optimizing hyperparameters. BO, a model-based optimization technique, efficiently explores the hyperparameter search space that leads to significant improvements in classification performance. To rigorously assess our approach, we conducted experiments on the GazeCom dataset, a rich resource annotated for diverse eye movements with a specific emphasis on smooth pursuit that is vital for calibration-free eye tracking applications. Using a lighter model, our approach significantly improved the classification of different types of eye movements - including fixation, saccade, and smooth pursuit. This result outperformed the baseline TCNs model by a margin of 1% to 7.21%. A notable improvement was observed in the classification result of smooth pursuit eye movement (F1 score: 0.8346). This achievement marks a decisive step toward refining the performance of assistive technology based on gaze interaction. Furthermore, our study can be used as a guide for future implementation of hyperparameters optimization in deep learning-based eye movements classification.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Library Dosen
Uncontrolled Keywords: Bayesian optimization, deep neural network, hyperparameters tuning, eye tracking, temporal convolutional network.
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering > Electronics Engineering Department
Depositing User: Rita Yulianti Yulianti
Date Deposited: 14 Aug 2024 02:24
Last Modified: 14 Aug 2024 02:24
URI: https://ir.lib.ugm.ac.id/id/eprint/70

Actions (login required)

View Item
View Item