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.
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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) |
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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 > Electrical and Information Technology 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 |