Cognitive activity recognition by analyzing eye movement with convolutional neural networks

Bachelor thesis (2022)

Authors

B.J. Brockbernd Electrical Engineering, Mathematics and Computer Science

Contributors

G. Lan Embedded Systems - EEMCS (supervisor 1)
L. Du Embedded Systems - EEMCS (supervisor 1)
M.T.J. Spaan Algorithmics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Bob Brockbernd
CNN Human Activity Recognition Gaze bazed activity recognition
More Info
expand_more

Published Date

23-06-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This research proposes a novel method to classify cognitive behavior based on eye-movement data. Most state-of-the-art approaches use conventional machine learning techniques needing manual feature extraction. This experiment explores the possibility of applying deep learning algorithms to cognitive activity recognition for feature extraction and classification of eye-movement data. Convolutional neural networks will be explored in particular. Two neural networks are proposed and optimized using hyperparameter tuning. This research shows that convolutional neural networks can indeed perform cognitive activity recognition. Some neural networks significantly outperform the state-of-the-art methods for known subjects. However, further research is needed to improve performance in classifying activities for unknown subjects.