Azizinezhad, Parastoo and Ghonchi, Hamidreza and Chowdhury, Anirban (2024) Pupil Diameter Classification using Machine Learning During Human-Computer Interaction. In: IEEE International Conference on Omni-Layer Intelligent Systems, 2024-07-29 - 2024-07-31, London. (In Press)
Azizinezhad, Parastoo and Ghonchi, Hamidreza and Chowdhury, Anirban (2024) Pupil Diameter Classification using Machine Learning During Human-Computer Interaction. In: IEEE International Conference on Omni-Layer Intelligent Systems, 2024-07-29 - 2024-07-31, London. (In Press)
Azizinezhad, Parastoo and Ghonchi, Hamidreza and Chowdhury, Anirban (2024) Pupil Diameter Classification using Machine Learning During Human-Computer Interaction. In: IEEE International Conference on Omni-Layer Intelligent Systems, 2024-07-29 - 2024-07-31, London. (In Press)
Abstract
The significance of eye tracking and pupillary responses spans various disciplines, especially for people with severe disabilities. Effortful decision-making is marked by pupil dilation, reflecting increased cognitive load, and can be used as a potential measure for system adaptation to users' mental states. This study investigates the classification of pupil diameter data to differentiate between decision-making and focus time in a mobile robot navigation task. Data were collected from 19 healthy participants utilizing an eye-tracking-based user interface to control the robot's movements along pre-set paths. This paper presents a deep learning and SVM-based classification approach to distinguish focus and decision-making from pupil diameter patterns, offering insights for achieving an adaptive command selection approach. On average, the proposed deep learning model has an average accuracy of over 82\% in classifying the data for participants using pupil diameter data.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Published proceedings: _not provided_ |
Uncontrolled Keywords: | Eye-tracking; Human-computer Interaction; Pupil Diameter |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Oct 2024 12:20 |
Last Modified: | 30 Oct 2024 17:41 |
URI: | http://repository.essex.ac.uk/id/eprint/38590 |
Available files
Filename: PupilDiameterClassification_COINS2024_acceptedVersion.pdf