AR-Eye: A Custom, Low-Cost Eye Tracker for Mental Fatigue Detection with Pattern-Based Machine Learning on Augmented Reality Headsets

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dc.contributor.authorStavroulakis, Alexiosen_US
dc.contributor.authorRoumeliotis, Michailen_US
dc.contributor.authorSafranoglou, Ioannisen_US
dc.contributor.authorRamiotis, Georgeen_US
dc.contributor.authorMania, Katerinaen_US
dc.contributor.editorJorge, Joaquim A.en_US
dc.contributor.editorSakata, Nobuchikaen_US
dc.date.accessioned2025-11-26T09:21:54Z
dc.date.available2025-11-26T09:21:54Z
dc.date.issued2025
dc.description.abstractWe present a low-cost, custom-built eye tracking system designed as a modular add-on for augmented reality (AR) headsets that either lack native eye tracking capabilities or provide insufficient ocular data for cognitive monitoring. Our system leverages infrared-enabled cameras and machine learning techniques to extract oculometric features-pupil diameter, blink dynamics, and saccadic velocity-in real time, enabling reliable detection of user mental fatigue. Unlike commercial high-cost solutions or restricted APIs of built-in AR trackers, the proposed device provides direct access to raw eye metrics under diverse conditions. We implement an unsupervised clustering approach combined with supervised classifiers to estimate fatigue levels on a persecond basis, integrating both self-reports and objective eye tracking data. Results are streamed into the AR environment, where individual and team-wide fatigue states are visualized through intuitive holographic overlays. This collaborative monitoring framework enables users to track both their own and others' cognitive states, supporting adaptive interventions in shared AR tasks. The system demonstrates the feasibility of affordable, portable, and extensible ocular-based fatigue detection for enhancing performance, safety, and well-being in group-oriented AR applications.en_US
dc.description.sectionheadersInterfaces
dc.description.seriesinformationICAT-EGVE 2025 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments
dc.identifier.doi10.2312/egve.20251350
dc.identifier.isbn978-3-03868-278-3
dc.identifier.issn1727-530X
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/egve.20251350
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egve20251350
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Mixed / augmented reality; Collaborative interaction; Hardware → Sensor devices and platforms; Computer systems organization → Real-time systems; Computing methodologies → Machine learning; Computer vision
dc.subjectHuman centered computing → Mixed / augmented reality
dc.subjectCollaborative interaction
dc.subjectHardware → Sensor devices and platforms
dc.subjectComputer systems organization → Real
dc.subjecttime systems
dc.subjectComputing methodologies → Machine learning
dc.subjectComputer vision
dc.titleAR-Eye: A Custom, Low-Cost Eye Tracker for Mental Fatigue Detection with Pattern-Based Machine Learning on Augmented Reality Headsetsen_US
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