Evidence map›Paper›PMID 42132462›Full record

ArticleInvestigative ophthalmology & visual science2026

Detection of Visual Fatigue Using Eye Tracking and a Wearable Device.

Julien Guégan, Noé Hirschauer, Qi Xu, Astrid Lassalle

Abstract read
In one paragraph

Article in Investigative ophthalmology & visual science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Julien GuéganEssilorLuxottica R&D, Paris, France.
Noé HirschauerEssilorLuxottica R&D, Paris, France.
Qi XuEssilorLuxottica R&D, Paris, France.
Astrid LassalleEssilorLuxottica R&D, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Objective assessment of visual fatigue remains challenging in contemporary research. This study explored the objective markers of visual fatigue using conventional eye-tracking and an innovative spectacle frame equipped with photodiodes and infrared emitters. Methods: Sixty participants underwent a 30-minute reading task designed to induce visual fatigue. Subjective visual discomfort was assessed using three questionnaires: DESQ (Digital Eye Strain Questionnaire), OSDI (Ocular Surface Disease Index), and SSQVF (Subjective Symptom Questionnaire for Visual Fatigue) before and after the task. Eye movement parameters were recorded using an Eyelink 1000 Plus eye-tracker, while a prototype spectacle frame with nine photodiodes measured periocular region changes. A time series transformer model was trained to classify visual fatigue states using signals from both the Eyelink tracker and the photodiode-equipped frame. Results: Subjective visual discomfort increased significantly across all questionnaires following the reading task. Several eye movement parameters showed significant changes: saccade amplitude and velocity increased, pupil size decreased, and blink duration increased. Correlations between objective measures and subjective reports were generally limited, with only fixation stability metrics (Bivariate Contour Ellipse Area and isoline area) showing significant correlations with SSQVF scores after correction for multiple comparisons. The time series transformer model achieved superior performance using photodiode data (82.5% user-level accuracy) compared with eye-tracking data (73.1% accuracy). Conclusions: Although traditional eye-tracking metrics showed limited correlation with subjective fatigue, with the exception of fixation stability measures, the photodiode-equipped wearable device demonstrated promising potential for objective visual fatigue detection. The superior performance of the photodiode signals suggests this technology could provide a practical alternative to laboratory-based eye-tracking for real-world fatigue monitoring applications.

Indexed as

AsthenopiaEye MovementsEye-Tracking TechnologyWearable Electronic DevicesAdultEyeglassesFemaleHumansMaleReadingSurveys and QuestionnairesYoung Adult

Identifiers

PMID42132462
PMCPMC13196828

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.