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Record W3042439002 · doi:10.1109/hsi49210.2020.9142677

FELiX: Fixation-based Eye Fatigue Load Index A Multi-factor Measure for Gaze-based Interactions

2020· article· en· W3042439002 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsConcordia University
Fundersnot available
KeywordsEye trackingGazeComputer scienceFixation (population genetics)Dwell timeArtificial intelligenceEye tracking on the ISSMeasure (data warehouse)Eye movementComputer visionTracking (education)PsychologyData miningMedicine

Abstract

fetched live from OpenAlex

Eye fatigue is a common challenge in eye tracking applications caused by physical and/or mental triggers. Its impact should be analyzed in eye tracking applications, especially for the dwell-time method. As emerging interaction techniques become more sophisticated, their impacts should be analyzed based on various aspects. We propose a novel compound measure for gaze-based interaction techniques that integrates subjective NASA TLX scores with objective measurements of eye movement fixation points. The measure includes two variations depending on the importance of (a) performance, and (b) accuracy, for measuring potential eye fatigue for eye tracking interactions. These variations enable researchers to compare eye tracking techniques on different criteria. We evaluated our measure in two user studies with 33 participants and report on the results of comparing dwell-time and gaze-based selection using voice recognition techniques.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.318
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations19
Published2020
Admission routes1
Has abstractyes

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