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Record W2752356709 · doi:10.1145/3098279.3098561

Designing a gaze gesture guiding system

2017· preprint· en· W2752356709 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsGestureGazeComputer scienceTask (project management)Modality (human–computer interaction)Human–computer interactionVocabularyComputer visionPresentation (obstetrics)Artificial intelligenceEngineering

Abstract

fetched live from OpenAlex

We propose the concept of a guiding system specifically designed for semaphoric gaze gestures, i.e. gestures defining a vocabulary to trigger commands via the gaze modality. Our design exploration considers fundamental gaze gesture phases: Exploration, Guidance, and Return. A first experiment reveals that Guidance with dynamic elements moving along 2D paths is efficient and resistant to visual complexity. A second experiment reveals that a Rapid Serial Visual Presentation of command names during Exploration allows for more than 30% faster command retrievals than a standard visual search. To resume the task where the guide was triggered, labels moving from the outward extremity of 2D paths toward the guide center leads to efficient and accurate origin retrieval during the Return phase. We evaluate our resulting Gaze Gesture Guiding system, G3, for interacting with distant objects in an office environment using a head-mounted display. Users report positively on their experience with both semaphoric gaze gestures and G3.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0040.003
Research integrity0.0010.001
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 head0.067
GPT teacher head0.282
Teacher spread0.214 · 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
Published2017
Admission routes2
Has abstractyes

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