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Record W2752356709 · doi:10.1145/3098279.3098561

Designing a gaze gesture guiding system

2017· preprint· en· W2752356709 on OpenAlexafffund
William Delamare, Teng Han, Pourang Irani

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2017
Admission routes2
Has abstractyes

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