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Record W2930507225 · doi:10.22215/etd/2018-12994

Empirical Studies on Selection and Travel Performance of Eye-tracking in Virtual Reality

2018· dissertation· en· W2930507225 on OpenAlexaff
Yuanyuan Qian

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsEye trackingSelection (genetic algorithm)Eye movementOptical head-mounted displayTask (project management)Computer scienceHead (geology)Eye tracking on the ISSVirtual realityArtificial intelligenceComputer visionSimulationHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

We presented two studies on VR selection and travel performances using eyebased interaction via FOVE head-mounted display (HMD). Our selection experiment was modelled after the ISO 9241-9 reciprocal selection task, with targets presented at varying depths in a custom virtual environment. We compared eye-based and head-based in isolation, and the combination of eye-tracking and head-tracking. Results indicate that eye-only offered the worst performance in terms of error rate, selection times, and throughput. Head-only offered significantly better performance. In our travel study, the task involved controlling movement direction while flying through target rings in the air by seven techniques. We found that the completion time and success rates of head+eye were very close to head-only, while eye-only did not perform better than head+eye due to learning effects and calibration issues, which also yield high cybersickness. Head+eye compensated for the eye-tracker issues and would be potentially an alternative to traditional traveling 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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.053
GPT teacher head0.379
Teacher spread0.326 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2018
Admission routes1
Has abstractyes

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