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Record W4401910133 · doi:10.1145/3689434

HeadShift: Head Pointing with Dynamic Control-Display Gain

2024· article· en· W4401910133 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

VenueACM Transactions on Computer-Human Interaction · 2024
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of Toronto
FundersEuropean Commission
KeywordsHead (geology)Computer scienceAutomatic gain controlGeologyTelecommunications

Abstract

fetched live from OpenAlex

Head pointing is widely used for hands-free input in head-mounted displays (HMDs). The primary role of head movement in an HMD is to control the viewport based on absolute mapping of head rotation to the 3D environment. Head pointing is conventionally supported by the same 1:1 mapping of input with a cursor fixed in the centre of the view, but this requires exaggerated head movement and limits input granularity. In this work, we propose to adopt dynamic gain to improve ergonomics and precision, and introduce the HeadShift technique. The design of HeadShift is grounded in natural eye-head coordination to manage control of the viewport and the cursor at different speeds. We evaluated HeadShift in a Fitts’ Law experiment and on three different applications in VR, finding the technique to reduce error rate and effort. The findings are significant as they show that gain can be adopted effectively for head pointing while ensuring that the cursor is maintained within a comfortable eye-in-head viewing range.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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