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Record W2942570762 · doi:10.1145/3290607.3312752

The Effect of Rotational Jitter on 3D Pointing Tasks

2019· article· en· W2942570762 on OpenAlex
Anil Ufuk Batmaz, Wolfgang Stuerzlinger

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
TopicInteractive and Immersive Displays
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsJitterComputer scienceFitts's lawVirtual realityCursor (databases)Noise (video)SimulationControl theory (sociology)Computer visionArtificial intelligenceMovement (music)Control (management)AcousticsPhysics

Abstract

fetched live from OpenAlex

Even when in a static position, data acquired from 6 Degrees of Freedom (DoF) trackers is affected by noise, which is typically called jitter. In this study, we analyzed the effects of 3D rotational jitter on Virtual Reality (VR) controllers in a 3D Fitts' law experiment, which explored how such jitter affects user performance. Eight subjects performed a Fitts' law experiment with or without additional jitter on the cursor. Results show that while error rate significantly increased above ±0.5° jitter and subjects' effective throughput started to decrease significantly above ±1° jitter, there was no significant effect on users' movement time. Further, the Fitts's law movement time model was affected when ±2° jitter was applied to the tracker. According to these results, ±0.5° jitter on the controller does not significantly affect user performance for the tasks explored here. The results of our study can guide the design of 3D controller and tracking systems for 3D user interfaces.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.752

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)0.0000.001

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 head0.004
GPT teacher head0.239
Teacher spread0.235 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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