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Record W2401480642 · doi:10.20380/gi2015.26

Hands, hover, and nibs: understanding stylus accuracy on tablets

2015· article· en· W2401480642 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

VenueCanada Human-Computer Communications Society · 2015
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStylusComputer scienceHuman–computer interactionComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

Although tablets and styli have become pervasive, styli have not seen widespread adoption for precise input tasks such as annotation, note-taking, algebra, and so on. While many have identified that stylus accuracy is a problem, there is still much unknown about how the user and the stylus itself influences accuracy. The present work identifies a multitude of factors relating to the user, the stylus, and tablet hardware that impact the inaccuracy experienced today. Further, we report on a two-part user study that evaluated the interplay between the motor and visual systems (i.e., hand posture and visual feedback) and an increasingly important feature of the stylus, the nib diameter. The results determined that the presence of visual feedback and the dimensions of the stylus nib are crucial to the accuracy attained and pressure exerted with the stylus. The ability to rest one's hand on the screen, while providing comfort and support, was found to have surprisingly little influence on accuracy.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.991

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.120
GPT teacher head0.300
Teacher spread0.181 · 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