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Record W3096395999 · doi:10.1145/3427334

Expanding Side Touch Input on Mobile Phones

2020· article· en· W3096395999 on OpenAlexaff
Yen-Ting Yeh, Quentin Roy, Antony Albert Raj Irudayaraj, Daniel Vogel

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2020
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThumbIndex fingerComputer scienceEnhanced Data Rates for GSM EvolutionComputer visionArtificial intelligenceTappingAcousticsMedicineAnatomyPhysics

Abstract

fetched live from OpenAlex

We investigate the performance of one-handed touch input on the side of a mobile phone. A first experiment examines grip change and subjective preference when reaching for side targets using different fingers. Results show all locations can be reached with at least one finger, but the thumb and index are most preferred and require less grip change for positions along the sides. Two following experiments examine taps and flicks using the thumb and index finger in a new two-dimensional input space. A side-touch sensor is simulated with a combination of capacitive sensing and motion tracking to distinguish touches on the lower, middle, or upper edges. When tapping, index and thumb speeds are similar with thumb more accurate and comfortable, and the lower edge is most reliable with the middle edge most comfortable. When flicking with the thumb, the upper edge is fast and rated highly.

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)0.000
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.053
GPT teacher head0.321
Teacher spread0.268 · 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
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

Citations7
Published2020
Admission routes1
Has abstractyes

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