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Record W2523335267 · doi:10.11159/mhci16.103

Getting Back To Basics: Bimanual Interaction on Mobile Touch Screen Devices

2016· article· en· W2523335267 on OpenAlexvenueno aff
G. Bryan Young, Andrew Wodehouse, Marion C. Sheridan

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2016
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceHuman–computer interactionMobile interactionMobile deviceMulti-touchMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

The availability, and popularity, of touch screen tablets is drastically increasing with over 30% of internet users now owning one. However the lack of bimanual interaction in touch screen tablets is presenting product designers with serious challenges. Several attempts have been made to facilitate bimanual interaction in such products but results are not comparable to that of their nonmobile cousins, e.g. laptops. This paper presents the finding of a group collaboration aimed at prototyping a mobile touch screen device which supports bimanual interaction, during internet browser navigation, through rear mounted inputs. The researchers found it problematic to add basic bimanual interactions for internet browser navigation to the rear of a prototype mobile touch screen device due to issues regarding grip type, finger movement and hand position. This paper concludes that in order to achieve high-quality bimanual interaction researchers need to re-examine these fundamental ergonomic aspects which hinder ease of use and consider how to free the hand and fingers from current constraints prior to the implementation of bimanual interaction.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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 designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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