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Record W4296784305 · doi:10.1145/3546736

Understanding and Adapting Bezel-to-Bezel Interactions for Circular Smartwatches in Mobile and Encumbered Scenarios

2022· article· en· W4296784305 on OpenAlexaff
Bradley Rey, Kening Zhu, Simon T. Perrault, Sandra Bardot, Ali Neshati, Pourang Irani

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2022
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsOkanagan University CollegeUniversity of WaterlooKelowna General HospitalUniversity of ManitobaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsSmartwatchComputer scienceHuman–computer interactionGestureSet (abstract data type)Control (management)Task (project management)Position (finance)Artificial intelligenceEmbedded systemWearable computerEngineering

Abstract

fetched live from OpenAlex

Supporting eyes-free interaction, mobility and encumbrance, while providing a broad set of commands on a smartwatch display is a difficult, yet important, task. Bezel-to-bezel (B2B) gestures are valuable for rapid command invocation during eyes-free operation, however we lack knowledge regarding B2B interactions on circular devices during common usage scenarios. We aim to improve our understanding of the dynamics of B2B interactions in these scenarios by conducting two studies and a third analysis: First, we explore the performance of B2B in a seated position; second, we explore the effect of mobility and encumbrance on the B2B interaction; finally, we improve on the B2B accuracies by calculating features and utilizing machine learning. With the limited interaction capabilities on smartwatches and the importance of the scenario of use, we conclude with applications and design guidelines for improved utilization of B2B that enables effective smartwatch control while in common, mobile and eyes-free scenarios.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.763

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 science0.0010.002
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 head0.121
GPT teacher head0.323
Teacher spread0.203 · 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 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

Citations10
Published2022
Admission routes1
Has abstractyes

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