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Record W3030277019 · doi:10.1145/3396339.3396363

User gesture elicitation of common smartphone tasks for hand proximate user interfaces

2020· article· en· W3030277019 on OpenAlexafffund
Shariff AM Faleel, Michael Gammon, Yumiko Sakamoto, Carlo Menon, Pourang Irani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsSimon Fraser UniversityUniversity of Manitoba
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsGestureComputer scienceHuman–computer interactionSet (abstract data type)Context (archaeology)Gesture recognitionUser interfaceInterface (matter)Space (punctuation)Artificial intelligence

Abstract

fetched live from OpenAlex

The ubiquity of smartphone interactions along with the advancements made in mixed reality applications and gesture recognition present an intriguing space for novel interaction techniques using the hand as an interface. This paper explores the idea of using hand proximate user interfaces (UI), i.e. interactions with and display of interface elements on and around the hand. We conducted two user studies to gain a better understanding of the design space for such interactions. The first study identifies the possible ways in which various elements can be displayed on and around the hand in the context of common smartphone applications. We conduct a second study to build a gesture set for interactions with elements displayed on and around the hand. We contribute an analysis of the data and observations collected from the two studies, resulting in a layout set and a gesture set for interactions with hand proximate UIs.

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)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.278
Teacher spread0.256 · 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 designObservational
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

Citations19
Published2020
Admission routes2
Has abstractyes

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