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Record W2621486358 · doi:10.1145/3064663.3064695

Exploring At-Your-Side Gestural Interaction for Ubiquitous Environments

2017· preprint· en· W2621486358 on OpenAlex
Shaishav Siddhpuria, Keiko Katsuragawa, James R. Wallace, Edward Lank

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaGoogle
KeywordsGestureComputer scienceHuman–computer interactionSet (abstract data type)AlphanumericGesture recognitionVocabularySpace (punctuation)Artificial intelligence

Abstract

fetched live from OpenAlex

Free-space gestural systems are faced with two major issues: a lack of subtlety due to explicit mid-air arm movements, and the highly effortful nature of such interactions. With an ever-growing ubiquity of interactive devices, displays, and appliances with non-standard interfaces, lower-effort and more socially acceptable interaction paradigms are essential. To address these issues, we explore at-one's-side gestural input. Within this space, we present the results of two studies that investigate the use of side-gesture input for interaction. First, we investigate end-user preference through a gesture elicitation study, present a gesture set, and validate the need for dynamic, diverse, and variable-length gestures. We then explore the feasibility of designing such a gesture recognition system, dubbed WatchTrace, which supports alphanumeric gestures of up to length three with an average accuracy of up to 82%, providing a rich, dynamic, and feasible gestural vocabulary.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.003
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 head0.214
GPT teacher head0.339
Teacher spread0.125 · 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

Quick stats

Citations26
Published2017
Admission routes2
Has abstractyes

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