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Record W4383681519 · doi:10.1145/3563657.3596032

Affordance-Based and User-Defined Gestures for Spatial Tangible Interaction

2023· article· en· W4383681519 on OpenAlexaff
Weilun Gong, Stephanie Santosa, Tovi Grossman, Michael Glueck, Daniel Clarke, Frances Lai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGestureAffordanceHuman–computer interactionComputer scienceObject (grammar)Set (abstract data type)Interface (matter)Augmented realityRepurposingArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Although mid-air hand gestures have been widely adopted by VR/AR products (e.g., Quest 2 and HoloLens), some drawbacks remain due to their lack of tangibility and tactile feedback. Opportunistic Tangible User Interfaces could address these shortcomings by repurposing existing objects in one's physical environment. However, there has yet to be a systematic investigation of the gestures that would be desirable when using opportunistic objects or how such gestures would be impacted by such objects. In this work, we conducted an elicitation study to investigate the desirability of object and gesture combinations across a variety of interactions. The results contribute (1) an opportunistic tangible UI gesture set for spatial interfaces, and (2) an Affordance-Based Object Selector Scheme that identifies ideal objects for tangible input given a desired input gesture, based on that object's physical affordances. Arising from these findings is the vision of the Adaptive Tangible User Interface, which supports the on-the-fly composition of tangible interfaces based on the affordances found in the physical environment and a user's input task.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.299

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 communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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