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Record W1970924458 · doi:10.1145/2441776.2441796

KinectArms

2013· article· en· W1970924458 on OpenAlex
Aaron Genest, Carl Gutwin, Anthony Tang, Michael R. Kalyn, Zenja Ivkovic

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
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of CalgaryUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGestureComputer scienceVisibilityUsabilityTable (database)Set (abstract data type)Human–computer interactionGesture recognitionArtificial intelligenceComputer visionMultimediaComputer graphics (images)Database

Abstract

fetched live from OpenAlex

Gestures are a ubiquitous part of human communication over tables, but when tables are distributed, gestures become difficult to capture and represent. There are several problems: extracting arm images from video, representing the height of the gesture, and making the arm embodiment visible and understandable at the remote table. Current solutions to these problems are often expensive, complex to use, and difficult to set up. We have developed a new toolkit - KinectArms - that quickly and easily captures and displays arm embodiments. KinectArms uses a depth camera to segment the video and determine gesture height, and provides several visual effects for representing arms, showing gesture height, and enhancing visibility. KinectArms lets designers add rich arm embodiments to their systems without undue cost or development effort, greatly improving the expressiveness and usability of distributed tabletop groupware.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.006

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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

Citations38
Published2013
Admission routes2
Has abstractyes

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