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Record W2164574606 · doi:10.1109/isspa.2012.6310603

Signal processing for low cost optical dataglove

2012· article· en· W2164574606 on OpenAlex
William Trottier-Lapointe, Lucas Majeau, Yahya El-Iraki, Sébastien Loranger, Guillaume Chabot-Nobert, Jonathan BORDUAS, Jonathan Lavoie, Jérôme Lapointe

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceTranslation (biology)DetectorSIGNAL (programming language)Cost reductionSign (mathematics)Sign languageSignal processingReduction (mathematics)Machine translationComputer hardwareArtificial intelligenceTelecommunicationsDigital signal processingProgramming language

Abstract

fetched live from OpenAlex

The PolyProject initiative goal is to produce a low cost dataglove for sign language translation by using an optical detector technology and a 3D positioning system. The main innovation here is the optical system used for the glove which allows a great cost reduction. The glove also makes sign language translation much more accessible. In this article, we describe the optical signal analysis as well as the 3D positioning. These two elements will lead to the demonstration of the complete sign language translation methodology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.334

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)0.0000.000

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 head0.045
GPT teacher head0.298
Teacher spread0.253 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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