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Record W2981619376 · doi:10.1145/3308561.3356111

Exploring Haptic Colour Identification Aids

2019· article· en· W2981619376 on OpenAlex

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
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIdentification (biology)Computer scienceArtificial intelligenceComputer visionPlan (archaeology)Haptic technologyPerceptionRGB color modelWristPsychologyMedicineGeography

Abstract

fetched live from OpenAlex

Colour identification is an important component of perception, but people with Colour Vision Deficiency (CVD) have trouble with colour identification tasks. Haptic colour identification aids exist but are slow to learn. To address this, we developed two new colour identification aids - ColourWrist and ColourVest. Colour-Wrist is a wrist-based aid that maps any single RGB input to 16 unique colour category patterns displayed on the wrist using four solenoids and a vibration motor. ColourVest is a back-based aid that uses a back-mounted 10 x 8 2D vibrotactile array to notify the user of the general location of a user-selected colour category (of 16 possible choices). We compared both tools to a control condition with two participants with CVD, and show that participants found ColourWrist and ColourVest "intuitive" and "useful". Based on participants' feedback, we next plan improvements for ColourWrist and ColourVest, as well as plan to study how they perform in real-world applications.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.985

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)0.0310.016

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 head0.081
GPT teacher head0.312
Teacher spread0.231 · 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
Published2019
Admission routes1
Has abstractyes

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