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Record W2131092780 · doi:10.1109/vr.2006.129

The Role of Prototyping Tools for Haptic Behavior Design

2006· article· en· W2131092780 on OpenAlexaff
Colin Swindells, E. Maksakov, Karon E. MacLean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHaptic technologyComputer scienceAffordanceHuman–computer interactionVariety (cybernetics)Representation (politics)StereotaxyRapid prototypingSimulationArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

We describe key affordances required by tools for developing haptic behaviors. Haptic icon design involves the envisioning, expression and iterative modification of haptic behavior representations. These behaviors are then rendered on a haptic device. For example, a sinusoidal force vs. position representation rendered on a haptic knob would produce the feeling of detents. Our contribution is twofold. We introduce a custom haptic icon prototyper that includes novel interaction features, and we then use the lessons learnt from its development plus our experiences with a variety of haptic devices to present and argue high-level design choices for such prototyping tools in general.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.005
Scholarly communication0.0040.007
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.075
GPT teacher head0.305
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2006
Admission routes1
Has abstractyes

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