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Record W1971906315 · doi:10.1109/haptics.2014.6775476

Improvising design with a Haptic Instrument

2014· article· en· W1971906315 on OpenAlexafffund
Oliver Schneider, Karon E. MacLean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyTweakingComputer scienceHuman–computer interactionVisualizationImprovisationStereotaxyMultimediaSimulationArtificial intelligence

Abstract

fetched live from OpenAlex

As the need to deploy informative, expressive haptic phenomena in consumer devices gains momentum, the inadequacy of current design tools is becoming more critically obstructive. Current tools do not support collaboration or serendipitous exploration. Collaboration is critical, but direct means of sharing haptic sensations are limited, and the absence of unifying conceptual models for working with haptic sensations further restricts communication between designers and stakeholders. This is especially troublesome for pleasurable, affectively targeted interactions that rely on subjective user experience. In this paper, we introduce an alternative design approach inspired by musical instruments - a new tool for real-time, collaborative manipulation of haptic sensations; and describe a first example, mHIVE, a mobile Haptic Instrument for Vibrotactile Exploration. Our qualitative study shows that mHIVE supports exploration and communication but requires additional visualization and recording capabilities for tweaking designs, and expands previous work on haptic language.

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: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.303

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 head0.055
GPT teacher head0.261
Teacher spread0.206 · 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

Citations47
Published2014
Admission routes2
Has abstractyes

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