MétaCan
Menu
Back to cohort
Record W2136085151 · doi:10.1109/toh.2009.31

Touch Is Everywhere: Floor Surfaces as Ambient Haptic Interfaces

2009· article· en· W2136085151 on OpenAlexafffund
Yon Visell, Alvin Law, Jeremy R. Cooperstock

Bibliographic record

VenueIEEE Transactions on Haptics · 2009
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersEuropean Social FundMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsHaptic technologyHuman–computer interactionComputer scienceInterface (matter)PhraseSimulationArtificial intelligence

Abstract

fetched live from OpenAlex

Floor surfaces are notable for the diverse roles that they play in our negotiation of everyday environments. Haptic communication via floor surfaces could enhance or enable many computer-supported activities that involve movement on foot. In this paper, we discuss potential applications of such interfaces in everyday environments and present a haptically augmented floor component through which several interaction methods are being evaluated. We describe two approaches to the design of structured vibrotactile signals for this device. The first is centered on a musical phrase metaphor, as employed in prior work on tactile display. The second is based upon the synthesis of rhythmic patterns of virtual physical impact transients. We report on an experiment in which participants were able to identify communication units that were constructed from these signals and displayed via a floor interface at well above chance levels. The results support the feasibility of tactile information display via such interfaces and provide further indications as to how to effectively design vibrotactile signals for them.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.040
GPT teacher head0.293
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

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

Citations94
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on HapticsSame topicTactile and Sensory InteractionsFrench-language works237,207