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Record W2767031149 · doi:10.1145/3126594.3126598

HapticClench

2017· article· en· W2767031149 on OpenAlexafffund
Aakar Gupta, Antony Albert Raj Irudayaraj, Ravin Balakrishnan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer sciencePerceptionComputer visionArtificial intelligenceHuman–computer interactionPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Squeezing sensations are one of the most common and intimate forms of human contact. In this paper, we investigate HapticClench, a device that generates squeezing sensations using shape memory alloys. We define squeezing feedback in terms of it perceptual properties and conduct a psychophysical evaluation of HapticClench. HapticClench is capable of generating up to four levels of distinguishable load and works well in distracted scenarios. HapticClench has a high spatial acuity and can generate spatial patterns on the wrist that the user can accurately recognize. We also demonstrate the use of HapticClench for communicating gradual progress of an activity, and for generating squeezing sensations using rings and loose bracelets.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.573
Threshold uncertainty score1.000

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)0.0010.002

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 head0.101
GPT teacher head0.346
Teacher spread0.246 · 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; both teacher heads agree on what is shown here.

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

Citations66
Published2017
Admission routes2
Has abstractyes

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