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Record W2761760772 · doi:10.1145/3131785.3131800

Designing Vibrotactile Widgets with Printed Actuators and Sensors

2017· preprint· en· W2761760772 on OpenAlexfundno aff
Christian Frisson, Julien Decaudin, Thomas Pietrzak, Alexander Ng, Pauline Poncet, F. Casset, Antoine Latour, Stephen Brewster

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
FundersH2020 LEIT Information and Communication TechnologiesHorizon 2020Horizon 2020 Framework ProgrammeUniversity of WaterlooEuropean Commission
KeywordsUSableActuatorCapacitive sensingHaptic technologyWearable computerComputer scienceGesture3d printedInkwellElectrical conductorMechanical engineeringEngineeringHuman–computer interactionElectrical engineeringSimulationArtificial intelligenceEmbedded systemMultimediaBiomedical engineering

Abstract

fetched live from OpenAlex

Physical controls are fabricated through complicated assembly of parts requiring expensive machinery and are prone to mechanical wear. One solution is to embed controls directly in interactive surfaces, but the proprioceptive part of gestural interaction that makes physical controls discoverable and usable solely by hand gestures is lost and has to be compensated, by vibrotactile feedback for instance. Vibrotactile actuators face the same aforementioned issues as for physical controls. We propose printed vibrotactile actuators and sensors. They are printed on plastic sheets, with piezoelectric ink for actuation, and with silver ink for conductive elements, such as wires and capacitive sensors. These printed actuators and sensors make it possible to design vibrotactile widgets on curved surfaces, without complicated mechanical assembly.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.053
GPT teacher head0.301
Teacher spread0.248 · 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.

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

Citations13
Published2017
Admission routes1
Has abstractyes

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