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Record W2346281211 · doi:10.1109/haptics.2016.7463157

Toward open-source portable haptic displays with visual-force-tactile feedback colocation

2016· article· en· W2346281211 on OpenAlexafffund
Colin Gallacher, Arash Mohtat, Steve Ding, József Kövecses

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologies
KeywordsHaptic technologyComputer scienceTactile displayComputer graphics (images)Visual feedbackOpen sourceComputer visionHuman–computer interactionArtificial intelligenceSoftwareOperating system

Abstract

fetched live from OpenAlex

Platforms capable of generating rich haptic feedback are usually quite expensive and under strict proprietary protection. In addition, most of them are not portable and do not deliver a fully integrated force, tactile and visual user experience. In an attempt to break through these limitations, we have created the Haplet: an open-source, portable and affordable haptic device with colocated visual, force and tactile feedback. The triple colocated user experience is delivered by a small-form parallel robotic mechanism that features a simple vibrotactile actuator at the end-effector. The robotic arm is optically transparent and is motorized at the base where it clips on a tablet or computer screen. All the electronic components are encapsulated into a custom designed board based on the Arduino Due microcontroller. This board interfaces computer software to the Haplet's hardware with a capacity of up to four motors and encoders and two vibrotactile actuators. This paper describes our ongoing research on developing the Haplet; and, reports our initial experimental studies on quantifying the effect of visual-force-tactile feedback colocation on the user's experience and performance.

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: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.298
Teacher spread0.254 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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