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Record W7117250364 · doi:10.1299/jsmermd.2025.1p1-p08

Pneumatic vibrotactile display using speakers

2025· article· en· W7117250364 on OpenAlexaff
Keitaro Ihara, Hiroki Ishizuka, Takefumi Hiraki, Yusuke Sakaue, Osamu Oshiro

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsAir compressorGas compressorVibrationCompressed airPresentation (obstetrics)Range (aeronautics)Sound pressurePressure sensor

Abstract

fetched live from OpenAlex

In this study, we developed a pneumatic vibrotactile display that uses sound pressure from a speaker to generate changes in air pressure, which is transmitted to the fingertip via a silicone tube to enable the presentation of vibrations over a wide bandwidth. Generally, pneumatic vibrotactile displays using air compressors have difficulty in presenting vibrations in high frequency bands, especially above 200 Hz, due to limitations in the response speed and control performance of the open/close valve. For this reason, this study attempted to construct a system that can handle a wider range of frequencies by taking advantage of the high responsiveness of the speaker. Specifically, by transmitting air pressure vibrations generated by changes in the speaker’s sound pressure to the fingertips through silicon tubes, this system enables the presentation of tactile stimuli that include high-frequency components, which has been difficult with the conventional air compressor system.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.041
GPT teacher head0.296
Teacher spread0.256 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)Same topicTactile and Sensory InteractionsFrench-language works237,207