The multichannel vibrotactile gloves: A transmodal technology to feel sound through touch
Bibliographic record
Abstract
Development of devices for transmitting sounds through touch is motivated by needs coming from diverse disciplines. Hard-of-hearing individuals could benefit from vibrations to overcome the limitations of existing hearing technologies. Adding tactile cues can be useful for all in contexts where the acoustic information is limited due to sounds coming from multiple sources or noise. The potential sensory augmentation provided by the technology is also interesting in an entertainment context in order to offer immersive experiences. A transdisciplinary approach based on a framework recently developed in our laboratory was used to design this technology that enables the transmission of acoustic signals through touch. Validation experiments were carried out via electro-acoustic measurements as well as behavioral measurements in human subjects (n = 5). Electro-acoustic and behavioral measures support that the system provides uniform stimulation across hands and actuators. The frequency response curve as well as the summation effect measured via behavioral threshold measurements support that the tactile receptors are accurately stimulated by the devices. The multichannel vibrotactile gloves offer the flexibility to transmit diverse acoustic features to individual actuators, making them a valuable tool for research and a prospective technology capable of substituting, compensating, or extending sensory perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".