TactTongue: Prototyping ElectroTactile Stimulations on the Tongue
Bibliographic record
Abstract
The tongue is a remarkable human organ with a high concentration of taste receptors and an exceptional ability to sense touch. This work uses electro-tactile stimulation to explore the intricate interplay between tactile perception and taste rendering on the tongue. To facilitate this exploration, we utilized a flexible, high-resolution electro-tactile prototyping platform that can be administered in the mouth. We have created a design tool that abstracts users from the low-level stimulation parameters, enabling them to focus on higher-level design objectives. Through this platform, we present the results of three studies. Our first study evaluates the design tool’s qualitative and formative aspects. In contrast, the second study measures the qualitative attributes of the sensations produced by our device, including tactile sensations and taste. In the third study, we demonstrate the ability of our device to sense touch input through the tongue when placed on the hard palate region in the mouth. Finally, we present a range of application demonstrators that span diverse domains, including accessibility, medical surgeries, and extended reality. These demonstrators showcase the versatility and potential of our platform, highlighting its ability to enable researchers and practitioners to explore new ways of leveraging the tongue’s unique capabilities. Overall, this work presents new opportunities to deploy tongue interfaces and has broad implications for designing interfaces that incorporate the tongue as a sensory organ.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".