Data-driven design guide for vibrotactile display layouts by continuous mapping
Bibliographic record
Abstract
Tactile displays are emerging as vital components across fields such as medical technology, assistive technology, virtual reality, infotech and gaming, yet their design remains hampered by an inadequate psychophysical foundation. In this work, we critically assess current tactile display research, uncovering gaps in spatial acuity data, and introduce robust, data-driven layout guidelines for vibrotactile displays (VTDs). We collected high-resolution vibrotactile data from 33 participants across five large-area body sites using a novel, fully automated experimental framework that employs Bayesian adaptive parameter estimation to generate continuous psychometric functions. This approach allows us to derive thresholds at any recognition rate, thereby overcoming the limitations of traditional, discrete measures. Our findings reveal that existing datasets are scattered and inconsistent, demonstrate a pronounced horizontal anisotropy especially near the body midline and expose a marked sensitivity gradient along the lower back. These insights provide a validated psychophysical basis for VTD development, paving the way for more reliable, user-centric designs in next-generation tactile interfaces.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".