Skin‐Mountable Vibrotactile Stimulator Based on Laterally Multilayered Dielectric Elastomer Actuators
Bibliographic record
Abstract
Abstract Skin‐stimulation technology has attracted intense attention for virtual/augmented reality applications and tactile‐feedback systems. However, bulky, heavy, and stiff characteristics of existing skin‐stimulating devices limit their wearability and comfort, thus disturbing the immersive experience of users. This study presents a new type of thin and lightweight dielectric elastomer actuator for developing a skin‐mountable vibrotactile stimulator. A new methodology is suggested to enhance the operating efficiency of dielectric elastomer actuators based on a laterally aligned dielectric multilayer structure (≈900 layer) with short dielectric distance (≈10 µm) and a soft elastomer/ionic liquid composite with low modulus and high dielectric constant. With the improved structural/material properties, the flexible actuator exhibits high displacements at low operating voltage (<200 V) over a wide frequency range (≈800 Hz). Therefore, the finger‐band type vibrotactile stimulator based on the laterally multilayered dielectric elastomer actuators can exert indentations that have the ability of stimulating all mechanoreceptors in human skin over the full perception frequency/amplitude range. In addition, the actuator shows a high electromechanical stability for long‐term operation due to time‐efficient and precise fabrication process using sophisticated photolithography and secondary sputtering. Therefore, this vibrotactile stimulator shows high promise for use in tactile‐assistive devices, tactile communications, haptic feedback, and beyond.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".