HaptiYarn: Development of an Actuator Yarn That Can Transform Everyday Textiles Into Haptic Devices
Bibliographic record
Abstract
A method of providing localised haptic feedback at precise locations on the body, utilising a lightweight textile garment is presented in this short paper. The textile comprises of subtly integrated actuator yarns (HaptiYarns) which are controlled by electropneumatic circuitry. Each yarn has two functional layers, an inner porous textile layer with limited extensibility and a second, durable outer layer made from an extensible elastomer. The HaptiYarns can provide radial forces and a maximum radial displacement of 28.09 ± 0.14 mm. It was found that the intrinsic addition of graphite powder (5% by weight), during elastomer preparation, offered better resistance to layer delamination and increased the ability of the yarn to withstand higher internal air pressures by 48%. Both the graphite-filled composite and the graphite free yarns demonstrated high durability, withstanding cyclic testing of >7500 cycles while having no significant impact on the force feedback. Finally, a wearable prototype knitted textile garment is presented with eight HaptiYarns subtly integrated within it and connected to a virtual reality (VR) program providing an immersive haptic experience. These yarns offer the potential to transform everyday clothing into wearable haptic devices with potential to revolutionise healthcare, VR-based training, gaming, and entertainment sectors.
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 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".