Contactless haptic feedback: state of the art
Bibliographic record
Abstract
This paper discusses some of the recent advances in contactless haptic feedback. We mainly discuss two research methods to produce haptic feedback in 3D space: Air-jet and ultrasound. We discuss and compare technical basics of each technology, and then give a literature review of some of the research done that is closely related to this field. This paper also surveys the stages of the design and implementation of airborne ultrasonic tactile displays (AUTD) by researchers in the University of Tokyo, as well as an application of this research done in the University of Bristol. A comparison is presented showing the main advances in the Tokyo research and the technical tests and implementation differences. A discussion follows covering possible improvements and safety issues on the contactless haptic feedback research in general. We show comments and drawbacks of the current technology. For future work in the field of mid-air haptic feedback, we propose a design method to build a "Touchable Avatar", which is a holographic display with contactless haptic feedback properties. Finally, a conclusion is provided including an outlook of the future applications in the field of contactless haptic feedback.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".