RecHap: An Interactive Recommender System for Navigating a Large Number of Mid-Air Haptic Designs
Bibliographic record
Abstract
Designing haptics is a difficult task especially when the user attempts to design a sensation from scratch. In the fields of visual and audio design, designers often use a large library of examples for inspiration, supported by intelligent systems like recommender systems. In this work, we contribute a corpus of 10 000 mid-air haptic designs (500 hand-designed sensations augmented 20x to create 10 000), and we use it to investigate a novel method for both novice and experienced hapticians to use these examples in mid-air haptic design. The RecHap design tool uses a neural-network based recommendation system that suggests pre-existing examples by sampling various regions of an encoded latent space. The tool also provides a graphical user interface for designers to visualize the sensation in 3D view, select previous designs, and bookmark favourites, all while feeling designs in real-time. We conducted a user study with 12 participants suggesting that the tool enables people to quickly explore design ideas and experience them immediately. The design suggestions encouraged collaboration, expression, exploration, and enjoyment, which improved creativity support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".