Fabricating Bendy: Design and Development of Deformable Prototypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deformable user interfaces leverage the physical actions we use intuitively to interact with real objects. It is therefore important to consider a prototype's physical characteristics when conducting research on deformable interactions. To create an authentic product experience, the authors set out to build a prototype that would mimic potential commercialized flexible devices and achieve the realism often lacking in low-fidelity prototypes. In this article, they outline the design and fabrication process to create Bendy, a prototype for studying deformable user interfaces. Their method creates prototypes quickly (one day) and inexpensively (approximately US $70) by using readily available materials. In addition, the process lets other researchers customize physical properties and interaction language to fit their specific purposes. The deformable prototype is composed of three main layers: a flexible plastic, an array of bend sensors connected to a flexible circuit, and a layer of silicone that encloses the sensors and circuit. The authors describe the fabrication process and demonstrate their method with two additional case studies. This article is part of a special issue on fabrication and printing. The Web extra shows the fabrication process, from the circuit design to printing and testing our flexible circuit, to creating the final Bendy prototype and playing Pac-Man using our deformable prototype. The web extra for this article can be found at http://youtu.be/PJ5ee5gAbm8.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it