Fabricating Bendy: Design and Development of Deformable Prototypes
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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".