A mobile platform for controlling and interacting with a do-it-yourself smart eyewear
Bibliographic record
Abstract
Purpose Smart eyewear, such as augmented or virtual reality headset, allows the projection of virtual content through a display worn on the user’s head. This paper aims to present a mobile platform, named “CARTON”, which transforms a smartphone into smart eyewear, following a do-it-yourself (DIY) approach. This platform is composed of three main components: a blueprint to build the hardware prototype with very simple materials and regular tools; a software development kit (SDK) to help with the development of new applications (e.g. augmented reality app); and, finally, a second SDK (ControlWear) to interact with mobile applications through a Smartwatch. Design/methodology/approach User experiments were conducted, in which participants were asked to create, by themselves, the CARTON’s hardware part and perform usability tests with their own creation. A second round of experimentation was conducted to evaluate three different interaction modalities. Findings Qualitative user feedback and quantitative results prove that CARTON is functional and feasible to anyone, without specific skills. The results also showed that ControlWear had the most positive results, compared with the other interaction modalities, and that user interaction preference would vary depending on the task. Originality/value The authors describe a novel way to create a smart eyewear available for a wide audience around the world. By providing everything open-source and open-hardware, they intend to solve the reachability of technologies related to smart eyewear and aim to accelerate research around it.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".