Design and evaluation of a versatile text input device for virtual and immersive workspaces
Bibliographic record
Abstract
While headsets dedicated to mixed reality are increasingly considered to virtualize workspaces in the office, the keyboard remains mostly unchanged and nevertheless is still envisioned as an interface for common professional activities heavily demanding in text entry. However, unlike headsets, a keyboard is intrinsically limited to immobile context during its use, due to the need for physical support. In this article, we investigate a radical change in the structural shape of the keyboard, switching from the plane to a cube, allowing users to fully benefit from their whole environment by entering text in a much wider variety of contexts. We designed a cubic layout based on the QWERTY and results from two user studies to define the best holding position as well as fingers preferences and reachability to the cubic shape. Then, we conducted two subsequent user studies and one longitudinal case study to compare and evaluate typing skill transfer from the keyboard, learning curve, fingers usage, usability, and workload in various contexts. The cubic-shaped text entry device finally showed its versatility with users having similar performances while standing with a mixed-reality headset or sitting in front of a laptop, and an input speed from an expert reaching 55.1 wpm.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".