Evaluating the Usability of Microgestures for Text Editing Tasks in Virtual Reality
Bibliographic record
Abstract
As virtual reality (VR) continues to evolve, traditional input methods such as handheld controllers and gesture systems often face challenges with precision, social accessibility, and user fatigue. These limitations motivate the exploration of microgestures, which promise more subtle, ergonomic, and device-free interactions. We introduce microGEXT, a lightweight microgesture-based system designed for text editing in VR without external sensors, which utilizes small, subtle hand movements to reduce physical strain compared to standard gestures. We evaluated microGEXT in three user studies. In Study 1 ($N=20$N=20), microGEXT reduced overall edit time and fatigue compared to a ray-casting + pinch menu baseline, the default text editing approach in commercial VR systems. Study 2 ($N=20$N=20) found that microGEXT performed well in short text selection tasks but was slower for longer text ranges. In Study 3 ($N=10$N=10), participants found microGEXT intuitive for open-ended information-gathering tasks. Across all studies, microGEXT demonstrated enhanced user experience and reduced physical effort, offering a promising alternative to traditional VR text editing techniques.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".