ThumbSwype: Thumb-to-Finger Gesture Based Text-Entry for Head Mounted Displays MHCI031
Bibliographic record
Abstract
Designing a comfortable, familiar, and efficient one-handed text entry method for Head-Mounted Displays (HMDs) remains a significant challenge. Existing midair typing systems induce fatigue, while novel techniques often demand extensive training or sacrifice input efficiency. Consequently, we introduce ThumbSwype , a novel thumb-to-finger text entry technique that adapts smartphone swipe typing for HMDs. Users see the traditional QWERTY keyboard overlaid on their index, middle, and ring fingers, allowing them to perform swipe gestures with their thumb to type words. In an evaluation study (N=16) , participants achieved a mean of 14.52 words per minute (WPM), which is 63.8% of their smartphone swipe-typing performance, with a peak average of 20.2 WPM. We compare ThumbSwype’s performance with related work, and discuss directions for future improvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".