Exploration of Foot-based Text Entry Techniques for Virtual Reality Environments
Bibliographic record
Abstract
Foot-based input can serve as a supplementary or alternative approach to text entry in virtual reality (VR). This work explores the feasibility and design of foot-based techniques that are hands-free. We first conducted a preliminary study to assess foot-based text entry in standing and seated positions with tap and swipe input approaches. The findings showed that foot-based text input was feasible, with the possibility for performance and usability improvements. We then developed three foot-based techniques, including two tap-based techniques (FeetSymTap and FeetAsymTap) and one swipe-based technique (FeetGestureTap), and evaluated their performance via another user study. The results show that the two tap-based techniques supported entry rates of 11.12 WPM and 10.80 WPM, while the swipe-based technique led to 9.16 WPM. Our findings provide a solid foundation for the future design and implementation of foot-based text entry in VR and have the potential to be extended to MR and AR.
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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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".