[DC] Exploring and Designing VR Locomotion Method Based on Bio-Signal for Hands-Free Context and its Improvement
Bibliographic record
Abstract
Along with the advanced hand-tracking algorithms, various interactions based on hand motion are actively developing. However, if too much function are concentrated on the hand, it might be a burden on the hand and interrupt immersion. Therefore, we focused on designing a locomotion method, which is essential for exploring the broad virtual world efficiently and comfortably. We utilized bio-signal (i.e., eye tracking, EEG) to reduce the load on the hand. First, we explored to compare the usability and efficiency of our method to hand-tracking-based locomotion methods. The result showed that our methods are suitable for hands-free VR contexts. Also, we found that EEG based method proposes enhanced experience when it is used with eye-tracking. In order to improve this effect, we are currently working on research that develops user-friendly Steady-State Visual Evoked Potential (SSVEP) stimuli that suit the VR HMD format. From our research, we aim to propose design guidelines for presenting appropriate locomotion methods depending on the various contexts in the virtual environment for an enhanced hands-free VR experience.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".