Spring Stepper: A Seated VR Locomotion Controller
Bibliographic record
Abstract
Consumer-level Virtual Reality (VR) locomotion has been gaining momentum with research focused on omnidirectional unlimited techniques in one place employing image processing, inertial sensors in mobile devices, redirected walking, and body tracking for hands-free locomotion while seated or standing. The affordability of current VR technologies has increased its user install base, thus requiring novel, creative and more effective forms of locomotion other than teleportation that can help users make the most of reduced spaces for walk-in-place. This paper presents the Spring Stepper, a seated VR locomotion prototype for walking in place. Finally, a preliminary study on usability and task completion was conducted comparing it against the 3D Rudder, a commercial off-the-shelf walking in place controller for Desktop VR and Playstation 4. Results indicate that the Spring Stepper was not perceived as usable with a System Usability Score of 65.26/100 in comparison with the teleportation, which obtained a score of 86.03. Interestingly, the Spring Stepper was better perceived with the 3D Rudder. In terms of time completion and task accuracy, the Spring Stepper was outperformed by the other technique. Despite these results, we believe that the stepping interactive mechanics of the Spring Stepper prototype caused slower and less accurate interactions but had the most impact on users who preferred.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".