Lean into it: Exploring leaning-based motion cueing interfaces for virtual reality movement
Bibliographic record
Abstract
We describe here a pilot user study comparing five different locomotion interfaces for virtual reality (VR) locomotion. We compared a standard non-motion cueing interface, Joystick, with four leaning-based seated motion-cueing interfaces: NaviChair, MuvMan, Head-Directed and Swivel Chair. The aim of this mixed methods study was to investigate the usability and user experience of each interface, in order to better understand relevant factors and guide the design of future ground-based VR locomotion interfaces. We asked participants to give talk-aloud feedback and simultaneously recorded their responses while they were performing a search task in VR. Afterwards, participants completed an online questionnaire. Although the Joystick was rated as more comfortable and precise than the other interfaces, the leaning-based interfaces showed a trend to provide more enjoyment and a greater sense of self-motion. There were also potential issues of using velocity-control for rotations in leaning-based interfaces when using HMDs instead of stationary displays. Developers need to focus on improving the controllability and perceived safety of these seated motion cueing interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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) | 0.002 | 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".