Comparing leaning-based motion cueing interfaces for virtual reality locomotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we describe a user study comparing five different locomotion interfaces for virtual reality locomotion. We compared a standard non-motion cueing interface, Joystick (Xbox), with four motion cueing interfaces, NaviChair (stool with springs), MuvMan (sit/stand active stool), Head-Directed (Oculus Rift DK2), and Swivel Chair (everyday office chair with leaning capability). Each interface had two degrees of freedom to move forward/backward and rotate using velocity (rate) control. The aim of this mixed methods study was to better understand relevant user experience factors and guide the design of future locomotion interfaces. This study employed methods from HCI to provide an understanding of why users behave a certain way while using the interface and to unearth any new issues with the design. Participants were tasked to search for objects in a virtual city while they provided talk-aloud feedback and we logged their behaviour. Subsequently, they completed a post-experimental questionnaire on their experience. We found that the qualitative themes of control, usability, and experience echoed the results of the questionnaire, providing internal validity. The quantitative measures revealed the Joystick to be significantly more comfortable and precise than the motion cueing interfaces. However, the qualitative feedback and interviews showed this was due to the reduced perceived controllability and safety of the motion cueing interfaces. Designers of these interfaces should consider using a backrest if users need to lean backwards and avoid using velocity-control for rotations when using HMDs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it