To Sit or Not to Sit in VR: Analyzing Influences and (Dis)Advantages of Posture and Embodied Interaction
Bibliographic record
Abstract
Virtual Reality (VR) users typically either sit or stand/walk when using VR; however, the impact of this is little researched, and there is a lack of any broad or systematic analysis of how this difference in physical posture might affect user experience and behavior. To address this gap, we propose such a systematic analysis that was refined through discussions and iterations during a dedicated workshop with VR experts. This analysis was complemented by an online survey to integrate the perspectives of a larger and more diverse group of VR experts, including developers and power users. The result is a validated expert assessment of the impact of posture and degree of embodiment on the most relevant aspects of VR experience and behavior. In particular, we posit potential strong effects of posture on user comfort, safety, self-motion perception, engagement, and accessibility. We further argue that the degree of embodiment can strongly impact cybersickness, locomotion precision, safety, self-motion perception, engagement, technical complexity, and accessibility. We provide a compact visualization of key findings and discuss areas where posture and embodiment do or do not have a known influence, as well as highlight open questions that could guide future research and VR design efforts.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".