Multimodal floor for immersive environments
Bibliographic record
Abstract
We have developed an interactive system that allows untethered users to experience walking on virtual ground surfaces resembling natural materials. The demonstration consists of a multimodal floor interface for providing auditory, tactile and visual feedback to users' steps. It is intended for immersive virtual and augmented reality environments (VE) that provide the impression of walking over natural ground surfaces, such as snow and ice. To date, immersive environments with interactive floor surfaces have been largely focused on visual and auditory feedback linked to a VE simulation (e.g., [Gronbaek 2007]; see also the comparative review in [Miranda and Wanderley 2006]). However, while walking in natural environments, we receive continuous, multisensory information about the nature of the ground we walk on -- the crush of dry leaves, the soft compression of grass. The static nature of floor surfaces in existing VEs typically bears little resemblance to a given natural ground material. This creates a perceptual conflict with the dynamic visual and/or auditory feedback that users are provided in the VE. This project illustrates a novel approach to reconciling such perceptual conflicts, based on multisensory feedback provided through a floor surface in response to users' steps.
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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 | 0.002 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".