Democratizing a Large Over Ground Virtual Suite (Logvs) Involving Human Interaction for Rehabilitation Sciences and Practice
Bibliographic record
Abstract
Virtual environments (VE) used within rehabilitation-based virtual reality (VR) platforms often limit their social environmental context and the ability to move naturally within the VE. This technical note presents an evolving VR platform offering virtual human pedestrian (VP) interaction and highlights specific developments underway and future directions for use in research and practice. The platform called the Large Over Ground Virtual Suite or Logvsuses affordable, mobile VR technology involving a VE with multimodal navigation options (biped and wheeled locomotion) while manipulating VP characteristics (e.g., emotions, expressions, physical interactions). The overall goal of the evolving platform is to democratically enhance interdisciplinary study, assessment, and training of different aspects of social interaction (social cognition, mobility, multiple sensory-motor interactions) to cater to rehabilitation research and clinical needs more effectively. In addition to detailing the platform and examples of current uses and developments, this work serves to also comment on the need for greater sharing of immersive technology resources across the rehabilitation sciences community.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".