New simple virtual walking method – walking on the spot
Bibliographic record
Abstract
In CAVE-like environments human locomotion is significantly restricted due to physical space and configural constraints. Interaction techniques based upon stepping in place have been suggested as a way to simulate long range locomotion. We describe a new method for step detection and estimation of forward walking speed and direction in an immersive virtual environment. To calibrate our system and to help in the modeling of the stepping behaviour, we collected motion capture data during real locomotion down a hallway while walking at different freely selected speeds, from very slow to very fast. From this data, the empirical relation between the forward speed of real walking and the trajectory of the leg motion during stepping was established. A simple model of stepping motion was fit for individual subjects. The model was used to estimate forward walking speed and direction from step characteristics during walking in place in a six-walled virtual environment. The system provides natural and effective simulated gait for interaction and travel within the virtual environment and provides the ability to study human locomotion and navigation in a CAVE-like environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".