<title>Effect of visual distortion on postural balance in a full immersion stereoscopic environment</title>
Bibliographic record
Abstract
This study attempted to determine the influence of non-linear visual movements on our capacity to maintain postural control. An 8x8x8 foot CAVE immersive virtual environment was used. Body sway recordings were obtained for both head and lower back (lumbar 2-3) positions. The subjects were presented with visual stimuli for periods of 62.5 seconds. Subjects were asked to stand still on one foot while viewing stimuli consisting of multiplied sine waves generating movement undulation of a textured surface (waves moving in checkerboard pattern). Three wave amplitudes were tested: 4 feet, 2 feet, and 1 foot. Two viewing conditions were also used; observers looking at 36 inches in front of their feet; observers looking at a distance near the horizon. The results were compiled using an instability index and the data showed a profound and consistent effect of visual disturbances on postural balance in particular for the x (side-to-side) movement. We have demonstrated that non-linear visual distortions similar to those generated by progressive ophthalmic lenses of the kind used for presbyopia corrections, can generate significant postural instability. This instability is particularly evident for the side-to-side body movement and is most evident for the near viewing condition.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".