Coordination of Gaze Behavior and Foot Placement During Walking in Persons With Glaucoma
Bibliographic record
Abstract
PURPOSE: Vision normally provides environmental information necessary to direct the foot to safe locations during walking. Peripheral visual field loss limits what a person can see, and may alter how a person visually samples the environment. Here we tested the hypothesis that the spatial-temporal coupling between gaze and stepping in a precision-based walking task is altered in persons with glaucoma, particularly under dual task situations, and results in reduced foot-placement accuracy. METHODS: Twenty persons with glaucoma and 20 normally sighted controls performed a precision walking task that involved stepping to the center of 4 targets under 3 conditions: targets only, walking, and counting backwards to simulate a conversation, and walking while performing a concurrent visual search task to simulate locating a landmark. We quantified foot-placement error and error variability with respect to the targets, as well as saccade and fixation timing with respect to foot placement. RESULTS: Compared with controls, persons with glaucoma looked earlier at future stepping targets (with respect to toe-off of the foot) in the targets only and count conditions, and transferred gaze away sooner from the current stepping target in all conditions (P<0.05). Persons with glaucoma also had increased foot-placement error, particularly in the count condition, and increased foot-placement error variability compared with normally sighted controls (P<0.05). CONCLUSIONS: Glaucoma significantly disrupts gaze-foot coordination and results in less accurate foot placement when precision is required during walking. This may increase the risk of trips and falls in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".