Visual Cues Promote Head First Strategies During Walking Turns in Individuals With Parkinson's Disease
Bibliographic record
Abstract
Anticipatory eye movement promotes cranio-caudal sequencing during walking turns. Clinical groups, such as Parkinson's disease (PD), do not produce anticipatory eye movements, leading to increased risk of falls. Visual cues may promote anticipatory eye movement by guiding the eyes into the turn. This study examined if visual cues could train anticipatory eye movement. Ten neurotypical young adults and 6 adults with PD completed three blocks of walking trials. Trials were blocked by visual condition: non-cued baseline turns (5 trials), visually cued turns (10 trials), and non-cued post turns (5 trials). A Delsys Trigno (Delsys, Boston, MA) recorded horizontal saccades at 1024 Hz via electrooculography (EOG). Two Optotrak cameras (Northern Digital Inc., ON, Canada) captured body segment kinematics at 120 Hz. Initiation of segment rotation with respect to ipsilateral foot contact (IFC1) prior to the turn was calculated. Neurotypical young adults (NYA) produced typical cranio-caudal rotation sequences during walking turns. Eyes led (407 ms prior to IFC1), followed by the head (50 ms prior to IFC1), then trunk and pelvis. In contrast, PD produced no anticipatory eye or segment movement at baseline. During pre-trials the eyes moved 96 ms after IFC1 and segment movement was initiated by the pelvis followed by trunk and head segments. After visual cue training however, PD produced anticipatory eye movements 161 ms prior to IFC1, followed by the head 88 ms following IFC1 but ahead of trunk and pelvis onset. These results suggest visual cues assist in producing cranio-caudal control during walking turns in PD.
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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) | 0.001 | 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".