Strategies to adapt walking mechanics to path curvature after a stroke
Bibliographic record
Abstract
Presented at the American Congress of Rehabilitation Medecine (ACRM), Vancouver, British Columbia, Canada (October 9 – 13, 2012).\n Objective: The ability to turn while walking is compromised in people with stroke. The aim of this study was to understand the contribution of biomechanical gait impairments to locomotor adapta- tions to path curvature in people with stroke. Data were collected from eight individuals with hemiparesis due to a stroke and 12 age-matched able-bodied individuals. Participants walked along four paths of different curvature (straight line, large circle, medium circle, small circle) while whole body kinematic data in three dimensions were collected. A modulation index representing the slope of the regression line between peak joint angle and path curvature at specific phases of the step cycle was computed to represent joint kinematic modulation patterns. \n Results: In able-bodied individuals, we observed consistent modula- tion of transverse and frontal plane movements at the ankle and hip as path curvature increased. For example, in order to walk smoothly around the circle, the control group increased the amplitude of hip adduction in the leg located on the inside of the circle during stance. In individuals with stroke, we observed disordered modulation of the adaptation of frontal and transverse kinematic movement parameters as path curvature increased. In general, the stroke group would show reduced hip adduction on their affected leg even if it was on the inside of the turn. Conclusions: Adaptations in the kinematic pattern seen during curved walking in able-bodied participants were not seen in the stroke group.
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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".