A Global Gait Asymmetry Index
Bibliographic record
Abstract
High levels of gait asymmetry are associated with many pathologies. Our long-term goal is to improve gait symmetry through real-time biofeedback of a symmetry index. Symmetry is often reported as a single metric or a collective signature of multiple discrete measures. While this is useful for assessment, incorporating multiple feedback metrics presents too much information for most subjects to use as visual feedback for gait retraining. The aim of this article was to develop a global gait asymmetry (GGA) score that could be used as a biofeedback metric for gait retraining and to test the effectiveness of the GGA for classifying artificially-induced asymmetry. Eighteen participants (11 males; age 26.9 y [SD = 7.7]; height 1.8 m [SD = 0.1]; body mass 72.7 kg [SD = 8.9]) walked on a treadmill in 3 symmetry conditions, induced by wearing custom-made sandals: a symmetric condition (identical sandals) and 2 asymmetric conditions (different sandals). The GGA score was calculated, based on several joint angles, and compared between conditions. Significant differences were found among all conditions (P < .001), meaning that the GGA score is sensitive to different levels of asymmetry, and may be useful for rehabilitation and assessment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".