Lower limb muscle activity underlying temporal gait asymmetry post-stroke
Bibliographic record
Abstract
Abstract Objective Asymmetric walking after stroke is common, detrimental, and difficult to treat, but current knowledge of underlying physiological mechanisms is limited. This study investigated electromyographic (EMG) features of temporal gait asymmetry (TGA). Methods Participants post-stroke with or without TGA and control adults (n=27, 8, and 9, respectively) performed self-paced overground gait trials. EMG, force plate, and motion capture data were collected. Lower limb muscle activity was compared across groups and sides (more/less affected). Correlation between burst timing variables and asymmetry ratios was examined. Results Significant group by side interaction effects were found: fewer TGA group dorsiflexor bursts during swing (p=.0009), more affected plantarflexor stance activity ended early (p=.0006) and less affected dorsiflexor on/off time was delayed (p<.01) in persons with asymmetry compared to symmetric and normative controls. Less affected side EMG timing correlated most with swing time ratio (r=0.68-0.90, p<.001). Conclusions Temporal patterns of muscular activation, particularly about the ankle around the stance-to-swing transition period, are associated with TGA. The results may reflect specific impairments or compensations that affect locomotor coordination. Significance Neuromuscular underpinnings of spatiotemporal asymmetry have not been previously characterized. These novel findings may inform targeted therapeutic strategies to improve gait quality after stroke.
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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) | 0.002 | 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".