The effect of perturbation magnitude on lower limb muscle activity during reactive stepping using functional data analysis
Bibliographic record
Abstract
Abstract This study aimed to determine the effect of perturbation magnitude on stance and stepping limb muscle activation during reactive stepping using functional data analysis. Nineteen healthy, young adults responded to 6 small and 6 large perturbations using an anterior lean-and-release system, evoking a single reactive step. Muscle activity from surface electromyography was compared between the two conditions for medial gastrocnemius, biceps femoris, tibialis anterior, and vastus lateralis of the stance and stepping limb using functional data analysis. Stance limb medial gastrocnemius and biceps femoris activation increased in the large compared to small perturbation condition immediately prior to foot-off and at foot contact. In the stepping limb, significant increases in medial gastrocnemius, biceps femoris, and tibialis anterior activity occurred immediately prior to foot-off during the large perturbations. Similar to the stance limb, medial gastrocnemius and biceps femoris activity significantly increased during and following foot contact in the large, compared to small, perturbation condition. Lastly, vastus lateralis activity significantly increased for large, compared to small, perturbations during foot-off and immediately following foot contact. These findings highlight lower limb muscle activity modulation associated with perturbation magnitude throughout reactive stepping and the additional benefit of implementing functional data analysis to study reactive balance control.
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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 | 0.004 |
| 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".