Individual limb contributions to mediolateral stability during perturbation-evoked stepping responses: A preliminary study
Bibliographic record
Abstract
Mediolateral stability control during compensatory stepping responses is challenging for older adults but is critically important since lateral falls carry an increased probability of hip fracture. Previous work has found the orientation (eccentricity) and timing of the net ground reaction force (GRF) relative to the centre of mass (COM) to be important determinants of mediolateral stability recovery during the restabilization phase of the stepping response, following foot-contact. The present work sought to understand the individual limb contributions to stability recovery, by examining the eccentricity of the GRF generated by each limb during the restabilization phase of the stepping response. A lean-and-release paradigm was used to evoke stepping responses amongst 5 younger females (18-25 years) and 5 older females (>65 years) participants. Whole-body COM kinematics and GRFs beneath both limbs were quantified using motion analysis and four force platforms. Kinematic instability was quantified as the difference between the peak lateral COM position and the final stable COM position. Spearman's correlations were used to understand whether the magnitude and timing of GRF eccentricity from both the stance and stepping limb were related to kinematic instability during restabilization. Greater kinematic instability was related to a reduced peak and longer time-to-peak eccentricity of the stepping limb GRF immediately ( Acknowledgments: Funding for this research was provided by the Manitoba Medical Service Foundation and the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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) | 0.003 | 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".