Does increased gait variability improve stability when faced with an expected balance perturbation during treadmill walking?
Bibliographic record
Abstract
Abstract Background Currently, there is uncertainty as to whether movement variability is errorful or exploratory. Research question This study aimed to determine if gait variability represents exploration to improve stability. We hypothesized that 1) spatiotemporal gait features will be more variable prior to an expected perturbation than during unperturbed walking, and 2) increased spatiotemporal gait variability pre-perturbation will correlate with improved stability post-perturbation. Methods Sixteen healthy young adults completed 15 treadmill walking trials within a motion simulator under two conditions: unperturbed and expecting a perturbation. Participants were instructed not to expect a perturbation for unperturbed trials, and to expect a single transient medio-lateral balance perturbation for perturbed trials. Kinematic data were collected during the trials. Twenty steps were recorded post-perturbation. Unperturbed and pre-perturbation gait variabilities were defined by the short- and long-term variabilities of step length, width, and time, using 100 steps from pre-perturbation and unperturbed trials. Paired t-tests identified between-condition differences in variabilities. Stability was defined as the number of steps to centre of mass restabilization post-perturbation. Multiple regression analyses determined the effect of pre-perturbation variability on stability. Results Long-term step width variability was significantly higher pre-perturbation compared to unperturbed walking (mean difference=0.28cm, p=0.0073), with no significant differences between conditions for step length or time variabilities. There was no significant relationship between pre-perturbation variability and post-perturbation restabilization. Significance Increased pre-perturbation step width variability was neither beneficial nor detrimental to stability. However, the increased variability in medio-lateral foot placement suggests that participants adopted an exploratory strategy in anticipation of a perturbation.
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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 | 0.002 |
| 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".