Changes in Seasonal Footwear Elicited Alterations in Gait Kinematics but Not Stability
Bibliographic record
Abstract
Abstract During daily walking, humans might contend with various perturbations from slippery surfaces in the winter to uneven sidewalks in the summer. Inertial sensors enable investigations of how humans maintain balance under these natural conditions, but conducting these outdoor studies has practical considerations that might influence study results, such as the selection of footwear under different weather conditions. Our study investigates the effects of winter and summer shoe types on gait patterns, specifically whether different shoe types induce changes in gait stability measures under the same walking environment. Twelve healthy adult participants walked indoors with winter and summer shoes while their gait kinematics were recorded using an inertial sensor-based motion capture system. Spatiotemporal measures, body kinematics, stability measures (minimum margin of stability and local divergence exponent), and stepping regressions were calculated to evaluate differences between walking in summer and winter shoes. Statistical significance was determined by paired t-tests. Varying shoe types altered spatiotemporal and kinematic measures, such as increased stride time and stance time while wearing winter shoes, but increased step width and reliance on stepping were the only stability-related changes found. Our study provides insights into the influence of footwear for inertial sensor-based gait studies in real environments, aiding the analysis and interpretation of those studies to augment our understanding of natural stability behavior.
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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".