Harmonization of Margin of Stability Calculations and Investigation of the Impact of Foot Length, Foot Width, Gait Speed, and Body Mass
Bibliographic record
Abstract
Abstract The margin of stability (MoS), the minimum distance between the extrapolated center of mass and the edges of the base of support (BoS), is one of the most widely used metric to describe the mechanical stability during gait. In the current literature, the markers used to define the edges of the BoS are variable and the MoS model neglects the influence of anthropometric factors, such as foot length, foot width, and body mass. This study aimed to evaluate differences between anteroposterior (AP) and mediolateral (ML) MoS measures using various BoS edge definitions (AP: n = 3 methods, ML: n = 4 methods) and to investigate the impact of foot length, foot width, gait speed, and body mass on the MoS measures. Results show that the BoS edges definition affects the resulting MoS across the entire stance phase (AP: p<0.001 between the 3 methods; ML: p<0.001 between the 4 methods). Moreover, the AP MoS is influenced by foot length (p<0.029), as well as gait speed and body mass on both the AP (gait speed: p<0.001; body mass: p<0.038) and ML (gait speed: p<0.032; body mass: p<0.001) MoS. This study proposes a new approach based on optimal foot markers for defining the edges of the BoS, which may contribute to better assess mechanical stability during gait. Finally, the results suggest that normalizing the MoS (i.e., the AP MoS by foot length, gait speed, and body mass, and the ML MoS by gait speed and body mass) can facilitate comparisons between populations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".