Effects of foot–ground friction and age-related gait changes on falls during walking: a computational study using a neuromusculoskeletal model
Bibliographic record
Abstract
We used a neuromusculoskeletal model of bipedal walking to examine the effects of foot-ground friction conditions and gait patterns on slip- and trip-induced falls. We developed three two-dimensional neuro-musculoskeletal models in a self-organized manner representing young adults, elderly non-fallers, and elderly fallers. We simulated walking under different foot-ground friction conditions. The static friction coefficient between the foot and the ground was varied from 0.05 to 2.0. Under low friction conditions, the three gait models demonstrated slip-induced falls. The elderly faller model experienced the most slip. This is because the RCOF was higher in the elderly faller model due to its short stride length but much smaller foot clearance. Under high friction conditions, only the elderly faller model demonstrated trip-induced falls. Based on the analysis using the margin of stability, the forward postural stability of the model gradually decreased under high-friction conditions, with the toe of the swing foot contacting the ground and subsequently falling forward. These results imply that there is an optimal coefficient of friction for the ground to prevent slip- and trip-induced falls by people with less stable gaits, which may provide new insights into the design of shoes and floor surfaces for the elderly.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".