Analysis of Ground Reaction Force Impulses During Uneven Walking for Young and Older Adults
Bibliographic record
Abstract
Abstract Humans navigate various terrains by exerting forces to direct the Center of Mass (COM) and maintain balance. During walking, humans transition from one stance leg to the next by exerting impulses during the step-to-step transition. Studying these transition impulses may provide insight into how humans traverse uneven terrains. When walking speed increased (constant terrain amplitude), the average braking and propulsive impulses (posterior/anterior) increased comparably (−0.0270 m · s −1 · g −1 v −1 versus 0.0252 m s −1 · g −1 v −1 ). In the vertical direction, while the collision impulse remained constant, the push-off impulse declined by -0.0535 m · s −1 · g −1 · v −1 . The interaction of age and speed also increased the collision impulse (0.0202 m · s −1 · g −1 · v −1 ). With the rise of terrain amplitude (constant speed), the braking and propulsive impulses rose by -0.0607 m · s −1 · g −1 · m −1 and 0.0701 m · s −1 · g −1 · m −1 , respectively. Thus, we could infer that the propulsive impulse also contributed to the gait mechanical energy. While the collision impulse increased with terrain amplitude (0.1775 m · s −1 · g −1 · m −1 ) and the interaction of age and terrain amplitude (0.1058 m · s −1 · g −1 · m −1 ), the push-off impulse declined (−0.2700 m · s −1 · g −1 · m −1 and -0.1473 m · s −1 · g −1 · m −1 ). We also observed the push-off as a fraction of the total vertical impulse declined. Therefore, we detected a mechanical energy deficit in the step-to-step transition that must have been compensated for during the mid-flight phase. Considering the portion of push-off occurring after the subsequent heel-strike as delayed push-off, while it increased with walking speed, it declined with terrain amplitude. Thus, during uneven walking, the push-off exertion must have been interrupted, indicating the demand for further mechanical energy infusion after the transition.
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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 | 0.001 | 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".