Altering Compliance of a Load Carriage Device in the Medial-Lateral Direction Reduces Peak Forces While Walking
Bibliographic record
Abstract
Altering mechanical compliance in load carriage structures has shown to reduce metabolic cost and accelerative forces of carrying weight. Currently, modifications to load carriage structures have been primarily targeted at vertical motion of the carried mass. No study to date has investigated altering load carriage compliance in the medial-lateral direction. We developed a backpack specifically for allowing a carried mass to oscillate in the horizontal direction, giving us the unique opportunity to understand the effects of lateral mass motion on human gait. Previous modelling work has shown that walking economy can be improved through the interaction of a bipedal model with a laterally oscillating walking surface. To test whether a laterally oscillating mass can experimentally improve walking economy, we systematically varied step width above and below the preferred level and compared the effects of carrying an oscillating and fixed mass. Walking with an oscillating mass was found to reduce the accelerative forces of load carriage in both horizontal and vertical directions. However, load eccentricity caused the vertical force component to create a significant bending moment in the frontal plane. Walking with an oscillating mass led to an increase in the metabolic energy expenditure during walking and an increase in positive hip work during stance. The device's ability to reduce forces experienced by the user, due to load carriage, holds promise. However, the requirement of additional metabolic energy to walk with the device requires future study to improve.
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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) | 0.001 | 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 | 0.001 |
| 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".