Effect of Simulated Mass-Tunable Auxetic Midsole on Vertical Ground Reaction Force
Bibliographic record
Abstract
When runners impact the ground, they experience a sudden peak ground reaction force (GRF), which may be up to 4× greater than their bodyweight. Increased GRF impact peak magnitude has been associated with lower limb injuries in runners. Yet, shoe midsoles are capable of cushioning the impact between the runner and the ground to reduce GRF. It has been proposed that midsoles should be tunable with subject mass to minimize GRF and reduce risk of injury. Auxetic metamaterials, structures designed to achieve negative Poisson's ratios, demonstrate superior impact properties and are highly tunable. Recently, auxetic structures have been introduced in footwear, but their effects on GRF are not documented in literature. This work investigates the viability of a three-dimensional auxetic impact structure with a tunable force plateau as a midsole through mass-spring-damper simulation. An mass-spring-damper model was used to perform 315 simulations considering combinations of seven subject masses (45-90 kg), 15 auxetic plateau forces (72-1080 N), and three auxetic damping conditions (450, 725, and 1000 Ns/m) and regression analysis was used to determine their influence on GRF impact peak, energy, instantaneous, and average loading rate. Simulations showed that tuning auxetic plateau force and damping based on subject mass may reduce GRF impact and loading rate versus simulated conventional midsoles. Auxetic plateau force and damping conditions of 450 Ns/m and ∼1 bodyweight (BW), respectively, minimized peak impact GRF. This work demonstrates the need for tunable auxetic midsoles and may inform future work involving midsole testing.
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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 | 0.001 | 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".