Mechanical Modeling of Tibial Axial Accelerations Following Impulsive Heel Impact
Bibliographic record
Abstract
A fourth order mass/spring/damper (MSD) mechanical model with linear coefficients was used to estimate axial tibial accelerations following impulsive heel impacts. A generic heel pad with constant stiffness was modeled to improve the temporal characteristics of the model. Subjects (n= 14) dropped (~5 cm) onto a force platform (3 trials), landing on the right heel pad with leg fully extended at the knee. A uni-axial accelerometer was mounted over the skin on the anterior aspect of the medial tibial condyle inferior to the tibial plateau using a Velcro™ strap (normal preload ~45 N). Model coefficients for stiffness (k1, k2) and damping (c1, c2) were varied systematically until the minimum difference in peak tibial acceleration (%PTAmin) plus maximum rate of tibial acceleration (%RTAmax) between the estimated and measured curves was achieved for each trial. Model responses to mean subject and mean group model coefficients were also determined. Subject PTA and RTA magnitudes were reproduced well by the model (%PTAmin= 1.4 ± 1.0 %, %RTAmin= 2.2 ± 2.7%). Model estimates of PTA were fairly repeatable for a given subject despite generally high variability in the model coefficients, for subjects and for the group (coefficients of variation: CVk1= 57; CVk2= 59; CVc1= 48; CVc2= 85). Differences in estimated parameters increased progressively when subject and group mean coefficients (%PTAsub= 8.4 ± 6.3%, %RTAsub= 18.9 ± 18.6%, and %PTAgrp= 19.9 ± 15.2 %, %RTAgrp= 30.2 ± 30.2%, respectively) were utilized, suggesting that trial specific calibration of coefficients for each subject is required. Additional model refinement seems warranted in order to account for the large intra-subject variability in coefficients.
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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 | 0.001 | 0.000 |
| 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".