A subject-specific inverse-dynamics approach for estimating joint stiffness in sideways fall
Bibliographic record
Abstract
Sideways fall has been identified as the most critical situation leading to hip fracture in the elderly. The stiffness and damping property of the body joints are necessary for constructing effective biomechanical models to study fall dynamics. However, very little has been known about the joint behaviour when the body is in fall. We developed a subject specific inverse-dynamics approach to estimate the joint stiffness and damping property. The anthropometric parameters required for constructing the inverse-dynamics model was extracted from the subject's whole body dual energy X-ray absorptiometry (DXA) image. The motion data of the body in sideways fall were obtained by protected fall tests using the same subject. The joints were represented by the Kelvin-Voigt model with undetermined stiffness and damping parameters, which were then determined by solving the inverse problems. For validation purpose, the obtained joint stiffness and damping parameters were substituted back into the dynamics equations and the forward problems were solved. The predicted fall kinematic variables were compared with those measured from the fall tests. Good agreements were observed, indicating that the proposed approach is reliable and reasonably accurate.
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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 | 0.001 |
| 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 | 0.001 | 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".