Time-varying identification of ankle dynamic joint stiffness during movement with constant muscle activation
Bibliographic record
Abstract
Dynamic joint stiffness defines the torque generated at the joint in response to position perturbations. Dynamic stiffness is modulated by the angular position and the muscle activation level, making it difficult to estimate during large movements and/or time-varying muscle contractions. This paper presents a new methodology for estimating dynamic joint stiffness during movement and muscle activation. For this, we formulate a novel, nonlinear, dynamic joint stiffness model and present a new algorithm to estimate its parameters. The algorithm assumes that the variability in the model parameters is a function of the mean joint position. Using this methodology we estimated the dynamic joint stiffness at the ankle throughout ramp and hold displacements during a constant muscle contraction. The estimated model accurately predicted the intrinsic and reflex torques produced at the ankle as a response to small position perturbations during large displacement with muscle activation. Preliminary results show that during muscle contraction, ankle intrinsic stiffness estimated during movement is significantly lower than that estimated during quasi-stationary experiments.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".