Identification of Time-varying Intrinsic and Reflex Joint Stiffness
Bibliographic record
Abstract
We have developed a time-varying, parallel- cascade system identification algorithm to separate joint stiffness into intrinsic and reflex components at each point in time throughout rapid movements. The components are identified using an iterative algorithm in which intrinsic and reflex dynamics are identified using separate time-varying (TV) techniques based on ensemble methods. An ensemble of input-output records having the same TV behavior is acquired and used to identify the system dynamics as impulse response functions at time increments corresponding to the sampling interval. Simulation studies showed that the time-varying, parallel-cascade algorithm performed well under realistic conditions with 99.9% VAF between simulated and predicted torque. To evaluate the performance of the algorithm under realistic conditions we applied it to an ensemble of experimental data acquired under stationary conditions. Results demonstrated that the TV estimates converged to those of the established time-invariant algorithm and allowed us to determine how variance of the TV estimates varied with the number of realizations in the ensemble.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".