Real-Time Estimation of Intrinsic and Reflex Stiffness
Bibliographic record
Abstract
Joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it. Joint stiffness is composed of two components: intrinsic and reflex stiffness. Separating the two stiffness components is difficult because they appear and change together. A number of approaches have been used to distinguish the components, but all these are inherently off-line. We have developed a novel algorithm that estimates the two components of ankle stiffness in real time. Cross-correlations between torque and position, velocity, and acceleration are used to estimate intrinsic stiffness. The reflex torque is then estimated by subtracting the estimated intrinsic components and the reflex stiffness estimated by computing the impulse response function (IRF) between the estimated reflex torque and the half-wave rectified velocity. A novel position perturbation, consisting of pseudo-random pulses of different lengths, is used to eliminate covariance between intrinsic and reflex stiffness estimates. Simulation results showed that the algorithm estimates intrinsic and reflex stiffness very accurately and responds to changes in stiffness in less than 15 s. Validation with experimental data showed that the real-time estimates were in close agreement with the estimates generated by an established off-line intrinsic and reflex stiffness identification algorithm.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".