Unbiased Estimation of Human Joint Intrinsic Mechanical Properties During Movement
Bibliographic record
Abstract
The overall mechanical properties of a joint are generated by a combination intrinsic (mechanical) and reflex (neural) mechanisms. Nevertheless, many methods for estimating joint mechanical properties have used a linear dynamic model whose parameters are commonly related to the joint inertial and visco-elastic properties. Such mechanical models cannot account for torques due to reflex mechanisms and consequently fitting them to data containing reflex torques can give biased results. This paper addresses this issue in two ways. First, using simulation studies, it demonstrates that fitting linear dynamic models in the presence of reflex torques will indeed provide biased estimates of intrinsic joint properties; the bias is significant for small reflex torques and increases proportionally with reflex torque magnitude. Second, it develops and validates a novel approach to accurately estimate the time-varying, intrinsic mechanical properties of a joint in the presence of reflex torques. The approach involves applying small position perturbations to the joint trajectory and then applying novel mathematical models and system identification techniques to analytically separate the measured total joint torque into its intrinsic and reflex components. The method first estimates a non-parametric, reflex electromyography-torque model, and uses it to predict the reflex torques which is subtracted from the total torque. Then, it estimates a non-parametric, linear, and time-varying model of the intrinsic mechanical properties from the residuals. Simulation results demonstrate that the new approach accurately tracks time-varying joint intrinsic mechanical properties during movement independently of the reflex torque magnitude. The new algorithm will be a useful tool in the study of motor control, as it supports the unbiased estimation of joint intrinsic mechanical properties during movement in the presence of reflex torques.
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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 | 0.006 |
| 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 | 0.001 |
| 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".