Identification of Central and Stretch Reflex Contributions to Human Postural Control
Bibliographic record
Abstract
Human postural control requires continuous modulation of ankle torque to stabilize the upright stance. The torque is generated by two components: active contributions, due to central control and stretch reflex, and passive mechanisms, due to joint intrinsic stiffness. Identifying the contribution of each component is difficult, since their effects appear together, and standing is controlled in closed-loop. This article presents a novel multiple-input, single-output method to identify central and stretch reflex contributions to human postural control. The model uses ankle muscle EMGs as inputs and requires no kinematic data. Application of the method to data from nine subjects during standing while subjected to perturbations of ankle position demonstrated that active torque accounted for 84.0± 5.5% of the ankle torque. The ankle plantar-flexors collectively produced the largest portion of the active torque through central control, with large inter-subject variability in the relative contributions of the individual muscles. In addition, reflex contribution of the plantar-flexors was substantial in half of the subjects, showing its potentially important functional role; finally, intrinsic contributions, estimated as the residual of the model, contributed to 15% of the torque. This study introduces a new method to quantify the contributions of the central and stretch reflex pathways to postural control; the method also provides an estimate of noisy intrinsic torque with significantly increased signal to noise ratio, suitable for identification of intrinsic stiffness in standing. The method can be used in different experimental conditions and requires minimal a-priori assumption regarding the role of different pathways in postural control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".