Nonlinear neural control using feedback linearization explains task goals of central nervous system for trunk stability in sitting posture
Bibliographic record
Abstract
Abstract Objective. Characterizing the task goals of the neural control system for achieving seated stability has been a fundamental challenge in human motor control research. This study aimed to experimentally identify the task goals of the neural control system for seated stability. Approach. Ten able-bodied young individuals participated in our experiments, which allowed us to measure their body motion and muscle activity during perturbed sitting. We used a nonlinear neuromechanical model of the seated human, along with a full-state feedback linearization approach and optimal control theory for identifying the neural control system and characterizing its task goals. Main results. We demonstrated that the neural feedback for trunk stability during seated posture uses angular position, velocity, acceleration, and jerk in a linearized space. The mean squared error between the predicted and measured motor commands was less than 0.6% among all trials and participants, with a median correlation coefficient <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="("> <mml:mi>r</mml:mi> </mml:mfenced> </mml:math> of more than 0.9. Our identified optimal neural control primarily used trunk angular acceleration and near-minimum muscle activation to achieve seated stability while keeping the deviations of the trunk angular position and acceleration sufficiently small. Significance. Our proposed approach to neural control system identification relied on a performance criterion (e.g. cost function) explaining what the functional goal is and subsequently, finds the control law that leads to the best performance. Therefore, instead of assuming what control schemes the neural control might utilize (e.g. proportional-integral-derivative control), optimal control allows the motor task and the neuromechanical model to dictate a control law that best describes the physiological process. This approach allows for a mechanistic understanding of the neuromuscular mechanisms involved in seated stability and for inferring the task goals used by the neural control system to achieve the targeted motor behavior. Such neural control characterization can contribute to the development of objective balance evaluation tools and of bio-inspired assistive neuromodulation technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".