Local Dynamic Stability of Spine Muscle Activation and Stiffness Patterns During Repetitive Lifting
Bibliographic record
Abstract
To facilitate stable trunk kinematics, humans must generate appropriate motor patterns to effectively control muscle force and stiffness and respond to biomechanical perturbations and/or neuromuscular control errors. Thus, it is important to understand physiological variables such as muscle force and stiffness, and how these relate to the downstream production of stable spine and trunk movements. This study was designed to assess the local dynamic stability of spine muscle activation and rotational stiffness patterns using Lyapunov analyses, and relationships to the local dynamic stability of resulting spine kinematics, during repetitive lifting and lowering at varying combinations of lifting load and rate. With an increase in the load lifted at a constant rate there was a trend for decreased local dynamic stability of spine muscle activations and the muscular contributions to spine rotational stiffness; although the only significant change was for the full state space muscle activation stability (p < 0.05). With an increase in lifting rate with a constant load there was a significant decrease in the local dynamic stability of spine muscle activations and the muscular contributions to spine rotational stiffness (p ≤ 0.001 for all measures). These novel findings suggest that the stability of motor inputs and the muscular contributions to spine rotational stiffness can be altered by external task demands (load and lifting rate), and therefore are important variables to consider when assessing the stability of the resulting kinematics.
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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".