Multidirectional quantification of trunk stiffness and damping during unloaded natural sitting
Bibliographic record
Abstract
Trunk instability during sitting is a major problem following neuromuscular injuries such as stroke and spinal cord injury. In order to develop new strategies for alleviating this problem, a better understanding of the intrinsic contributions of the healthy trunk to sitting control is needed. As such, this study set out to propose and validate a novel methodology for determining multidirectional trunk stiffness during sitting using randomized transient perturbations. Fifteen healthy individuals sitting naturally on a custom-made seat were randomly perturbed in eight horizontal directions. Trunk stiffness and damping were quantified using force and trunk kinematics in combination with translational and torsional models of a mass-spring-damper system. The results indicate that stiffness and damping of the healthy trunk are roughly symmetrical between the two body sides. Moreover, both quantities are smallest in the anterior and largest in the lateral directions. In conclusion, a novel protocol for identifying intrinsic trunk stiffness and damping has been developed, eliminating anticipation effects with respect to perturbation timing and direction. Subsequent studies will use these findings as a reference not only for quantifying trunk stiffness and damping in individuals with various neuromuscular disorders, but also for assessing whether neuroprostheses could increase upper body stiffness and, hence, stability.
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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 | 0.001 |
| 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) | 0.001 | 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".