Quantifying Segmental Contributions to Center-of-Mass Motion During Dynamic Continuous Support Surface Perturbations Using Simplified Estimation Models
Bibliographic record
Abstract
Investigating balance reactions following continuous, multidirectional, support surface perturbations is essential for improving our understanding of balance control in moving environments. Segmental motions are often incorporated into rapid balance reactions following external perturbations to balance, although the effects of these motions during complex, continuous perturbations have not been assessed. This study aimed to quantify the contributions of body segments (ie, trunk, head, upper extremity, and lower extremity) to the control of center-of-mass (COM) movement during continuous, multidirectional, support surface perturbations. Three-dimensional, whole-body kinematics were captured while 10 participants experienced 5 minutes of perturbations. Anteroposterior, mediolateral, and vertical COM position and velocity were calculated using a full-body model and 7 models with reduced numbers of segments, which were compared with the full-body model. With removal of body segments, errors relative to the full-body model increased, while relationship strength decreased. The inclusion of body segments appeared to affect COM measures, particularly COM velocity. Findings suggest that the body segments may provide a means of improving the control of COM motion, primarily its velocity, during continuous, multidirectional perturbations, and constitute a step toward improving our understanding of how the limbs contribute to balance control in moving environments.
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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".