Neuromusculoskeletal Dynamic Modelling of Human Movement in Motion Environments
Bibliographic record
Abstract
Personnel onboard high-speed marine craft are exposed to eccentric slam impacts of up to 20 g due to hull separation from water during operation. To maintain postural stability, occupants adopt a semi-squatted position to attenuate the high-acceleration loading, which causes severe acute and chronic musculoskeletal injuries, and hinders post-transit performance. The harsh environment warrants a thorough understanding of human-body behaviour to predict responses and quantify energy expenditure in maintaining postural stability. A comprehensive, three degree-of-freedom sagittal-plane musculoskeletal dynamic model was developed to estimate musculotendon forces from neuromuscular stimuli and joint kinematics to provide estimates of joint torques and muscle energetics. The model was validated through experimental trials with seven participants and indicates good agreement with torque profiles obtained through inverse dynamics. The framework provides general applicability to postural stability in a wide range of motion environments and supports future investigation of injury criteria and occupant-seat interaction on high-speed craft.
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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 | 0.000 |
| 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 | 0.001 | 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".