A comprehensive three-dimensional dynamic model of the human head and trunk for estimating lumbar and cervical joint torques and forces from upper body kinematics
Bibliographic record
Abstract
Linked-segment representations of human body dynamics have been used extensively in biomechanics, ergonomics, and rehabilitation research to systemize thinking, make predictions, and suggest novel experiments. In the scope of upper body biomechanics, these models play an even more essential role as the human spine dynamics are difficult to study in vivo. No study exists to date, however, that specifically disseminates the technical details of a comprehensive three-dimensional model of the upper body for the purpose of estimating spinal joint torques and forces for a wide range of scenarios. Consequently, researchers are still bound to develop and implement their own models. Therefore, the objective of this study was to design a dynamic model of the upper body that can comprehensively estimate spinal joint torques and forces from upper body kinematics. The proposed three-dimensional model focuses on the actions of the lumbar and cervical vertebrae and consists of five lumbar segments (L1 to L5), the thorax, six cervical segments (C2 to C7), and the head. Additionally, the model: (1) is flexible regarding the kinematic nature of the spinal joints (free, constrained, or fixed); (2) incorporates all geometric and mass-inertia parameters from a single, high-resolution source; and (3) can be feasibly implemented via different inverse dynamics formulations. To demonstrate its practicality, the model was finally employed to estimate the lumbar and cervical joint torques during perturbed sitting using experimental motion data. Considering the growing importance of mathematical predictions, the developed model should become an important resource for researchers in different fields.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".