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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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 it