An Integrated Rigid-Flexible Body Dynamic Approach to Computationally Efficient Musculoskeletal Modeling and Muscle Recruitment Simulation of the Lumbosacral Spine and Torso
Bibliographic record
Abstract
OBJECTIVE: In silico biomechanical models of the spine traditionally follow either rigid body dynamic (RBD) modeling (multibody modeling) or finite element (FE) modeling techniques. While RBD models lack robust representation for flexible tissues, FE models are computationally expensive. This study proposes an integrated rigid-flexible body dynamic (RFBD) architecture to address these limitations, and develops a full-torso human model, focusing spinal mechanical stability. METHODS: The model consisted of L1-L5 lumbar vertebrae, pelvis, sacrum, a lumped thoracic spine with ribcage as rigid bodies, while the intervertebral discs (IVDs), abdominal cavity and thoracolumbar fascia (TLF) were modeled as deformable reduced-order flexible bodies. Spinal ligaments were represented as nonlinear tension-only springs, while the musculature was modeled as tension-only forces. Level-by-level spinal stiffness was validated under pure flexion moments up to 7.5 Nm against literature studies. The reduced-order implementation was also validated against an identical FE model. Spinal stability contribution of different tissues in flexion was systematically evaluated using six on-off cases. RESULTS: Passive spine segmental stiffness profiles matched well with ex vivo and in silico comparators. The RFBD method demonstrated strong agreement with the FE solver, while significantly reducing computational demand. Stability analyses highlighted the role of intra-abdominal pressure in spinal unloading and generation of compressive loads along the spinal curvature through muscle recruitment. CONCLUSION: This parametric, fast-solving, high-fidelity spine simulation platform could be a useful biomechanical tool for spine researchers. SIGNIFICANCE: A novel human torso model with integrated rigid and flexible bodies was presented in this study, providing insights into mechanical spine stability.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".