On Supporting the Learning of Biomechanics Using Multidisciplinary Physical Prototyping
Bibliographic record
Abstract
Abstract Over the last decades, the use of multibody dynamics in biomechanics research has grown considerably and holds significant promises for the health and biomedical industries. Nowadays, it allows estimating internal data of the body that would be impractical or impossible to obtain experimentally, e.g. individual muscle forces. Also, multibody dynamics simulation allows one to constrain virtually any apparatus to the musculoskeletal system, helping to understand and improve the patient’s dynamic interactions with the device. The modeling and validation of human multibody models remain a tedious task to achieve for the research community and can vary significantly depending on the applications. Despite the advantages offered by the multibody modeling of the human body, its introduction in the biomedical engineering curriculum is not widespread. The present paper aims to evaluate the feasibility and the interest of introducing multibody modeling into multidisciplinary, real-world projects using 3D printed prototypes to add an experimental understanding of the difficulties and validation of the human body modeling. The proposed methodology is based on a literature review of the multibody dynamics teaching methods used in mechanical engineering, followed by a first pilot project and feedback from students and professors of the community through interviews. Finally, a project is proposed, using physical prototyping to support the learning.
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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".