Production method in rehabilitation based on digital molding technology
Bibliographic record
Abstract
ABSTRACT: This project presents the development of an automated workflow for designing custom wrist orthoses using digital molding technologies integrated within Autodesk Fusion 360. TradiWonal orthosis fabricaWon is Wme-consuming and dependent on expert intervenWon. By contrast, this system enables novice users to generate anatomically precise and customizable orthoses from 3D limb scans with minimal input. Key innovaWons include automated mesh reorientaWon, customizable trimming based on userdefined parameters, and advanced shell generaWon with integrated ergonomic features such as fillets, venWlaWon holes, and mounWng supports. The use of Python scripWng within Fusion 360 significantly reduces manual modeling tasks, ensuring consistency and efficiency. Validated across 16 representaWve cases, the method proves robust and adaptable for various forearm sizes, orientaWons, and clinical needs. While limitaWons remain in edge smoothing and hole distribuWon due to scan irregulariWes, the system provides a substanWal foundaWon for clinical and industrial applicaWons in rehabilitaWon contexts. Future improvements will aim to enhance geometric robustness and usability for real-world deployment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".