Design and development of a customized 3D-printed assistive device using modular 3D blocks
Bibliographic record
Abstract
PURPOSE: 3D printing enables the production of customizable, cost-effective, and reproducible items, making it a promising approach for manufacturing assistive devices. This study aims to develop customized 3D-printed assistive devices using 3D blocks. METHODS: A 3D scanner was used to scan the limbs and trunks where the devices would be worn. The study utilized 3D blocks capable of undergoing subdivision surfaces to match the scanned external appearance of limbs and trunks. The mass-spring model and Gauss-Newton method were applied to optimize the subdivision surfaces, ensuring a better fit for users' hand shapes. Additionally, 3D blocks were used as design units for blending-based morphing, generating diverse 3D patterns. RESULTS: The proposed approach successfully enabled real-time manufacturing of customized external appearances for assistive devices. The resulting designs met usability, functionality, and aesthetic requirements. Usability testing, conducted using the Quebec User Evaluation of Satisfaction with Assistive Technology, demonstrated high satisfaction scores, confirming the effectiveness of 3D blocks in customizing assistive devices. CONCLUSIONS: By integrating 3D scanning and printing technologies, this study highlights the feasibility of using reverse engineering to develop personalized assistive devices. The findings suggest that the proposed method enhances user satisfaction and provides a practical approach to assistive device customization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".