Fabric-Lego: 3D-Printing Fabric-Based Lego-Compatible Designs for Assistive Wearables, Personalization, and Self-Expression
Bibliographic record
Abstract
Medical braces and assistive wearables serve critical functions in supporting mobility and aiding individuals with temporary disabilities. However, these devices often lack aesthetic appeal and fail to provide personal expression, leading to psychological challenges and social stigma. We propose a novel fabrication method, Fabric-Lego, which combines 3D-printing with traditional garment-making (pattern-making, sewing, ironing, and overlocking) to address these shortcomings. By integrating customizable Lego®-like blocks into wearable fabrics in accessible DIY ways, users can personalize their wearables while maintaining comfort and functionality. We present the fabrication process, including insights on materiality, pre-processing, 3D-printing, and post-processing steps. To demonstrate the design space and potential applications of our method, we implemented 3 prototypes: 1) a customizable arm sling cover, 2) a customizable finger splint cover, and 3) a T-shirt with integrated blocks. This approach offers a promising path for enhancing the user experience and empowering individuals to embrace their unique identities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".