Engineering a Costume for Performance Using Illuminated LED-Yarns
Bibliographic record
Abstract
A goal in the field of wearable technology is to blend electronics with textile fibers to create garments that drape and conform as normal, with additional functionality provided by the embedded electronics. This can be achieved with electronic yarns (E-yarns), in which electronics are integrated within the fibers of a yarn. A challenge is incorporating non-stretch E-yarns with stretch fabric that is desirable for some applications. To address this challenge, E-yarns containing LEDs were embroidered onto the stretch fabric of a unitard used as part of a carnival costume. A zig-zag pattern of attachment of E-yarns was developed. Tensile testing showed this pattern was successful in preventing breakages within the E-yarns. Use in performance demonstrated that a dancer was unimpeded by the presence of the E-yarns within the unitard, but also a weakness in the junctions between E-yarns was observed, requiring further design work and reinforcement. The level of visibility of the chosen red LEDs within black E-yarns was low. The project demonstrated the feasibility of using E-yarns with stretch fabrics. This will be particularly useful in applications where E-yarns containing sensors are required in close contact with skin to provide meaningful on-body readings, without impeding the wearer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".