Applications of smart textiles in occupational health and safety
Bibliographic record
Abstract
Abstract Smart textiles can detect, react, and adapt to various stimuli; they offer promising perspectives in occupation health and safety (OH&S). The objective of this study was to identify technologies, solutions, and products based on smart textiles and flexible materials that could have an application in OH&S and provide a response to some of the current needs. The collection of information included three aspects: 1) technologies, solutions, and products involving smart textiles and flexible materials found in the literature over the period 2000-2016; 2) issues in OH&S associated with traditional textiles and flexible materials; and 3) current or foreseeable problems associated with the use of smart textiles and flexible materials in OH&S. Issues with traditional textiles and flexible materials in OH&S were cross-matched with technologies, solutions and products relevant to smart textiles and flexible materials. This allowed us to propose short, mid, and long-term developments that could provide a response to some of the current needs in OH&S. The analysis shows that smart textiles and flexible materials offer a promising response to current challenges observed with PPE used in OH&S. With manufacturing and R&D capabilities available in various countries, the feasibility of development of these solutions is very high.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".