Desirability in design for safety: Developing life jacket through creative problem solving method of TRIZ
Bibliographic record
Abstract
Drowning is one of the main causes of death worldwide and according to the recent statistics published by coast guards of the US, Canada, and UK, most of drowned people were not using a life jacket. The main reason to refuse wearing a lifejacket considered being undesirable design of current samples.This project intended to develop more desirable product and in order to achieve this goal, some of the main TRIZ tools were utilized in five phases of product development process. Moreover, in order to examine our hypothesis, different studies on behavioural factors associated with life jacket use were reviewed and this became clear which boaters avoid wearing life jackets due to its bulky, uncomfortable and restrictive design. The product appearance and usefulness also were questioned by some boaters, when they indicated life jacket is just suitable for weak swimmers.Final result of this project presented in form of a concept which grants user's needs in both normal and emergency situations and makes it beneficial in all phases of the product using process. This concept combines life jacket, shoulder bag, smartphones capabilities and survival kit in one product to improve product desirability, usability and also its appearance.
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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 | 0.004 | 0.008 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".