Using Smart Glasses in assembly/disassembly: Current state of the art
Bibliographic record
Abstract
Smart glasses are entering the manufacturing sector. It is therefore \nimportant to summarize current knowledge about their utility, usability, \nrisks, and practical acceptability. A trilingual literature search covering \nmaterial published in the main engineering databases between \n2014 and 2020 was conducted. Smart glasses are not appropriate for \nall tasks and work contexts. They must obey multiple standards covering \nhuman-equipment interaction, the Internet of Things, and personal \nprotective equipment. Design, usability and acceptability criteria \nhave been proposed. Several challenges remain, notably because \nthese devices have not reached full technical maturity. Although a \nfew successful industrial implementation cases exist, more laboratory \nand field experiments must be conducted to provide clear and \ndetailed guidelines for the use of smart glasses in the workplace. Their \ndevelopment remains, however, a promising avenue towards expanding \nthe pool of available workers in manufacturing. In addition, such \nsmart tools are promising to contribute in mitigating contamination \nrisks (e.g., virus spreading) by reducing the need for hand-contact \nwith assembly/disassembly tasks instruction systems (PC keyboard/ \nmouse/touchscreen or paper instructions) in COVID and Post-COVID \nmanufacturing systems.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".