Integrating smart glasses in a hybrid manufacturing system: Towards a better understanding of impacts on productivity, quality and ergonomics/human factors
Bibliographic record
Abstract
Production process is progressively shifting away from fully automated towards hybrid alternatives. \nTechnology-assisted manual labor in manufacturing, more specifically low-volume processes, promises increased job \nproductivity and is expected to support the workers. This study aims to gain a better understanding of the impacts on \nproductivity, quality and ergonomics/human factors, when smart glasses are introduced in a hybrid system. \n10 recruited participants were asked to do four complex assemblies each with 15 repetitions using manual and air ratchets \nwith and without smart glasses. The data was collected through cameras, an eye-tracker, time measuring, NASA-TLX for \ntask workload and quality control with documented pictures of each finished assembly. \nResults show that completion time was shorter with the smart glasses and, with assembly repetition, participants skipped \nreading some instructions. Globally, the weighted and unweighted NASA-TLX were high for the physical and effort \nindicators. Participants’ individual scores however show important differences. All participants made assembly errors, \nwhether bracket alignment or loose bolts. The tools used (manual and air ratchet) had an impact on quality. \nThis paper presents preliminary results. More refined analysis of this study’s data is needed to better comprehend how to \nintegrate conventional, automated, and intelligent technology like smart glasses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".