Mechanical and Biomechanical Approaches for Measuring Protective Glove Adherence
Bibliographic record
Abstract
Workers wearing insufficiently adherent gloves must exert additional muscular effort to grip objects, leading to discomfort, pain and even musculoskeletal disorders of the upper limbs. The main goal of this preliminary study was to explore mechanical and biomechanical approaches for characterizing glove adherence in order to verify whether a purely mechanical method can provide results that are in agreement with a biomechanical method, which takes into account the human factor. A mechanical method was developed to measure the coefficient of friction (COF) between 23 glove models and a steel surface. A biomechanical method was also developed to measure the COF at the hand/glove and glove/steel interfaces. Six subjects performed tests with three glove models, and gave their perception of glove adherence. The comparison between the biomechanical and mechanical results revealed that both methods produced similar COF values for each glove/steel interface. Those values agreed with the subjects' perception. However, the biomechanical method revealed a stick-slip phenomenon for one out of the three glove models, which makes evaluation of the COF difficult. On the other hand, the proposed mechanical method is capable of measuring the static and dynamic COFs of various glove models. It also has the advantages of being simple, reproducible, and inexpensive.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".