Measurement of Vibrations and Evaluation of Protective Gloves for Work with Hand-held Power Tools in Industrial Settings
Bibliographic record
Abstract
This study considers the use of hand-held power tools and the exposure of a large number of employees to hand-arm vibrations in work settings as well as the harmful effects that such exposure has on health and safety. The major objective of the project was the development of a diagnostic device for the detection and monitoring of the vibrations produced during work activities in a natural working environment and to analyze the impact of the vibrations on workers during different work operations. The developed device for vibration monitoring is based on the latest generation MEMS tri-axis accelerometer with a wireless link with the PC station. This study demonstrates the use of the device in evaluating the level of protection that gloves provide concerning the level of vibrations during work operations. The initial evaluation shows that the proposed solution provides an effective multifunctional, low-cost diagnostic device for vibration measurement in natural work settings. Preliminary results indicate that the developed device could be used for health and safety studies, evaluation of protective equipment, and ongoing monitoring in a natural working environment, and in this way may lead to more effective prevention and management of the risks associated with exposure to workplace vibrations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".