Vibration-Sensing Electronic Yarns for the Monitoring of Hand Transmitted Vibrations
Bibliographic record
Abstract
Overexposure to hand transmitted vibrations (HTVs) from prolonged use of vibrating power tools can result in severe injuries. By monitoring the exposure of a worker to HTVs, overexposure, and injury, can be mitigated. An ideal HTV-monitoring system would measure vibration were it enters the body, which for many power tools will be the palm and fingers, however this is difficult to achieve using conventional transducers as they will affect the comfort of the user and subsequently alter the way that the tool is held. By embedding a transducer within the core of a textile yarn, that can be used to produce a glove, vibration can be monitored close to where it enters the body without compromising the comfort of the user. This work presents a vibration-sensing electronic yarn that was created by embedding a commercially available accelerometer within the structure of a yarn. These yarns were subsequently used to produce a vibration-sensing glove. The purpose of this study is to characterize the response of the embedded accelerometer over a range of relevant frequencies and vibration amplitudes at each stage of the electronic yarn's manufacture to understand how the yarn structure influences the sensors response. The vibration-sensing electronic yarn was subsequently incorporated into a fabric sample and characterized. Finally, four vibration-sensing electronic yarns were used to produce a vibration-sensing glove that is capable of monitoring vibration at the palm and index finger.
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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 | 0.001 | 0.001 |
| 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".