Ready-to-wear strain sensing gloves for human motion sensing
Bibliographic record
Abstract
Integrating soft sensors with wearable platforms is critical for sensor-based human augmentation, yet the fabrication of wearable sensors integrated into ready-to-wear platforms remains underdeveloped. Disposable gloves are an ideal substrate for wearable sensors that map hand-specific gestures. Here, we use solution-based metallization to prepare resistive sensing arrays directly on off-the-shelf nitrile butadiene rubber (NBR) gloves. The NBR glove acts as the wearable platform while its surface roughness enhances the sensitivity of the overlying sensing array. The NBR sensors have a sheet resistance of 3.1 ± 0.6 Ω/sq and a large linear working range (two linear regions ≤70%). When stretched, the rough NBR substrate facilitates microcrack formation in the overlying metal, enabling high gauge factors (62 up to 40% strain, 246 from 45 - 70% strain) that are unprecedented for metal film sensors. We apply the sensing array to dynamically monitor gestures for gesture differentiation and robotic control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".