High‐Performance Textile‐Based Capacitive Strain Sensors via Enhanced Vapor Phase Polymerization of Pyrrole and Their Application to Machine Learning‐Assisted Hand Gesture Recognition
Bibliographic record
Abstract
Sensors based on everyday textiles are extremely promising for wearable applications. The present work focuses on high‐performance textile‐based capacitive strain sensors. Specifically, a conductive textile is obtained via vapor‐phase polymerization of pyrrole, in which the usage of methanol co‐vapor and the addition of imidazole to the iron chloride oxidant solution are shown to maximize conductivity. A technique to provide insulation and mechanical resistance using thermoplastic polyurethane and polystyrene‐block‐polyisoprene‐block‐polystyrene/barium titanate composite is developed. Such insulated conductive elastics are then used to fabricate highly sensitive twisted yarn capacitive sensors. A textile glove is subsequently embedded with such sensors. The wireless measurement and transmission system demonstrate efficacy in capturing capacitance variations upon strain and monitoring hand motions. A machine learning model to recognize 12 gestures is implemented—100% classification accuracy is obtained.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".