All-Textile Wearable Capacitive Pressure Sensors Based on Cut-Pile Fabrics with Integrated Electrodes
Bibliographic record
Abstract
Wearable pressure sensors have the potential to revolutionize healthcare and promote wellness through the detection and monitoring of vital signs and human motion. Although textiles are an ideal platform for wearable sensors due to their ubiquity in daily life, textile-based pressure sensors typically suffer from low sensitivity. Capacitive pressure sensors require a porous, deformable dielectric layer to achieve high sensitivity, and off-the-shelf textiles have not met this challenge. In this paper, we present all-textile capacitive pressure sensors based on off-the-shelf cut-pile fabrics, in which we use selective solution metallization to integrate the electrode and cut-pile dielectric layer into a single piece of fabric. The resulting sensors exhibit sensitivities (0.029 kPa –1 ) and response times (3 ms) suitable for monitoring motions of the human body. We demonstrate their utility to detect subtle human facial motions, as well as grip strength. Through a comparative analysis of different cut-pile fabrics, we show that the compressibility of the cut-pile layer and thus the sensitivity of the sensor depend on the specific attributes of the cut piles. This work provides not only a new approach to wearable textile-based sensor fabrication but also insight into the textile structure/performance relationships necessary to advance the field of e-textiles.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".