Printed hybrid capacitive Kirigami sensor: enhancing flexibility and conformability for improved motion artifacts
Bibliographic record
Abstract
Abstract Capacitive sensing of electrophysiological signals is a promising alternative to traditional contact-type sensing for long-term and ubiquitous health monitoring. Many researchers are focusing on developing flexible capacitive electrodes to improve the conformability and the quality of acquisition of this family of sensors. However, current flexible devices still present many limitations due to the negative Poisson’s ratio of the materials used, which affects the dimensions and characteristics of the materials when under stress, and their incompatibility with traditional manufacturing methods and solid-state devices. We present a novel, inkjet-printed, hybrid capacitive Kirigami sensor design. This novel structure comprises different layers with different functionalities, in order to allow improved flexibility and conformability of the flexible Kirigami printed electrode, while securing its inclusion on a traditional rigid printed circuit board for a quality signal acquisition. The novel sensor design has been tested on different shapes and dimensions of sensing target and with different weights applied. Capacitive and electrical measurements were performed to obtain the main basic sensor characteristics such as coupled capacitance, acquired signal amplitude and cutoff frequency. When compared to an analog but rigid sensor, the novel designed hybrid flexible sensor showed significant improvement and enhanced uniformity of measurements, with an increase in amplitude value up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>82</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:mrow> </mml:math> for the bigger curvatures, while maintaining good electrical contact and integrity of all the layers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".