An EIT-based piezoresistive sensing skin with a lattice structure
Bibliographic record
Abstract
Spatially distributed sensing has gained significant value in diverse domains, with wearables as a notable application. In this work, a flexible skin-like sensor was developed for distributed pressure sensing. The sensing skin comprised a carbon black/silicone composite lattice structure embedded in a silicone sheet. The lattice-patterned structure is a distinct departure from conventional uniform sensing skins. Electrical impedance tomography (EIT) was employed to reconstruct electrical resistance over the sensing area, which was then mapped into pressure distribution based on the principle of piezoresistivity. EIT offers continuity and design simplicity as it eliminates the need for internal wiring, making it a promising technique in the wearable industry. The lattice sensing skin offered favorable sensing attributes, including quick response and recovery (75 ms and 84 ms at 85 kPa), a linear response with sensitivity as high as 0.119 kPa−1, a full-scale range of at least 100 kPa, and high repeatability (∼0.5% drop in maximum relative resistance over 300 cycles). The sensing skin was responsive over its entire area in both flat and non-planar conditions and was able to detect both single- and multi-point touch. The sensitivity and tactile area detectability varied depending on the position of applied pressure over the sensing area. Future studies will examine other lattice patterns and conductive composite fillers with the intention to develop a framework for optimizing the lattice sensing skin for tailored accuracy and resolution.
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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) | 0.000 | 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 | 0.001 |
| 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".