Wireless pressure monitoring system utilizing a 3D-printed Origami pressure sensor array
Bibliographic record
Abstract
Abstract We present here a 3D-printed pressure mapping mat, equipped with customizable architecture sensors, that offers a cost-effective and adaptable solution, overcoming the size constraints and sensing accuracy issues commonly associated with existing commercial pressure mats across various fields, such as healthcare and sports applications. Leveraging a pillar-origami structure, the demonstrated sensor offers multifaceted stiffness properties, effectively filtering skin deformations and enabling capacitive pressure sensing. Notably, the sensor’s detection range can be finely tuned, spanning from 70 to 2500 kPa, with a sensitivity range between 0.01 kPa-1 and 0.0002 kPa-1, and an impressive response time of just 800 milliseconds. Furthermore, the inclusion of a modular sensor array enhances maintenance and allows for greater flexibility in shaping and enhancing the device’s resolution. This technology finds practical applications in wireless foot pressure mapping and sports protection pads, marking a significant milestone in the advancement of flexible and custom-shaped pressure sensor technology.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".