Embedded Pressure Sensing Metamaterials using TPU-Graphene Composites and Additive Manufacturing
Bibliographic record
Abstract
Nearly 15% of the global population is affected by disabilities impacting mobility. Monitoring foot pressure distribution during gait is a fundamental aspect of evaluating rehabilitation. Wearable systems provide a portable alternative to stationary equipment monitoring gait without laboratory space limitations. However, wearable sensors in some applications present challenges in the calibration, sensitivity, and human-sensor interface, requiring application-specific sensors. This study aimed to develop wearable sensors where the structural and material properties can characterise the sensitivity and range of measurement during the design phase. We developed wearable piezoresistive sensors using additive manufacturing to create mechanical metamaterials with embedded pressure-sensing capabilities. The sensors were fabricated in TPU using SLS and graphene ink infusion processes. Three structural designs were developed for different measuring ranges (0 – 50 N, 0 – 100 N, and 0 – 150 N) using body-centred cubic lattices constructed via pyramid unit cells. Two graphene infusion processes were evaluated. We tested the sensors’ mechanical and piezoresistive behaviour, measuring the compressive force, strain, and electrical resistance across the sensor. We analysed the influence of structural dimensions and the infusion process on the piezoresistive behaviour. The measuring range was affected mainly by tuneable structural dimensions. The infusion process influenced the piezoresistive sensitivity and affected the linearity response. The results indicate the characterisation of the sensitivity of piezoresistive sensors based on structural parameters and material properties. Mechanical metamaterials could embed pressure sensing in wearables, allowing for customisation based on design parameters using additive manufacture and graphene inks.
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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 | 0.000 |
| 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 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".