Electrical Metamaterial‐Based Interconnects‐Enabled Highly Stretchable Wireless Electrocardiography Circuit
Bibliographic record
Abstract
Abstract Advancement of truly stretchable wireless circuits is crucial for the development of high‐fidelity wearables for health monitoring, human‐machine interfaces and body‐sensor network applications. Reported stretchable wireless circuits are capable of reliable functioning under tensile strain of up to 30%, which is not sufficient for applications on the parts of the body with greater deformation. Here, a novel strategy is reported for forming highly stretchable interconnects, namely electrical metamaterial‐based interconnect (EMI), that can be integrated with electronic components to develop complex stretchable circuits for various applications including wearables. EMIs are 3D microfluidic channels embedded in hyperelastic polymers, filled with liquid metal, gallium indium (GaIn). Unlike other metal conductors and liquid metal‐based interconnects reported so far, EMI shows metamaterial‐like property of reduction of its electrical resistance under strain. Using EMIs a highly stretchable wireless electrocardiography wearable is developed that functions reliably under up to 100% strain. The circuit includes amplifiers, filters, Bluetooth components, and a rechargeable battery and attaches to soft sensor patches via magnetic connectors. The use of the soft and stretchable circuit with the Young's modulus of 0.65 MPa along with soft sensors results in minimizing the motion artifacts significantly making it ideal for reliable long‐term health monitoring.
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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) | 0.001 | 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".