A Wearable Electrocardiography Sensor-System with Three-Dimensional Stretchable Interconnects
Bibliographic record
Abstract
The recent COVID-19 pandemic has proven that low-cost wireless wearable medical device which offer reliable continuous health monitoring can be extremely valuable and game changing tools in early diagnosis, control and management of contiguous disease. Current commercial wearables are not reliable and accurate enough for medical diagnosis, further they are rigid, bulky, and expensive. Here we present a skin-mounted, low-cost, soft and stretchable wearable electrocardiography (ECG) sensor-system which enables continuous and wireless ECG recording. The ECG sensor-system consists of low-cost, disposable, soft and stretchable, carbon-based electrodes patch; and a reusable, front-end and wireless circuits patch. The sensor patches are made by our developed low-cost scalable manufacturing method of spray-printing, on breathable, conformal, stretchable medical adhesives. The softness and thinness of electrodes patch ensure the conformability of electrodes to skin, maximizes the quality of sensing by decreasing the electrode-skin impedance and consequently improving the signal to noise ratio (SNR) and furthermore offer comfort to users. The reusable ECG circuit patch consists of three-dimensional stretchable/deformable interconnects (SDI) integrated with off-the-shelf integrated circuit components. SDI is made of microfluidic channels filled with liquid metal (LM). Unless other reported liquid metal based interconnects, SDI has been designed in 3D form to increase the stretchability and make it robust to deformations in all three x, y and z axis. The sensor-system is also waterproof and the developed disposable sensors are breathable and more comfortable than commercial wet gel electrodes. The recorded ECG signal can be sent to a personal device such as tablet and laptop wirelessly and be displayed in real-time. The developed sensor-system is a platform that can be applied for the construction of various types of sensor-systems with other sensing capabilities. Figure 1
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".