Ultra-Low-Power Wearable ECG Model with Cost-Effective Wireless Sensing System Through Convolutional Neural Network
Bibliographic record
Abstract
Research is being conducted on an Ultra-Low-Power Wearable ECG Model with Cost-Effective Wireless Sensing System Through Convolutional Neural Network (UWECWC) for applications in health monitoring, structural assessment, and environmental sensing. A deep learning framework has been designed to detect epileptic seizures by processing Inertial Measurement Unit (IMU) and Electroencephalogram (EEG) data through convolutional neural networks (CNNs), using sliding window techniques to enhance classification while optimizing computational resources and reducing artifacts. A low-power wearable Electrocardiogram (ECG) model enables real-time detection of cardiac arrhythmias during physical activity, featuring energy harvesting via solar panels and a compact electronic design that promotes communication and power management. A wireless sensing system designed for ultra-high-performance concrete uses a two-probe resistance measurement technique along with low-power management features to enable continuous and energy-efficient monitoring. The following parameters are calculated for UWECWC is sensitivity and specificity calculation, power consumption, accuracy calculation, amplitude calculation, and sensitivity & AUC calculation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".