Low Power Wireless Body Area Networks with Compressed sensing theory
Bibliographic record
Abstract
Wireless Body Area Networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body. These wireless sensors are responsible for collecting, processing, and transmitting vital information such as: blood pressure, heart rate, respiration rate, electrocardiographic (ECG), electroencephalography (EEG) and oxygenation signals to provide continuous health monitoring with real-time feedback to the users and medical centers. In order to fully exploit the benefits of WBANs for important applications such as Electronic Health (EH), Mobile Health (MH), and Ambulatory Health Monitoring (AHM), the power consumption must be minimized. Since Wireless Nodes (WNs) in WBANs are usually driven by battery power consumption is the most important factor to determine the life of WBANs. This paper presents the applications of Compressed Sensing (CS) theory in WBANs. We have achieved networks with low-sampling rate and low-power consumption on a number of applications. A combination of CS theory to WBANs is the optimal solution for achieving the networks with low-sampling rate and low-power consumption. Our simulation results in ECG signals show that sampling rate can be reduced t0 25% and power consumption to 35% without sacrificing performances by employing the CS theory to WBANs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".