IoT for remote wireless electrophysiological monitoring: proof of concept
Bibliographic record
Abstract
The Internet of Things (IoT) offers integrated sensing of all aspects of daily life. The field of healthcare offers the greatest potential for IoT to benefit society, but also presents significant challenges. A key component of IoT is the development of intelligent ubiquitous sensing. Achieving this requires circuits and systems that require low power and efficient computation. As a proof of concept, we present a prototype design of a continuous wireless electrocardiogram (ECG) monitoring device that uses a small, low-cost IoT wi-fi module to upload real-time data to the cloud. Two IoT cloud services were evaluated to record and plot real-time ECG data: IBM Bluemix and ThingSpeak. Preliminary data quality was analyzed using kurtosis and spectral distribution ratio. Future development is necessary to improve battery power and to implement real-time data analysis. Remote medical and health monitoring is an important step in supporting personalized predictive analytics, smart homes, and chronic illness management. The presented device has the potential to provide health professionals with real-time ECG data allowing for diagnosis of cardiac pathologies, monitoring of patients suffering from heart disease and/or patients recovering from cardiac conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".