Low Powered Solar ECG with ZigBee Based Bio-Telemetry
Bibliographic record
Abstract
The advent of modern age has shown a drastic shift in the way humans have worked leading into sedentary lifestyles. Change in dietary pattern where fresh food is replaced by processed and fast food along with the increase of stress has led to rise of cardio-vascular disease which is glaringly evident in developing countries. Especially, Asians are more prone to cardio-vascular diseases genetically. The ECG device is a diagnostic medical instrument which determines the electrical activity of the heart. The conventional ECG devices are powered by mains electricity, thus are not energy efficient. Transformers used make the device bulky and expensive. Optimum isolation amplifiers have to be incorporated in these devices for patient safety, adding to the cost and complex circuit. We have designed a high quality solar electrocardiograph that is extremely energy efficient, cost effective and works on alternate energy, making it a completely eco-friendly product. Healthcare infrastructure is extremely weak in various developing countries of Asia, Africa and South America. Electricity is one of the major factor that drives the health care economy in these areas. Most of these areas lack basic electricity, thus have no access to good health care facilities. One of the most essential factors in developing a solar based ECG device is to give out an extremely energy efficient device yet excellent results and inexpensive in order to get net cost per test below $1. Having eliminated conventional power supply, the electro cardiogram is free from line frequency noise of 50/60 Hz, thus adding to excellent ECG quality. The device is incorporated with a traditional DC charging facility, thus can be charged with a normal cell phone charger in extreme case. The device is capable of acquiring ECG for more than 30 hours, at a sampling rate of 500 Hz. The ECG can be wirelessly transmitted with the help of low power wireless ZigBee, thus allowing monitoring ECG in real time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".