Design and Implementation of an Open-Source and Internet-of-Things-Based Health Monitoring System
Bibliographic record
Abstract
Across the globe, COVID-19 had far-reaching impacts that included healthcare facilities, public health, as well as all forms of transport. Hospitals were experiencing staffing shortages at the same time as patients were experiencing healthcare issues. Consequently, even in developing countries without full access to technology, remote health monitoring became necessary. There was a greater severity of the pandemic in countries with fewer financial and technical resources. It became evident that such remote health monitoring systems that not only allowed the user to monitor their basic health information, but also to communicate that information to healthcare personnel, were essential. In this article, we present an open-source, Internet-of-Things (IoT)-based health monitoring system that is intended to mitigate the basic healthcare challenges posed by remote areas of developing countries. To facilitate remote health monitoring, an IoT server has been configured on an ESP32 chip as part of this study. The microcontroller was also connected to a Max 30100 sensor, a DHT11 sensor, and a global positioning system GPS module. As a result of this, the user is able to measure the heart rate (HR), blood oxygen level (SpO2), human body temperature, ambient temperature and humidity, as well as the location of the user. Through the internet protocol, the important vital signs can be displayed in real time on the dashboard using a private communication network. This article presents the details of a complete system design, implementation, testing, and results. Such systems can help limit the spread of infectious diseases like COVID-19.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".