Design and Implementation of an Open-Source and Internet-of-Things-Based Health Monitoring System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it