E-Health Tracker: An IoT-Cloud Based Health Monitoring System
Bibliographic record
Abstract
Health Management and its Monitoring during Pandemic is one of the major issues in not only our country, but the whole world. People are losing their lives due to the ignorance of their body's vitals (symptoms or signs of any disease). It is really important for one to keep track of their health, not only for themselves but also for those around them as well. Keeping this in mind, a proposed system titled E-Health Tracker was designed and constructed. Using ESP8266 Node MCU Wi-Fi Module, DS18B20 Temperature sensor probe, MAX 30100 Pulse Oximetry sensor and DHT-11 Temperature and Humidity sensor. A 0.96″ OLED screen is used to display all the readings from the sensors processed by the Node MCU ESP8266. In addition to that an Open-Source IOT Web API service called ThingSpeak which allows to aggregate, visualize, and analyze live data streams in the Cloud is utilized. A user can create a Channel by signing up and naming the Channel along with the Fields where the user wants to display the sensor data.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".