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E-Health Tracker: An IoT-Cloud Based Health Monitoring System

2022· article· en· W4214541594 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2022
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsComputer scienceCloud computingMicrocontrollerSensor nodeWireless sensor networkReal-time computingChannel (broadcasting)Node (physics)Computer networkEmbedded systemOperating systemEngineeringKey distribution in wireless sensor networksWireless

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.283
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it