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A Wearable Temperature Sensor for IoT Applications

2023· article· en· W4408717144 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWearable computerInternet of ThingsComputer scienceIntelligent sensorWearable technologyWireless sensor networkEmbedded systemComputer network

Abstract

fetched live from OpenAlex

This paper presents a wearable device for IoT applications that accurately measures the body temperature of athletes during exercise. The device is a flexible passive capacitive sensor with a TPU insulator that makes the capacitance dependent on the temperature. This insulator was made with a 3D printer and can be made in any other shape for other applications. A microcontroller reads the sensor's capacitor value by sampling its voltage in an RC series structure and determining its charge time constant. Using a LoRa module, this device transmits the athlete's body temperature from long distances to the server, providing online monitoring of body temperature during exercise. The sensor was tested from 25°C to 45°C to cover athletes' skin temperature variations, showing about a 5% variation in this range. The simulation of the proposed sensor was done in CST Studio Suite, and the measurement results follow the simulation results quite well.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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