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Designing a Wearable Wireless System for Real-time Bioimpedance Spectroscopy of Body Fluid

2023· article· en· W4390993433 on OpenAlexaff
Antonio Bandur, Delaram Sadatamin, Bryan Piper, Ivana Čuljak, Hrvoje Džapo, Azadeh Yadollahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsWearable computerWirelessComputer scienceMicrocontrollerBluetoothSTM32Embedded systemComputer hardwareOperating systemTelecommunicationsChip

Abstract

fetched live from OpenAlex

This paper introduces a wearable system for monitoring body fluid dynamics using a wireless technology. The system utilizes Bluetooth Low Energy (BLE) and the AFE4300 integrated circuit for bioimpedance spectroscopy. It combines the STM32 microcontroller and BLE connectivity with a custom-designed hardware platform, allowing real-time acquisition, processing, and analysis of bioimpedance data. The system offers a user-friendly solution for non-invasive monitoring of body fluid, enabling personalized healthcare approaches. The study involved six participants and compared the system's performance to the SFB7 ImpediMed system, as a gold-standard for bioimpedance measurement using gel-based and textile electrodes. The system demonstrated comparable performance to the SFB7 ImpediMed system for assessing leg and total body water. The findings underscore the potential of the system to enable real-time bioimpedance spectroscopy in a wearable and wireless environment, facilitating advancements in personalized healthcare. This holds particular significance for individuals with heart failure, as frequent monitoring of body fluid levels is critical in managing their condition.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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