Designing a Wearable Wireless System for Real-time Bioimpedance Spectroscopy of Body Fluid
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".