Design of smart-textiles for segmental bio-impedance measurement of the leg
Bibliographic record
Abstract
Recently there have been significant interest in the design of textile-based electrodes which can be integrated into clothing to provide a convenient and repeatable measurement technique without the need for excessive patient effort. This study focuses on the development of textile-based electrodes for bio-impedance spectroscopy, a method used to measure body composition and fluid volumes. Our work involved testing different electrode materials, surface area, spacing, and polymer coatings to improve signal quality and durability. The results demonstrated that silver-based electrodes had the best performance, with strong correlations (r2=0.68-0.89, p<0.001) to the gold-standard gel-based electrodes in calculating resistance values. The textile electrodes exhibited consistent results over long-duration testing and were able to detect fluid changes in the legs, imposed by postural changes. However, challenges such as variability due to skin hydration and hair density were observed. Future work will focus on further optimization, including electrode size reduction for cost-effectiveness and the application of electrodes into a sock design. Overall, the study highlights the potential of textile-based electrodes for bio-impedance measurements and their potential for home-based monitoring of fluid-related conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".