SmartTex - A DIY Textile-Based Multi-Modal Sensing System for Non-Invasive Health Monitoring Applications
Bibliographic record
Abstract
Wearable sensors are transforming non-invasive health monitoring by enabling real-time tracking of physiological signals. This work presents a low-cost, DIY (Do-It-Yourself) approach for fabric-based multimodal sensing, integrating electrochemical, deformation-based, and pressure sensing to monitor sweat glucose levels, breath rate, and step counts. The sensor platform is developed using commercially available Adafruit 1364 conductive fabric, leveraging accessible fabrication techniques such as heat bonding and Cricut Maker 3 machine-assisted cutting to ensure scalability and ease of use. The electrochemical glucose sensor is validated using Differential Pulse Voltammetry (DPV), achieving a sensitivity value of 2.94 μA μM–1, demonstrating a stable response to physiologically relevant sweat glucose concentrations. The deformation-based breath rate sensor effectively tracks breathing patterns, while the step counter sensor, with conductive fabric embedded in the shoe sole, detects ankle positions and ankle pressure variations. This multi-modal sensing platform demonstrates the feasibility of affordable, fabric-based sensors for personalized health monitoring, laying the foundation for the next generation of wearable, ubiquitous sensing technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".