(Keynote) Wearable Devices and Systems on Textiles for Biomedical Monitoring and Safety
Bibliographic record
Abstract
Great advances continue to be made in fields such wearable electronics, flexible electronics, functional polymers, and other soft materials. However, much of this research is field-specific, despite the enormous potential that the convergence of these research areas has to revolutionize personalized medicine, worker safety, and biomedical and environmental monitoring. We present our work in developing wearable devices and systems based on the convergence of multiple research areas in order to offer practical solutions for applications such as lighting and health monitors for safety vests; heart and perspiration monitors for athletic clothing; and other applications in real-time wearable bioelectric and biochemical monitoring. Intense research into wearable electronics results in many innovative devices and systems, e.g.: flexible polymer printed circuit boards (PCBs); roll-to-roll foil and other printed devices; printing and weaving of textiles; and special geometries for flexible interconnect between rigid components. Our lab develops alternative methods, including conductive polymer nanocomposites and metal transfer processes for wearable bioelectric sensors. We present textile-based wearable devices and systems for electrocardiogram (ECG), tissue impedance, and pressure sensors. Unlike many other techniques, our technologies and materials are highly compatible with clothing-based textiles, and result in non-polarizable electrodes for improved frequency response. Traditionally, biofluid-based biomedical sensors are fabricated in rigid substrates or flexible materials such as polydimethylsiloxane (PDMS) that are bonded to rigid substrates. Free-standing flexible devices are developed for, e.g., perspiration sensors; however, such processes typically require long fabrication times, equipment in a cleanroom facility, and difficulty with integration onto textiles. Other devices employ porous materials to deliver biofluids, e.g., perspiration, by capillary forces; however, such devices are limited in fluid collection and suffer from evaporation issues. To overcome these and other limitations of current devices, we present printing-based fabrication processes that employ screen printable ink for biomedical sensors for biofluid analysis. These devices and systems can be fabricated on textiles that can be laundered, facilitating rugged wearable devices and systems for biomedical and environmental monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.313 | 0.192 |
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".