Wearable Microsystem for Minimally Invasive, Pseudo-Continuous Blood Glucose Monitoring: The e-Mosquito
Bibliographic record
Abstract
This paper presents a wearable microsystem for minimally invasive, autonomous, and pseudo-continuous blood glucose monitoring, addressing a growing demand for replacing tedious fingerpricking tests for diabetic patients. Unlike prevalent solutions which estimate blood glucose levels from interstitial fluids or tears, our design extracts a whole blood sample from a small lanced skin wound using a novel shape memory alloy (SMA)-based microactuator and directly measures the blood glucose level from the sample. In vitro characterization determined that the SMA microactuator produced penetration force of 225 gf, penetration depth of 3.55 mm, and consumed approximately 5.56 mW·h for triggering. The microactuation mechanism was also evaluated by extracting blood samples from the wrist of four human volunteers. A total of 19 out of 23 actuations successfully reached capillary vessels below the wrists producing blood droplets on the surface of the skin. The integrated potentiostat-based glucose sensing circuit of our e-Mosquito device also showed a good linear correlation (R2= 0.9733) with measurements using standard blood glucose monitoring technology. These proof-of-concept studies demonstrate the feasibility of the e-Mosquito microsystem for autonomous intermittent blood glucose monitoring.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".