Recent advances in MEMS sensor technology – biomedical applications
Bibliographic record
Abstract
Micro-electromechanical systems (MEMS) use microminiature sensors and actuators. MEMS technology provides the benefits of small size, low weight, high performance, easy mass-production and low cost. This article is the first part of a three-part series on MEMS sensors. In the present article, we provide a general introduction to MEMS sensing and the primary sensing techniques. Next, MEMS-based bio-medical sensors are explained. We consider MEMS devices that are: designed to detect triglycerides, c-reactive protein, and glucose, respectively; bio-inspired robotic fingers with tissue softness characterization sensors for pressure measurement during surgical procedures; for counting blood cells; acoustic sensors for 2-D sound source localization; pressure measurement sensors on the wings of an insect-like flying robot; and ultra-miniature sensors for intramuscular pressure measurement. The second part of the series will be dedicated to mechanical sensors. There, some related technologies of MEMS sensors will be discussed including compensation for environmental effects, the Casimir effect, and harvesting of energy for self-powered sensors. Also, the subject of sensor selection will be addressed. The third part will treat MEMS sensing in the thermo-fluid and electro-magnetic domains.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".