Wireless, passive strain sensor in a doughnut-shaped contact lens for continuous non-invasive self-monitoring of intraocular pressure
Bibliographic record
Abstract
After cataract, glaucoma is the second leading cause of blindness worldwide and real-time monitoring of intraocular pressure (IOP) is of great demand. We present a wireless, passive sensor sitting inside a customized, planar and circular doughnut-shaped contact lens capable of continuous monitoring of the change in the curvature of cornea caused by IOP fluctuations. The sensor consists of a constant capacitor and a variable inductor in the form of a stretchable, closed-loop, serpentine wire that serves as both the sensor and the antenna. Results show a pressure responsivity of 523 kHz per 1% axial strain on a pressurized polydimethylsiloxane membrane and 35.1 kHz per 1 mmHg change in the IOP of a canine eye. The sensor is tested for stability and shows unvaried characteristics after repeated cycles and parasitic movements. Predictable influences of temperature and humidity on the sensor response are also verified experimentally, which can be canceled out using real-time calibration with temperature and humidity sensors to integrate with a reader device. The design reported here has numerous advantages, such as design simplicity, component reliability, high responsivity, and low cost, thereby opening up potential opportunities for the translation of this non-invasive, continuous IOP monitoring technique into clinical applications.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".