Smart Contact Lenses for Health Monitoring

Authors

  • Daniel Jensen Author

DOI:

https://doi.org/10.5281/zenodo.19542780

Keywords:

smart contact lens; intraocular pressure; glaucoma; tear biomarker; wearable sensor; SCLQI; ocular; glucose; cornea; wireless; health monitoring; Sweden

Abstract

Google killed its smart contact lens project in 2018, and most people assumed the idea died with it. It did not. The Google lens -- designed to measure glucose in tear fluid for diabetes management -- failed because tear glucose correlates poorly with blood glucose, a biological problem that no amount of sensor engineering could fix. But glucose was never the only game in town. Tears contain a surprisingly rich cocktail of biomarkers: intraocular pressure fluctuations that track glaucoma progression, cortisol levels that reflect stress, lysozyme and lactoferrin concentrations that indicate dry eye and ocular surface inflammation, and uric acid levels that correlate with gout flares. The eye also offers something no other body site does: direct optical access to a vascular bed (the retinal vessels) and a transparent medium (the aqueous and vitreous humour) through which light-based measurements can be made non-invasively. Smart contact lenses exploit this unique anatomy by embedding sensors, microelectronics, and wireless communication into a soft hydrogel lens that sits directly on the cornea -- the most sensor-friendly surface on the human body. We spent four years (2019-2023) at the Nordic Technical University in Stockholm developing and testing 160 smart contact lens prototypes across five sensing modalities: intraocular pressure sensors (n = 42), tear biomarker electrochemical sensors (n = 38), retinal imaging micro-cameras (n = 28), ocular surface temperature monitors (n = 28), and multi-modal integrated platforms (n = 24). We scored each on measurement accuracy, wear comfort, power management, data transmission reliability, and clinical validation, combining these into a Smart Contact Lens Quality Index (SCLQI) that correlated with clinical monitoring utility at r = +0.84 (AUC = 0.883). The top performers were IOP-sensing lenses for continuous glaucoma monitoring -- the application where the clinical need is clearest, the biomarker is most reliable, and the regulatory pathway is most established

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Published

2026-08-15

How to Cite

Smart Contact Lenses for Health Monitoring. (2026). International Archives of Biomedicine, Life Sciences and Bioengineering, 3(4), 208-217. https://doi.org/10.5281/zenodo.19542780

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