| Autor: |
Jessica Hanna, Youssef Tawk, Sami Azar, Ali H. Ramadan, Batoul Dia, Elias Shamieh, Sumaya Zoghbi, Rouwaida Kanj, Joseph Costantine, Assaad A. Eid |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-022-19251-z |
| Popis: |
Abstract This work introduces novel body-matched, vasculature-inspired, quasi-antenna-arrays that act as electromagnetic sensors to instantaneously, continuously, and wirelessly sense glucose variations in the bloodstream. The proposed sensors are personalized, leverage electromagnetic waves, and are coupled with a custom machine-learning-based signal-processing module. These sensors are flexible, and embedded in wearable garments such as socks, which provide conformity to curved skin surfaces and movement resilience. The entire wearable system is calibrated against temperature, humidity, and movement resulting in high accuracy in glucose variations tracking. In-Vivo experiments on diabetic rats and pigs exhibit a 100% diagnostic accuracy over a wide range of glucose variations. Human trials on patients with diabetes and healthy individuals reveal a clinical accuracy of continuous glucose monitoring of 99.01% in twenty-eight subjects who underwent Oral Glucose Tolerance Tests. Hence, our approach ensures the continuous tracking of glucose variations from hypo-to-hyper glycemic levels with great fidelity. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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