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. 2022 Sep 1;12:14885. doi: 10.1038/s41598-022-19251-z

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Principle of a multi-sensing, multi-location approach. (A), i, the system is composed of two sensors targeting two on-body locations. Additional environmental and physiological sensors are added to the system to calibrate the temperature, humidity, sweat, and motion effects. ii, all these sensors are worn within the sock apparatus. iii, the system provides the glucose levels in mg/dl using a machine learning algorithm. (B) Targeted blood vessels to provide information about the blood dielectric properties. The reflected EM waves are monitored and correlated to the blood glucose variation. (C) Top sensing layer of the flexible leg sensor prototype. (D) The layer-by-layer layout of the integrated leg sensors where the bottom layer is composed of copper, the middle layer is a PET substrate and the top layer is a copper layer.