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. 2021 Jun 2;4:89. doi: 10.1038/s41746-021-00465-w

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© The Author(s) 2021

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — This study has four objectives: (1) Engineer features from non-invasive systems using a combined data-driven and domain-driven feature engineering approach. (2) Develop personalized glucose excursions definitions. (3) Classify glucose excursions using engineered features. (4) Build predictive models of glucose using both a population approach with leave-one-person-out cross validation and a personalized approach. Sensor placement for the study (Empatica E4 on the wrist and a Dexcom G6 continuous glucose monitor on the abdomen) is also shown.