A personalized model and optimization strategy for estimating blood glucose concentrations from sweat measurements
Our innovative optimization strategy, also leveraging more accurate modeling, shows promising results, paving the way for non-invasive blood glucose monitoring and, possibly, improved diabetes management.
Authors
- Jaap M. J. den Toonder (ORCID: https://orcid.org/0000-0002-5923-4456)
- Elisabetta Peri (ORCID: https://orcid.org/0000-0002-1231-9372)
- Lisa Klous (ORCID: https://orcid.org/0000-0002-9458-4830)
- Massimo Mischi (ORCID: https://orcid.org/0000-0002-1179-5385)
- Eduard Pelssers (ORCID: https://orcid.org/0000-0002-0584-8209)
- H.A.M. Daanen (ORCID: https://orcid.org/0000-0002-7459-0678)
- Xiaoyu Yin (ORCID: https://orcid.org/0009-0005-2713-8450)
Institutions
- Netherlands Organisation for Applied Scientific Research (NL)
- Eindhoven University of Technology (NL)
- Vrije Universiteit Amsterdam (NL)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108743
- Citations
- 4
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 2.70
Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek