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DOI: 10.4121/f62008b2-4c3a-42c6-bf3a-c55e37a9598c

Haakma, Jelte; Turco, Simona; Peri, Elisabetta; Mischi, Massimo (2025): Models, datasets, and raw results of "Measurement of sweat gland activity by discrete sweat sensing, statistics, and deep learning". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/f62008b2-4c3a-42c6-bf3a-c55e37a9598c.v1

Categories

• Biomedical Engineering
• Engineering

Keywords

bio-fluid biomarker Deep learning discrete sweat sensing patient monitoring sweat sensing device

Time coverage

n.v.t.

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

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by Haakma Jelte , Simona Turco, Elisabetta Peri , Massimo Mischi

The paper investigated the use of deep learning for deriving the number of active sweat glands and the sweat rate per gland from a(n in-silico) discrete sweat sensing device. The study was completely in silico. This dataset includes the trained neural networks that were evaluated for this study (.keras, version in READ ME), the synthetic datasets that were used for training and testing (.parquet) and the results of the tests (.xlsx). The latter contains more results than presented in the paper (including the precision and recall).

History

• 2025-06-27 first online, published, posted

Publisher

4TU.ResearchData

Format

zip file containing: .keras (models), .parquet (datasets), .xlxs (raw data)

Funding

• Penta (grant code PENTA-2019-Call 4-19017) Penta

Organizations

TU Eindhoven, Department of Electrical Engineering