Models, datasets, and raw results of "Measurement of sweat gland activity by discrete sweat sensing, statistics, and deep learning"

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).

本研究探讨了利用深度学习从（计算机模拟，in silico）离散汗液传感设备中获取活跃汗腺数量与单腺体出汗速率的方法。本研究全程采用计算机模拟（in silico）方式开展。本次公开的数据集包含本研究中用于评估的经训练神经网络（格式为.keras，版本信息详见README文件）、用于训练与测试的合成数据集（格式为.parquet），以及测试结果文件（格式为.xlsx）。其中该测试结果文件涵盖的内容较论文中展示的更为丰富，包含精确率（Precision）与召回率（Recall）等指标。