@inproceedings{75d604b723024f5d8cb4b34702384655,
title = "Skin Conductance as Proxy for the Identification of Hydration Level in Human Body",
abstract = "The skin dehydration level can be used to infer serious health conditions in patients since diseases like cardiovascular abnormality, diabetes and cancer symptoms do exhibit correlation with skin disorders. Therefore a systematic analysis of human skin hydration levels is critical for multiple health care applications. Motivated by this, in this study we proposed a unique approach of measuring body hydration levels against different body postures using skin conductance electrical activity. In this paper, we report the collection, processing and analysis techniques used in the analysis of skin conductance data. Subsequently in order to predict body hydration levels we employed state-of-the-art machine learning models using the skin conductance data and achieved 81.82% and 73.91% recognition accuracy for the data of standing and sitting postures, respectively using KNN model.",
keywords = "Bio-Sensors Data, Classification, EDA, GSR, Hydration Level, Machine Learning, SCL, SCR",
author = "A. Rizwan and A. Zoha and A. Alomainy and Ali, {N. A.} and Imran, {M. A.} and Abbasi, {Q. H.}",
note = "Funding Information: ACKNOWLEDGMENT This work is funded by project AARE17-019 provided by the ADEC Award for Research Excellence, Abu Dhabi, United Arab Emirates Univesity. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE MTT-S International Microwave Biomedical Conference, IMBioC 2019 ; Conference date: 06-05-2019 Through 08-05-2019",
year = "2019",
month = may,
doi = "10.1109/IMBIOC.2019.8777806",
language = "English",
series = "IEEE MTT-S 2019 International Microwave Biomedical Conference, IMBioC 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE MTT-S 2019 International Microwave Biomedical Conference, IMBioC 2019 - Proceedings",
}