Non-invasive hydration level estimation in human body using Galvanic Skin Response

Rizwan, A., Ali, N. A., Zoha, A. , Ozturk, M., Alomaniy, A., Imran, M. A. and Abbasi, Q. H. (2020) Non-invasive hydration level estimation in human body using Galvanic Skin Response. IEEE Sensors Journal, 20(9), pp. 4891-4900. (doi: 10.1109/JSEN.2020.2965892)

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Abstract

Dehydration and overhydration, both have mild to severe medical implications on human health. Tracking Hydration Level (HL) is, therefore, very important particularly in patients, kids, elderly, and athletes. The limited solutions available for the estimation of HL are commonly inefficient, invasive, or require clinical trials. Need for a non-invasive auto-detection solution is imminent to track HL on a regular basis. To the best of authors’ knowledge, it is for the first time a Machine Learning (ML) based auto-estimation solution is proposed that uses Galvanic Skin Response (GSR) as a proxy of HL in the human body. Various body postures, such as sitting and standing, and distinct hydration states, hydrated vs dehydrated, are considered during the data collection and analysis phases. Six different ML algorithms are trained using real GSR data, and their efficacy is compared for different parameters (i.e., window size, feature combinations etc). It is reported that a simple algorithm like K-NN outperforms other algorithms with accuracy upto 87.78% for the correct estimation of the HL.

Item Type:Articles
Additional Information:This work is funded by project AARE17-019 provided by the ADEC Award for Research Excellence, Abu Dhabi, United Arab Emirates University.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Rizwan, Ali and Abbasi, Dr Qammer and Imran, Professor Muhammad and Öztürk, Metin
Authors: Rizwan, A., Ali, N. A., Zoha, A., Ozturk, M., Alomaniy, A., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Sensors Journal
Publisher:IEEE
ISSN:1530-437X
ISSN (Online):1558-1748
Published Online:10 January 2020
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Sensors Journal 20(9): 4891-4900
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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