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)
|
Text
207708.pdf - Accepted Version 1MB |
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, Professor 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 |
University Staff: Request a correction | Enlighten Editors: Update this record