Non invasive skin hydration level detection using machine learning

Liaqat, S., Dashtipour, K., Arshad, K. and Ramzan, N. (2020) Non invasive skin hydration level detection using machine learning. Electronics, 9(7), 1086. (doi: 10.3390/electronics9071086)

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Abstract

Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions.

Item Type:Articles
Additional Information:Funding: This work is supported in part by the Ajman University Internal Research Grant.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Dashtipour, Dr Kia
Creator Roles:
Dashtipour, K.Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
Authors: Liaqat, S., Dashtipour, K., Arshad, K., and Ramzan, N.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Electronics
Publisher:MDPI
ISSN:2079-9292
ISSN (Online):2079-9292
Copyright Holders:Copyright © 2020 by the authors
First Published:First published in Electronics 9(7):1086
Publisher Policy:Reproduced under a Creative Commons license

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