TY - JOUR
T1 - Non-Invasive Hydration Level Estimation in Human Body Using Galvanic Skin Response
AU - Rizwan, Ali
AU - Abu Ali, Najah
AU - Zoha, Ahmed
AU - Ozturk, Metin
AU - Alomainy, Akram
AU - Imran, Muhammad Ali
AU - Abbasi, Qammer H.
N1 - Funding Information:
Manuscript received October 25, 2019; accepted December 27, 2019. Date of publication January 10, 2020; date of current version April 3, 2020. This work was supported by the Project AARE17-019 provided by the ADEC Award for Research Excellence, Abu Dhabi, United Arab Emirates University. The associate editor coordinating the review of this article and approving it for publication was Dr. Amitava Chatterjee. (Corresponding author: Najah Abu Ali.) Ali Rizwan, Ahmed Zoha, Metin Ozturk, Muhammad Ali Imran, and Qammer H. Abbasi are with the James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K. (e-mail: a.rizwan.1@research.gla.ac.uk; ahmed.zoha@glasgow.ac.uk; muhammad.imran@glasgow.ac.uk; qammer.abbasi@glasgow.ac.uk).
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - GSR
KW - Skin conductance level (SCL)
KW - bio-sensors data
KW - electrodermal activity (EDA)
KW - hydration level
KW - machine learning (ML)
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U2 - 10.1109/JSEN.2020.2965892
DO - 10.1109/JSEN.2020.2965892
M3 - Article
AN - SCOPUS:85083035292
SN - 1530-437X
VL - 20
SP - 4891
EP - 4900
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 9
M1 - 8955795
ER -