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Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing

Liaqat, Sidrah; Dashtipour, Kia; Rizwan, Ali; Usman, Muhammad; Shah, Syed Aziz; Arshad, Kamran; Assaleh, Khaled; Ramzan, Naeem


Sidrah Liaqat

Ali Rizwan

Muhammad Usman

Syed Aziz Shah

Kamran Arshad

Khaled Assaleh

Naeem Ramzan


Personalized hydration level monitoring play vital role in sports, health, wellbeing and safety of a person while performing particular set of activities. Clinical staff must be mindful of numerous physiological symptoms that identify the optimum hydration specific to the person, event and environment. Hence, it becomes extremely critical to monitor the hydration levels in a human body to avoid potential complications and fatalities. Hydration tracking solutions available in the literature are either inefficient and invasive or require clinical trials. An efficient hydration monitoring system is very required, which can regularly track the hydration level, non-invasively. To this aim, this paper proposes a machine learning (ML) and deep learning (DL) enabled hydration tracking system, which can accurately estimate the hydration level in human skin using galvanic skin response (GSR) of human body. For this study, data is collected, in three different hydration states, namely hydrated, mild dehydration (8 hours of dehydration) and extreme mild dehydration (16 hours of dehydration), and three different body postures, such as sitting, standing and walking. Eight different ML algorithms and four different DL algorithms are trained on the collected GSR data. Their accuracies are compared and a hybrid (ML+DL) model is proposed to increase the estimation accuracy. It can be reported that hybrid Bi-LSTM algorithm can achieve an accuracy of 97.83%.

Journal Article Type Article
Acceptance Date Feb 15, 2022
Online Publication Date Mar 8, 2022
Publication Date 2022
Deposit Date Aug 25, 2022
Publicly Available Date Aug 25, 2022
Journal Scientific Reports
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 12
Issue 1
Article Number 3715
Public URL


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