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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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%.
Original languageEnglish
Article number3715
Number of pages9
JournalScientific Reports
Volume12
Issue number1
DOIs
Publication statusPublished - 8 Mar 2022

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