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

Liaqat, S., Dashtipour, K., Rizwan, A., Usman, M., Shah, S. A. , Arshad, K., Assaleh, K. and Ramzan, N. (2022) Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing. Scientific Reports, 12, 3715. (doi: 10.1038/s41598-022-07754-8)

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%.

• Item Type: Articles
• Additional Information: This work is supported in part by SAFE_RH project under Grant No. ERASMUS+ CBHE - 619483-EPP-1-2020-1-UK-EPPKA2- CBHE and also by Ajman University Internal Research Grant No. 2021-IRG-ENIT-11.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rizwan, Ali and Dashtipour, Dr Kia and Usman, Dr Muhammad and Shah, Mr Syed

Creator Roles

Dashtipour, K.: Methodology, Software, Validation, Formal analysis, Resources, Writing – original draft, Writing – review and editing, Visualization
Usman, M.: Writing – original draft, Writing – review and editing

• Authors: Liaqat, S., Dashtipour, K., Rizwan, A., Usman, M., Shah, S. A., Arshad, K., Assaleh, K., and Ramzan, N.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Scientific Reports
• Publisher: Nature Research
• ISSN: 2045-2322
• ISSN (Online): 2045-2322
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Scientific Reports 12: 3715
• Publisher Policy: Reproduced under a Creative Commons License