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

doi:10.1038/s41598-022-07754-8

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

Research Centre for Intelligent Healthcare

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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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 language	English
Article number	3715
Number of pages	9
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-07754-8
Publication status	Published - 8 Mar 2022

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Funder

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.

Keywords

Biomedical engineering
Disease prevention

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41598-022-07754-8Licence: CC BY

PublishedFinal published version, 1.46 MBLicence: CC BY

Cite this

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.

In: Scientific Reports, Vol. 12, No. 1, 3715, 08.03.2022.

Research output: Contribution to journal › Article › peer-review

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AU - Assaleh, Khaled

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

Abstract

Bibliographical note

Funder

Keywords

UN SDGs

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