Non invasive skin hydration level detection using machine learning

Sidrah Liaqat, Kia Dashtipour, Kamran Arshad, Naeem Ramzan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
43 Downloads (Pure)


Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions.

Original languageEnglish
Article number1086
Pages (from-to)1-10
Number of pages10
JournalElectronics (Switzerland)
Issue number7
Publication statusPublished - 3 Jul 2020


  • machine learning
  • non-invasive sensing
  • skin conductance
  • skin hydration
  • wearable


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