Internet of medical things for non-invasive and non-contact dehydration monitoring away from the hospital: state-of-the-art, challenges and prospects

TL;DR

This paper reviews the latest internet of medical things-based approaches for non-invasive, non-contact dehydration monitoring, highlighting challenges, datasets, and future research directions for early detection outside hospital settings.

Contribution

It provides a comprehensive overview of innovative IoMT techniques for dehydration assessment, emphasizing multi-modal sensing and machine learning applications in non-invasive monitoring.

Findings

Critical examination of non-invasive hydration assessment studies

Discussion of multi-modal sensing methods and their impact

Identification of datasets supporting machine learning research

Abstract

Dehydration occurs when the body loses more water than it takes in. Mild dehydration can lead to fatigue, cognitive impairments, and physical complications, while severe dehydration can cause life-threatening conditions like heat stroke, kidney damage, and hypovolemic shock. Traditional bio chemistry-based clinical gold standard methods are expensive, time-consuming, and invasive. Thus, there is a pressing need to design novel non-invasive methods that could do in-situ, early and accurate detection of dehydration, which will in turn allow timely intervention. This article presents a methodological review of the literature on a range of innovative internet of medical things-based techniques for dehydration monitoring. We begin by briefly describing the pathophysiology of the dehydration problem, its clinical significance, and current clinical gold-standard methods for assessing hydration level. Subsequently, we critically examine a number of non-invasive and non-contact hydration assessment studies. We also discuss multi-modal sensing methods and assess the impact of dehydration among specific population groups (e.g., elderly, infants, athletes) and on different organs. We also provide a list of existing public and private datasets which make the backbone of machine learning-driven research on dehydration monitoring. Finally, we provide our opinion statement on the challenges and future prospects of non-invasive and non-contact hydration monitoring.