A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches.
hydration monitoring
hypohydration and euhydration
microwave transmission
non-invasive
regression analysis
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
25 Mar 2022
25 Mar 2022
Historique:
received:
17
02
2022
revised:
20
03
2022
accepted:
20
03
2022
entrez:
12
4
2022
pubmed:
13
4
2022
medline:
14
4
2022
Statut:
epublish
Résumé
Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2-3.5 GHz between a pair of planar antennas positioned across a participant's arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2-3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).
Identifiants
pubmed: 35408154
pii: s22072536
doi: 10.3390/s22072536
pmc: PMC9003514
pii:
doi:
Substances chimiques
Water
059QF0KO0R
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Paul L. Spainhour Professorship
ID : NA
Organisme : Michelle Munson - Serman Simu Keystone Research Scholar funds
ID : NA
Organisme : ECE undergraduate research funds - Kansas State University
ID : NA
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