{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:06:14Z","timestamp":1772676374508,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Paul L. Spainhour Professorship","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"Michelle Munson - Serman Simu Keystone Research Scholar funds","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"ECE undergraduate research funds - Kansas State University","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u20133.5 GHz between a pair of planar antennas positioned across a participant\u2019s 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\u00b10.44% and 0.49\u00b10.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2\u20133.5 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%).<\/jats:p>","DOI":"10.3390\/s22072536","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:31:25Z","timestamp":1648416685000},"page":"2536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches"],"prefix":"10.3390","volume":"22","author":[{"given":"Deepesh","family":"Agarwal","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Philip","family":"Randall","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Zachary","family":"White","sequence":"additional","affiliation":[{"name":"Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Bayleigh","family":"Bisnette","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Jenalee","family":"Dickson","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Cross","family":"Allen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Faraz","family":"Chamani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6467-722X","authenticated-orcid":false,"given":"Punit","family":"Prakash","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Carl","family":"Ade","sequence":"additional","affiliation":[{"name":"Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Balasubramaniam","family":"Natarajan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Subudhi, A., Askew, E., and Luetkemeier, M. (2013). Dehydration. Reference Module in Biomedical Sciences Encyclopedia of Human Nutrition, Elsevier. [3rd ed.].","DOI":"10.1016\/B978-0-12-375083-9.00068-4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/RBME.2017.2776041","article-title":"Engineering approaches to assessing hydration status","volume":"11","author":"Garrett","year":"2017","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_3","first-page":"377","article-title":"American College of Sports Medicine exercise and fluid replacement position stand","volume":"39","author":"Sawka","year":"2007","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1249\/00005768-198508000-00009","article-title":"Influence of diuretic-induced dehydration on competitive running performance","volume":"17","author":"Armstrong","year":"1985","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1007\/BF01045276","article-title":"Effect of primary hypohydration on physical work capacity","volume":"32","author":"Pichan","year":"1988","journal-title":"Int. J. Biometeorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.1088\/0031-9155\/53\/24\/004","article-title":"Assessing skin hydration status in haemodialysis patients using terahertz spectroscopy: A pilot\/feasibility study","volume":"53","author":"Kadlec","year":"2008","journal-title":"Phys. Med. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schiavoni, R., Monti, G., Piuzzi, E., Tarricone, L., Tedesco, A., De Benedetto, E., and Cataldo, A. (2020). Feasibility of a wearable reflectometric system for sensing skin hydration. Sensors, 20.","DOI":"10.3390\/s20102833"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4891","DOI":"10.1109\/JSEN.2020.2965892","article-title":"Non-invasive hydration level estimation in human body using galvanic skin response","volume":"20","author":"Rizwan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/JERM.2019.2911849","article-title":"Feasibility Study of Hydration Monitoring Using Microwaves\u2013Part 1: A Model of Microwave Property Changes With Dehydration","volume":"3","author":"Garrett","year":"2019","journal-title":"IEEE J. Electromagn. RF Microw. Med. Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1088\/0967-3334\/25\/1\/005","article-title":"Hydration status measurement by radio frequency absorptiometry in young athletes\u2014a new method and preliminary results","volume":"25","author":"Moran","year":"2003","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/JERM.2019.2911909","article-title":"Feasibility Study of Hydration Monitoring Using Microwaves\u2013Part 2: Measurements of Athletes","volume":"3","author":"Garrett","year":"2019","journal-title":"IEEE J. Electromagn. Microw. Med. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kamran, F., Le, V.C., Frischknecht, A., Wiens, J., and Sienko, K.H. (2021). Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes. Sensors, 21.","DOI":"10.3390\/s21134469"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Alvarez, A., Severeyn, E., Vel\u00e1squez, J., Wong, S., Perpi\u00f1an, G., and Huerta, M. (2019, January 11\u201315). Machine Learning Methods in the Classification of the Athletes Dehydration. Proceedings of the 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador.","DOI":"10.1109\/ETCM48019.2019.9014877"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Reljin, N., Malyuta, Y., Zimmer, G., Mendelson, Y., Blehar, D.J., Darling, C.E., and Chon, K.H. (2018, January 20\u201321). Automatic Detection of Dehydration using Support Vector Machines. Proceedings of the 2018 14th Symposium on Neural Networks and Applications (NEUREL), Belgrade, Serbia.","DOI":"10.1109\/NEUREL.2018.8587008"},{"key":"ref_15","unstructured":"(2022, February 03). Keysight 85070E\u2014Dielectric Probe Kit 200 MHz to 50 GHz. Available online: https:\/\/www.keysight.com\/us\/en\/product\/85070E\/dielectric-probe-kit.html."},{"key":"ref_16","unstructured":"Britain, K. (2022, January 30). 2.4-GHz Patch Antennas. Available online: https:\/\/www.wa5vjb.com\/references\/2.4-GHz%20Patch%20Antennas%20by%20WA5VJB.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4081","DOI":"10.1007\/s00421-012-2390-0","article-title":"Hydration assessment using the cardiovascular response to standing","volume":"112","author":"Cheuvront","year":"2012","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1152\/jappl.1972.32.4.474","article-title":"Respiratory weight losses during exercise","volume":"32","author":"Mitchell","year":"1972","journal-title":"J. Appl. Physiol."},{"key":"ref_19","unstructured":"(2022, February 08). Clinical Refractometers. Available online: http:\/\/site.jjstech.com\/pdf\/Atago\/clinical_en.pdf."},{"key":"ref_20","unstructured":"(2022, January 15). Keysight Technologies\u2014N9923A FieldFox Handheld RF Vector Network Analyzer\u20144\/6 GHz. Available online: https:\/\/www.keysight.com\/us\/en\/assets\/7018-02396\/technical-overviews\/5990-5087.pdf."},{"key":"ref_21","unstructured":"Molnar, C. (2022, January 18). Interpretable Machine Learning, Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s00704-019-03048-8","article-title":"Estimation of soil moisture using decision tree regression","volume":"139","author":"Pekel","year":"2020","journal-title":"Theor. Appl. Climatol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kusakunniran, W., Wu, Q., Zhang, J., and Li, H. (2010, January 13\u201318). Support vector regression for multi-view gait recognition based on local motion feature selection. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540113"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"020026","DOI":"10.1063\/1.5085539","article-title":"Multi-classification of cardiac diseases utilizing wavelet thresholding and support vector machine","volume":"Volume 2058","author":"Qin","year":"2019","journal-title":"AIP Conference Proceedings"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9767","DOI":"10.1109\/ACCESS.2018.2794346","article-title":"ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications","volume":"6","author":"Venkatesan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Palimkar, P., Shaw, R.N., and Ghosh, A. (2022). Machine learning technique to prognosis diabetes disease: Random forest classifier approach. Advanced Computing and Intelligent Technologies, Springer.","DOI":"10.1007\/978-981-16-2164-2_19"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/JBHI.2020.2973324","article-title":"Multimodal data analysis of Alzheimer\u2019s disease based on clustering evolutionary random forest","volume":"24","author":"Bi","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111367","DOI":"10.1016\/j.biopha.2021.111367","article-title":"Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes","volume":"137","author":"Xia","year":"2021","journal-title":"Biomed. Pharmacother."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3389\/fmed.2020.00427","article-title":"Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging","volume":"7","author":"Yoo","year":"2020","journal-title":"Front. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2536\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:43:43Z","timestamp":1760136223000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,25]]},"references-count":29,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072536"],"URL":"https:\/\/doi.org\/10.3390\/s22072536","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,25]]}}}