[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125302-en":3,"doc-seo-125302-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125302,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Boosting Microwave Hydration Sensors Performance with Machine Learning Techniques","Study proposes innovative non-invasive microwave hydration sensing combined with machine learning for practical monitoring. A miniaturized complementary-split ring resonator (CSRR) in the 2–3 GHz band estimates human skin hydration via near-field interaction, while a separate 2.45 GHz patch antenna tracks drying of greenwood. Full-wave simulations validate penetration into skin layers and wood structures, followed by fabrication on cost-effective substrates and extensive testing on multiple media. Human volunteers are monitored over six days under varying dietary conditions; greenwood is measured over twenty days in a controlled climate chamber. MVDA-based ML analyzes spectral data, enabling accurate hydration-level categorization and demonstrating efficiency for real-world deployment.","Alma Mater Studiorum Università di Bologna Archivio istituzionale della ricerca  \nBoosting Microwave Hydration Sensors Performance with Machine Learning Techniques  \nThis is the final peer-reviewed author’s accepted manuscript (postprint) of the following publication:  \nPublished Version:  \nAfif, O. , Di Florio Di Renzo, A. , Trovarello, S. , Costanzo, A. , Tartagni, M. (2024) . Boosting Microwave Hydration Sensors Performance with Machine Learning Techniques. Institute of Electrical and Electronics Engineers Inc. [10.1109/imas61316.2024.10818105] .  \nAvailability:  \nThis version is available at: [https://hdl.handle.net/11585/1004674 since: 2025-02-11](https://hdl.handle.net/11585/1004674 since: 2025-02-11)[ ](https://hdl.handle.net/11585/1004674 since: 2025-02-11)Published:  \nDOI: [http://doi.org/10.1109/imas61316.2024.10818105](http://doi.org/10.1109/imas61316.2024.10818105)  \nTerms of use:  \nSome rights reserved. The terms and conditions for the reuse of this version of the manuscript are  \nspecified in the publishing policy. For all terms of use and more information see the publisher's website.  \nThis item was downloaded from IRIS Università di Bologna ( [https://cris.unibo.it/](https://cris.unibo.it/) ) .  \nWhen citing, please refer to the published version.  \n(Article begins on next page)  \n14 May 2025  \nBoosting Microwave Hydration Sensors Performance with Machine Learning Techniques  \nOumaima Afif, Alessandra Di Florio Di Renzo, Simone Trovarello, Alessandra Costanzo, and Marco Tartagni  \nDEI, University of Bologna, Italy  \ne-mail: {oumaima.afif2, alessandra.diflorio3, simone.trovarello2, alessandra.costanzo, marco.tartagni}@unibo.it  \nAbstract—This study introduces innovative non-invasive hydration microwave sensors combined with machine learning (ML) algorithms for monitoring purposes. Specifically, a miniaturized complementary-split ring resonator (CSRR) operating in the 2÷3 GHz band for assessing human skin hydration through near-field interaction, and a separate patch antenna operating at 2.45 GHz for tracking the drying process of greenwood. The design process involves full-wave simulations to evaluate the resonators and patch antenna capability to effectively penetrate skin layers and wood structures. After fabrication on cost-effective substrates, extensive testing measurements were conducted on different mediums. Human volunteers’ proximal wrist areas are monitored over six days with multiple daily measurements under various dietary conditions. Concurrently, the greenwood sample is assessed over twenty days in a controlled climate chamber. The spectral data obtained from the resonator and the patch antenna are analyzed using advanced multivariate data analysis (MVDA). The results confirm the method’s effectiveness in accurately categorizing hydration levels and emphasize its potential for practical hydration monitoring applications due to its cost-effectiveness and operational efficiency.  \nIndex Terms—CSRR, patch antenna, non-invasive measurements, hydration, and machine learning.  \nI. INTRODUCTION  \nThe assessment of hydration levels is crucial in many fields, from dermatology to materials science [1], [2] . Nondestructive testing and evaluation (NDT&E) methods have advanced significantly, particularly through the integration of microwave technology [3] . Microwave-based methods represent the future of non-invasive monitoring, capable of penetrating diverse materials and delivering comprehensive data without inducing harm or modifying the subject under investigation [3] . In dermatological applications, accurate and non-invasive monitoring of skin hydration is essential for diagnosing and managing various skin conditions, as well as for evaluating the efficacy of cosmetic and therapeutic treatments [4] . split ring resonator (SRR) and complementarysplit ring resonator (CSRR) are preferred methods for sensing and characterization in monitoring applications such as skin hydration, due to their precise performa","cbCaidpTtwDUk3SK","https://ap.wps.com/l/cbCaidpTtwDUk3SK","pdf",3566873,1,5,"English","en",105,"# Introduction\n# Sensor Design and Fabrication\n## CSRR","[{\"question\":\"What microwave sensors and frequency bands are used in this study?\",\"answer\":\"The work uses a CSRR operating in the 2–3 GHz band to assess human skin hydration and a separate patch antenna at 2.45 GHz to monitor greenwood drying.\"},{\"question\":\"How is the sensor performance evaluated?\",\"answer\":\"The design uses full-wave simulations, then fabrication on cost-effective substrates, followed by extensive test measurements across different media.\"},{\"question\":\"How are the collected spectral data analyzed to estimate hydration?\",\"answer\":\"Spectral data from both resonator and patch antenna are analyzed using multivariate data analysis (MVDA), which supports machine-learning-based categorization of hydration levels.\"}]","Boosting Microwave Hydration Sensors Performance with Machine Learning Techniques | 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microwave sensors and frequency bands are used in this study?","Question",{"text":75,"@type":76},"The work uses a CSRR operating in the 2–3 GHz band to assess human skin hydration and a separate patch antenna at 2.45 GHz to monitor greenwood drying.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the sensor performance evaluated?",{"text":80,"@type":76},"The design uses full-wave simulations, then fabrication on cost-effective substrates, followed by extensive test measurements across different media.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the collected spectral data analyzed to estimate hydration?",{"text":84,"@type":76},"Spectral data from both resonator and patch antenna are analyzed using multivariate data analysis (MVDA), which supports machine-learning-based categorization of hydration 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