{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:27:50Z","timestamp":1764937670372,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MCTES","award":["UIDB\/50008\/2020-UIDP\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020-UIDP\/50008\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The Bio-Radar system, useful for monitoring patients with infectious diseases and detecting driver drowsiness, has gained popularity in the literature. However, its efficiency across diverse populations considering physiological and body stature variations needs further exploration. This work addresses this gap by applying machine learning (ML) algorithms\u2014Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest\u2014to classify subjects based on gender, age, Body Mass Index (BMI), and Chest Wall Perimeter (CWP). Vital signs were collected from 92 subjects using a Continuous Wave (CW) radar operating at 5.8 GHz. The results showed that the Random Forest algorithm was the most accurate, achieving accuracies of 76.66% for gender, 71.13% for age, 72.52% for BMI, and 74.61% for CWP. This study underscores the importance of considering individual variations when using Bio-Radar, enhancing its efficiency and expanding its potential applications.<\/jats:p>","DOI":"10.3390\/app14020921","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T08:28:28Z","timestamp":1705998508000},"page":"921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8855-6323","authenticated-orcid":false,"given":"Beatriz","family":"Soares","sequence":"first","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, 3810-193 Aveiro, Portugal"},{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2077-2871","authenticated-orcid":false,"given":"Carolina","family":"Gouveia","sequence":"additional","affiliation":[{"name":"Colab Almascience, Madan Parque, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8897-9123","authenticated-orcid":false,"given":"Daniel","family":"Albuquerque","sequence":"additional","affiliation":[{"name":"CISeD, Polytechnic of Viseu, 3504-510 Viseu, Portugal"},{"name":"ESTGA, University of Aveiro, 3750-127 \u00c1gueda, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-7794","authenticated-orcid":false,"given":"Pedro","family":"Pinho","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, 3810-193 Aveiro, Portugal"},{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3500404","DOI":"10.1109\/LSENS.2021.3063086","article-title":"CNN-based driver monitoring using millimeter-wave radar sensor","volume":"5","author":"Jung","year":"2021","journal-title":"IEEE Sens. 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