Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar
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 addres...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/921 |
| _version_ | 1827369829108023296 |
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| author | Beatriz Soares Carolina Gouveia Daniel Albuquerque Pedro Pinho |
| author_facet | Beatriz Soares Carolina Gouveia Daniel Albuquerque Pedro Pinho |
| author_sort | Beatriz Soares |
| collection | DOAJ |
| description | 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—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest—to 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. |
| first_indexed | 2024-03-08T09:58:18Z |
| format | Article |
| id | doaj.art-e90af79e3cc346d0ae80e920031a17e8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:18Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e90af79e3cc346d0ae80e920031a17e82024-01-29T13:45:58ZengMDPI AGApplied Sciences2076-34172024-01-0114292110.3390/app14020921Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave RadarBeatriz Soares0Carolina Gouveia1Daniel Albuquerque2Pedro Pinho3Instituto de Telecomunicações, 3810-193 Aveiro, PortugalColab Almascience, Madan Parque, 2829-516 Caparica, PortugalCISeD, Polytechnic of Viseu, 3504-510 Viseu, PortugalInstituto de Telecomunicações, 3810-193 Aveiro, PortugalThe 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—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest—to 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.https://www.mdpi.com/2076-3417/14/2/921Continuous Wave radardatasetmachine learningbody stature variabilityphysiological variability |
| spellingShingle | Beatriz Soares Carolina Gouveia Daniel Albuquerque Pedro Pinho Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar Applied Sciences Continuous Wave radar dataset machine learning body stature variability physiological variability |
| title | Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar |
| title_full | Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar |
| title_fullStr | Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar |
| title_full_unstemmed | Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar |
| title_short | Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar |
| title_sort | impact and classification of body stature and physiological variability in the acquisition of vital signs using continuous wave radar |
| topic | Continuous Wave radar dataset machine learning body stature variability physiological variability |
| url | https://www.mdpi.com/2076-3417/14/2/921 |
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