Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar
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Published:2024-01-22
Issue:2
Volume:14
Page:921
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Soares Beatriz12ORCID, Gouveia Carolina3ORCID, Albuquerque Daniel45ORCID, Pinho Pedro12ORCID
Affiliation:
1. Instituto de Telecomunicações, 3810-193 Aveiro, Portugal 2. Departamento de Eletrónica, Telecomunicações e Informática, Universidade de Aveiro, 3810-193 Aveiro, Portugal 3. Colab Almascience, Madan Parque, 2829-516 Caparica, Portugal 4. CISeD, Polytechnic of Viseu, 3504-510 Viseu, Portugal 5. ESTGA, University of Aveiro, 3750-127 Águeda, Portugal
Abstract
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.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference55 articles.
1. CNN-based driver monitoring using millimeter-wave radar sensor;Jung;IEEE Sens. Lett.,2021 2. Kagawa, M., Suzumura, K., and Matsui, T. (2016, January 16–20). Sleep stage classification by non-contact vital signs indices using Doppler radar sensors. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA. 3. Gouveia, C., Tomé, A., Barros, F., Soares, S.C., Vieira, J., and Pinho, P. (2020). Study on the usage feasibility of continuous-wave radar for emotion recognition. Biomed. Signal Process. Control, 58. 4. Lubecke, O.B., Ong, P.W., and Lubecke, V.M. (2002, January 21). 10 GHz Doppler radar sensing of respiration and heart movement. Proceedings of the Proceedings of the IEEE 28th Annual Northeast Bio Engineering Conference, Philadelphia, PA, USA. 5. Ichapurapu, R., Jain, S., John, G., Lie, D.Y., Banister, R., and Griswold, J. (2009, January 20–21). A 2.4 GHz non-contact biosensor system for continuous vital-signs monitoring. Proceedings of the 2009 IEEE 10th Annual Wireless and Microwave Technology Conference, Clearwater, FL, USA.
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