Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar

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.

Keywords

• Continuous Wave radar
• dataset
• machine learning
• body stature variability
• physiological variability