Body stature and vital signs in radar sensing

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

Scientific Paper 12 February 2024 1 minute read

Authors: B. Soares, C. Gouveia, D. Albuquerque, P. Pinho
DOI: https://doi.org/10.3390/app14020921
Journal: Applied Sciences 2024, 14, 921
Read the full article: https://www.mdpi.com/2076-3417/14/2/921

The bio-radar system, useful for remotely monitoring vital signs, 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 92 subjects based on gender, age, Body Mass Index (BMI), and Chest Wall Perimeter (CWP). 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.