---
title: "Body stature and vital signs in radar sensing - AlmaScience"
description: "Machine learning classifies 92 subjects by gender, age, BMI and chest wall perimeter, testing how body stature affects bio-radar vital sign accuracy."
url: "https://almascience.pt/news/scientific-paper/acquisition-of-vital-signs-using-continuous-wave-radar"
date: "2026-10-16T06:11:23+00:00"
language: "en-GB"
---

# News

  ![Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar]() ##  Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar

  [Scientific Paper](https://almascience.pt/news/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>
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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.

- [ Biometrics ](https://almascience.pt/component/tags/tag/biometrics)

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