Identification, Activity, and Biometric Classification using Radar-based Sensing

Le Nguyen, Constantino Alvarez Casado, Olli Silven, Miguel Bordallo Lopez

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

We explore the possibility of leveraging radar-based sensing systems to analyze vital signs for classification, user identification, and regression tasks. Specifically, we extract time-domain and frequency-domain features from distance, respiration, and pulse signals obtained by filtering radio-frequency signals. Our Random Forest classification models are trained on these features to recognize scenarios in which the radar data were collected, categorize individuals into age groups, and classify human activities. For classification, we achieved up to 94.7% of accuracy when distinguishing apnea and normal breathing in the lying position. We then show the feasibility of identifying individuals in a small group using vital signs, which can support model fine-tuning with data acquired from new users. Furthermore, we used a Random Forest regression model to estimate the Body Mass Index, height, and weight of subjects. These classification, identification, and regression models benefit smart systems that can simultaneously identify users, recognize their behaviours, and extract their vital signs from radar sensors.

Original languageEnglish
Title of host publication2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation, ETFA 2022
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages8
ISBN (Electronic)978-1-66549-996-5
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
Event27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022 - Stuttgart, Germany
Duration: 6 Sep 20229 Sep 2022

Conference

Conference27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022
Country/TerritoryGermany
CityStuttgart
Period6/09/229/09/22

Keywords

  • classification
  • radar
  • signal processing
  • user identification
  • vital signs

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