Artificial Neural Network for Radar-based Respiration Detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A continuous wave (CW) radar system that is designed to detect respiration and heart rate of a human, is presented. The radar operates at 5.8 GHz and has two outputs, the in-phase (I) and quadrature (Q) signals. The respiration and heartbeat frequencies can be extracted by processing these outputs using traditional methods, such as Fast Fourier Transform (FFT) and simple peak searches. Emphasis is given to the developed artificial neural network (ANN) used to determine the respiration frequency of the waveform. The motivation is that ANNs are faster than FFT-based approaches. The proposed method consists of training, validating and testing the ANN model with digitally constructed waves emulating respiration waveforms. Real world scenario measurement data from the CW radar system are then fed to the ANN model and the respiration frequency is obtained consistently. Experimental results demonstrate that this approach is on average 91.84% accurate and effective.
Original languageEnglish
Title of host publicationXXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS)
PublisherIEEE
ISBN (Electronic)9789463968096
DOIs
Publication statusPublished - 3 Oct 2023
Event35th General Assembly and Scientific Symposium of the International Union of Radio Science 2023 - Sapporo, Japan
Duration: 19 Aug 202326 Aug 2023

Conference

Conference35th General Assembly and Scientific Symposium of the International Union of Radio Science 2023
Abbreviated titleURSI GASS 2023
Country/TerritoryJapan
CitySapporo
Period19/08/2326/08/23

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