Sensors (Apr 2022)

mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning

  • Srikrishna Iyer,
  • Leo Zhao,
  • Manoj Prabhakar Mohan,
  • Joe Jimeno,
  • Mohammed Yakoob Siyal,
  • Arokiaswami Alphones,
  • Muhammad Faeyz Karim

DOI
https://doi.org/10.3390/s22093106
Journal volume & issue
Vol. 22, no. 9
p. 3106

Abstract

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A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.

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