High Accuracy Respiration and Heart Rate Detection Based on Artificial Neural Network Regression

Yu Chiao Tsai, Shih Hsuan Lai, Ching Ju Ho, Fang Ming Wu, Lindor Henrickson, Chia Chien Wei, Irwin Chen, Vincent Wu, Jyehong Chen*

*Corresponding author for this work

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

Abstract

A 24GHz Doppler radar system for accurate contactless monitoring of heart and respiratory rates is demonstrated here. High accuracy predictions are achieved by employing a CNN+LSTM neural network architecture for regression analysis. Detection accuracies of 99% and 98% have been attained for heart rate and respiration rate, respectively.Clinical Relevance - This work establishes a non-contact radar system with 99% detection accuracy for a heart rate variability warning system. This system can enable convenient and fast monitoring for daily care at home.

Original languageEnglish
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages232-235
Number of pages4
ISBN (Electronic)9781728119908
DOIs
StatePublished - Jul 2020
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period20/07/2024/07/20

Fingerprint Dive into the research topics of 'High Accuracy Respiration and Heart Rate Detection Based on Artificial Neural Network Regression'. Together they form a unique fingerprint.

Cite this