Precise Heart Rate Measurement Using Non-contact Doppler Radar Assisted by Machine-Learning-Based Sleep Posture Estimation

Non-contact and continuous heart rate measurement using Doppler radar is important for various healthcare applications. In this paper, we propose a precise heart rate measurement method assisted by machine learning based sleep posture estimation. Machine learning is used for processing time-domain s...

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Bibliographic Details
Published inConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2019; pp. 788 - 791
Main Authors Higashi, Kotaro, Sun, Guanghao, Ishibashi, Koichiro
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
body movement
Doppler radar
Estimation
Face
Heart Rate
Heart rate measurement
Humans
Machine Learning
Microsoft Windows
Posture
Radar
Signal Processing, Computer-Assisted
sleep monitoring
vital sign
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Summary:Non-contact and continuous heart rate measurement using Doppler radar is important for various healthcare applications. In this paper, we propose a precise heart rate measurement method assisted by machine learning based sleep posture estimation. Machine learning is used for processing time-domain signal of the Doppler radar. Doppler radar has attracted much attention due to its non-contact to the subject feature. Moreover, it will not encroach into the privacy of the subject compared to image sensors. The method proposed in this paper automatically removes the data from the raw signal while the patient is moving or is not staying on the bed. This method based on machine learning uses simple features to reduce the computational cost thereby enabling real-time application. The sleeping posture was detected with an accuracy of 88.5%, and the error ratios of heart rate estimation were reduced by 15.2% in a sleep laboratory testing on 6 subjects.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8857830