Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring

Yang, Xingyu, Zhang, Zijian ORCID: 0000-0002-3004-6697, Huang, Yi ORCID: 0000-0001-7774-1024, Zheng, Yalin ORCID: 0000-0002-7873-0922 and Shen, Yaochun ORCID: 0000-0002-8915-1993 (2022) Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring. Scientific Reports, 12 (1). 15197-.

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Official URL: http://dx.doi.org/10.1038/s41598-022-19198-1

Abstract

<jats:title>Abstract</jats:title><jats:p>Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still unmet needs in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time–frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under pre- and post-exercise conditions and were repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements were observed in terms of precision, accuracy, and stability. The performance was reflected by achieving an averaged Pearson correlation coefficient (PCC) of 93.8% on multiple subjects. We believe that the proposed estimation method will help address the needs for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Humans, Algorithms, Radar, Vital Signs, Respiratory Rate, COVID-19
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	08 Sep 2022 10:18
Last Modified:	18 Jan 2023 20:45
DOI:	10.1038/s41598-022-19198-1
Open Access URL:	https://doi.org/10.1038/s41598-022-19198-1
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3163866