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

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 d...

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Vydané v:Scientific reports Ročník 12; číslo 1; s. 15197 - 12
Hlavní autori: Yang, Xingyu, Zhang, Zijian, Huang, Yi, Zheng, Yalin, Shen, Yaochun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 07.09.2022
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Algorithms
Correlation coefficient
COVID-19
COVID-19 - diagnostic imaging
Fourier transforms
Heart rate
Humanities and Social Sciences
Humans
Image processing
multidisciplinary
Radar
Respiration
Respiratory Rate - physiology
Science
Science (multidisciplinary)
Segmentation
Signal processing
Vital Signs
ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-19198-1