Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring
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 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.
Yang, Xingyu
8a9e405f-212a-42c8-888f-fd6c609774f6
Zhang, Zijian
8991f20c-24e9-4285-a7a6-b7c0b2507b0a
Huang, Yi
474e2d27-912e-46e6-b0e6-57641e7dbf93
Zheng, Yalin
cf121a1d-9a54-4d7c-aeea-6354fe19d880
Shen, Yaochun
0dd7fc84-23be-4931-b718-057db1286a39
7 September 2022
Yang, Xingyu
8a9e405f-212a-42c8-888f-fd6c609774f6
Zhang, Zijian
8991f20c-24e9-4285-a7a6-b7c0b2507b0a
Huang, Yi
474e2d27-912e-46e6-b0e6-57641e7dbf93
Zheng, Yalin
cf121a1d-9a54-4d7c-aeea-6354fe19d880
Shen, Yaochun
0dd7fc84-23be-4931-b718-057db1286a39
Yang, Xingyu, Zhang, Zijian, Huang, Yi, Zheng, Yalin and Shen, Yaochun
(2022)
Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring.
Scientific Reports, 12, [15197 (2022)].
(doi:10.1038/s41598-022-19198-1).
Abstract
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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Published date: 7 September 2022
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Local EPrints ID: 499099
URI: http://eprints.soton.ac.uk/id/eprint/499099
ISSN: 2045-2322
PURE UUID: e00d88e8-1431-450b-89f4-5a8b7f134a5a
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Date deposited: 07 Mar 2025 17:51
Last modified: 08 Mar 2025 03:17
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Author:
Xingyu Yang
Author:
Zijian Zhang
Author:
Yi Huang
Author:
Yalin Zheng
Author:
Yaochun Shen
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