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

Xingyu Yang1, Zijian Zhang1, Yi Huang1

  • 1Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK.

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Summary

This study introduces a novel video and mm-wave radar analysis for contactless vital sign monitoring. The image segmentation method enhances accuracy and robustness for heart rate and respiration rate extraction, even during physical activity.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Imaging

Background:

  • Contactless vital sign monitoring (respiration, heart rate) is crucial for clinical and home settings but current methods struggle with motion and lighting.
  • Existing signal processing techniques for vital sign extraction from mm-wave radar and video are susceptible to environmental disruptions.
  • The need for reliable remote cardiovascular sensing is heightened by public health concerns like COVID-19.

Purpose of the Study:

  • To develop and validate an image segmentation-based method for extracting vital signs from video and mm-wave radar signals.
  • To improve the robustness and accuracy of heart rate and respiration rate measurements compared to existing techniques.
  • To provide a reliable solution for remote cardiovascular sensing and diagnosis.

Main Methods:

  • Utilized an image segmentation approach to analyze combined video and mm-wave radar data.
  • Employed time-frequency spectrograms derived from Short-Time Fourier Transform (STFT) for signal analysis, moving beyond traditional time-domain methods.
  • Conducted experiments on multiple individuals under varying conditions (pre- and post-exercise) and validated against gold standard contact-based measurements.

Main Results:

  • The proposed method demonstrated significantly improved precision, accuracy, and stability in vital sign extraction.
  • Achieved an average Pearson correlation coefficient (PCC) of 93.8% across multiple subjects, indicating high agreement with gold standard measurements.
  • Showcased enhanced robustness against body motion disruptions and illumination variations compared to prior methods.

Conclusions:

  • The image segmentation-based analysis of time-frequency spectrograms offers a robust and accurate approach for contactless vital sign monitoring.
  • This method effectively extracts heart rate and respiration rate, addressing limitations of current technologies.
  • The developed technique holds significant potential for advancing remote cardiovascular sensing and diagnostic applications.