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

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.

This record has no associated files available for download.

Contributors

Author: Xingyu Yang

Download statistics