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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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An improved Dijkstras shortest path algorithm based on search strategy is proposed in this paper. In order to solve the defects of the conventional algorithm, such as large redundancy of space and time, the proposed algorithm introduces a constraint function with weighted valueωfor searching each position in the state space to guide the search forward to expected direction. Meanwhile, according to different complexity of map information, the weighted valueωcan be flexibly changed to make the constraint function more reasonable to effectively improve the search efficiency. Experimental result shows that the number of nodes on search path and computation time is obviously reduced, and the improved algorithm can be fast to search out the goal nodes.