Key result
Motion-robust video respiratory rate monitoring using adaptive filtering outperforms state-of-the-art methods on benchmark datasets.
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The proposed video-based respiratory rate measurement method with adaptive filtering improves accuracy over existing methods, potentially enhancing non-contact vital sign monitoring.
This article proposes a motion-robust video-based respiratory rate (RR) monitoring method. Specifically, the chest and abdomen regions of the human body are detected and tracked as the region of interest (ROI), which are further divided into patches. The dense inverse search (DIS) is then employed to calculate the optical flow within each patch. The phase signal of the optical flow is taken as the raw respiratory signal, which is denoised by a normalized least mean square (NLMS) adaptive filtering with the horizontal flow signal as the motion noise reference. Finally, a quality-guided RR pulse selection method in the time–frequency domain is designed to fuse the denoised RR pulses from all patches. The proposed approach is evaluated on the in-house BSIPL-RR dataset and the public COHFACE dataset. Experimental results indicate that this method outperforms the state-of-the-art (SOTA) comparison methods. This research effectively extends the scope of video-based RR measurements in real-world applications.
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Song et al. (2024) studied Respiratory rate monitoring. Video-based respiratory rate monitoring method vs. State-of-the-art methods was evaluated on Respiratory rate measurement performance. A motion-robust video-based respiratory rate monitoring method using adaptive filtering outperformed state-of-the-art comparison methods on the BSIPL-RR and COHFACE datasets.
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