The paper addresses the challenge of extracting vital sign information, such as respiration rate and heart rate, from Doppler radar signals in the presence of interfering body motions. A key problem is that the vital sign contributions and the interference motions can have overlapping Doppler bands and significant differences in power levels, making them difficult to separate using traditional signal processing techniques.
The proposed solution utilizes a variational convolutional neural network (CNN) as an interference removal system. The network is trained on a semi-experimental dataset, where the vital sign STFT (Short-Time Fourier Transform) signals are derived from real recordings and the interference motion is synthesized according to a widely applied model.
The results demonstrate that the network is capable of generalizing beyond the training dataset and can produce outputs with low reconstruction loss for unseen samples. Furthermore, the network's output is shown to enable more accurate extraction of the respiration rate compared to the original mixed signal, even in the presence of high levels of interference and additive noise.
The key highlights of the proposed approach are:
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by Mikolaj Czer... at arxiv.org 04-15-2024
https://arxiv.org/pdf/2404.08298.pdfDeeper Inquiries