Can We Obtain Fine-grained Heartbeat Waveform via Contact-free RF-sensing?
Contact-free vital-signs monitoring enabled by radio frequency (RF) sensing is gaining increasing attention, thanks to its non-intrusiveness, noise-resistance, and low cost. Whereas most of these systems only perform respiration monitoring or retrieve heart rate, few can recover fine-grained heartbe...
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| Vydané v: | Annual Joint Conference of the IEEE Computer and Communications Societies s. 1759 - 1768 |
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02.05.2022
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| Abstract | Contact-free vital-signs monitoring enabled by radio frequency (RF) sensing is gaining increasing attention, thanks to its non-intrusiveness, noise-resistance, and low cost. Whereas most of these systems only perform respiration monitoring or retrieve heart rate, few can recover fine-grained heartbeat waveform. The major reason is that, though both respiration and heartbeat cause detectable micro-motions on human bodies, the former is so strong that it overwhelms the latter. In this paper, we aim to answer the question in the paper title, by demystifying how heartbeat waveform can be extracted from RF-sensing signal. Applying several mainstream methods to recover heartbeat waveform from raw RF signal, our results reveal that these methods may not achieve what they have claimed, mainly because they assume linear signal mixing whereas the composition between respiration and heartbeat can be highly nonlinear. To overcome the difficulty of decomposing nonlinear signal mixing, we leverage the power of a novel deep generative model termed variational encoder-decoder (VED). Exploiting the universal approximation ability of deep neural networks and the generative potential of variational inference, VED demonstrates a promising capability in recovering fine-grained heartbeat waveform from RF-sensing signal; this is firmly validated by our experiments with 12 subjects and 48-hour data. |
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| AbstractList | Contact-free vital-signs monitoring enabled by radio frequency (RF) sensing is gaining increasing attention, thanks to its non-intrusiveness, noise-resistance, and low cost. Whereas most of these systems only perform respiration monitoring or retrieve heart rate, few can recover fine-grained heartbeat waveform. The major reason is that, though both respiration and heartbeat cause detectable micro-motions on human bodies, the former is so strong that it overwhelms the latter. In this paper, we aim to answer the question in the paper title, by demystifying how heartbeat waveform can be extracted from RF-sensing signal. Applying several mainstream methods to recover heartbeat waveform from raw RF signal, our results reveal that these methods may not achieve what they have claimed, mainly because they assume linear signal mixing whereas the composition between respiration and heartbeat can be highly nonlinear. To overcome the difficulty of decomposing nonlinear signal mixing, we leverage the power of a novel deep generative model termed variational encoder-decoder (VED). Exploiting the universal approximation ability of deep neural networks and the generative potential of variational inference, VED demonstrates a promising capability in recovering fine-grained heartbeat waveform from RF-sensing signal; this is firmly validated by our experiments with 12 subjects and 48-hour data. |
| Author | Luo, Jun Chen, Zhe Zheng, Tianyue Zhang, Shujie |
| Author_xml | – sequence: 1 givenname: Shujie surname: Zhang fullname: Zhang, Shujie email: shujie002@ntu.edu.sg organization: Nanyang Technological University,School of Computer Science and Engineering,Singapore – sequence: 2 givenname: Tianyue surname: Zheng fullname: Zheng, Tianyue email: tianyue002@ntu.edu.sg organization: Nanyang Technological University,School of Computer Science and Engineering,Singapore – sequence: 3 givenname: Zhe surname: Chen fullname: Chen, Zhe email: chenz@ssijri.com organization: China-Singapore International Joint Research Institute,Guangzhou,China – sequence: 4 givenname: Jun surname: Luo fullname: Luo, Jun email: junluo@ntu.edu.sg organization: Nanyang Technological University,School of Computer Science and Engineering,Singapore |
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| Snippet | Contact-free vital-signs monitoring enabled by radio frequency (RF) sensing is gaining increasing attention, thanks to its non-intrusiveness, noise-resistance,... |
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| SubjectTerms | contact-free sensing Costs Deep learning Heart beat Neural networks Radio frequency RF signals RF-sensing Spatial diversity variational encoder-decoder Vital signs monitoring |
| Title | Can We Obtain Fine-grained Heartbeat Waveform via Contact-free RF-sensing? |
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