Reconstruction of Human Physiological Signal in Partition Wall Based on UWB Signal Time-Frequency Graph

Through-wall physiological parameter monitoring has significant application potential in non-intrusive health surveillance for individuals living alone and in post-disaster rescue operations. To address challenges such as multipath effects and clutter interference, this paper proposes a method for through-wall physiological signal reconstruction based on ultra-wideband signal time-frequency spectrogram. The method effectively mitigates multipath effects and improves the signal-to-noise ratio using Singular Value Decom-position (SVD) and the Least Squares method. It further employs Ensemble Empirical Mode Decomposition (EEMD) and time-frequency spectrogram for signal decomposition and reconstruction, accurately estimating both respiration and heart rates. Ablation experiments demonstrate that this method enhances monitoring accuracy stability and improves signal reliability.

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Publication Details

Published
2024-12-12
DOI
https://doi.org/10.1109/icarcv63323.2024.10821690
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
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Reconstruction of Human Physiological Signal in Partition Wall Based on UWB Signal Time-Frequency Graph

Wendong Xiao, Chenglin Li, Qingyun Chi, Xingcan Chen et al.
Non-Invasive Vital Sign Monitoring
article

Reconstruction of Human Physiological Signal in Partition Wall Based on UWB Signal Time-Frequency Graph

Wendong Xiao, Chenglin Li, Qingyun Chi, Xingcan Chen, Yi Zou, Fenghu Jin
article en

Abstract

Through-wall physiological parameter monitoring has significant application potential in non-intrusive health surveillance for individuals living alone and in post-disaster rescue operations. To address challenges such as multipath effects and clutter interference, this paper proposes a method for through-wall physiological signal reconstruction based on ultra-wideband signal time-frequency spectrogram. The method effectively mitigates multipath effects and improves the signal-to-noise ratio using Singular Value Decom-position (SVD) and the Least Squares method. It further employs Ensemble Empirical Mode Decomposition (EEMD) and time-frequency spectrogram for signal decomposition and reconstruction, accurately estimating both respiration and heart rates. Ablation experiments demonstrate that this method enhances monitoring accuracy stability and improves signal reliability.

China South Industries Group (China) (CN), Zaozhuang University (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 77%
Non-Invasive Vital Sign Monitoring
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