Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication

YANG Xiaolong; ZHANG Tingting; ZHOU Mu; GAO Ming; TONG Ruixuan

doi:10.11999/JEIT240640

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YANG Xiaolong, ZHANG Tingting, ZHOU Mu, GAO Ming, TONG Ruixuan. Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240640

Citation:

YANG Xiaolong, ZHANG Tingting, ZHOU Mu, GAO Ming, TONG Ruixuan. Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240640

Citation:

YANG Xiaolong, ZHANG Tingting, ZHOU Mu, GAO Ming, TONG Ruixuan. Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240640

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Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication

doi: 10.11999/JEIT240640

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62101085), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400647), Chongqing Graduate Student Research Innovation Project (CYS240399)

Received Date: 2024-07-23
Rev Recd Date: 2024-12-12

Available Online: 2024-12-17

Abstract

Abstract

Integrated Sensing And Communication (ISAC) as a key technology for 6G integrates communication and sensing functions into Wi-Fi devices, providing an effective method for indoor human breath rate sensing. Addressing current challenges of low robustness and blind spots in ISAC-based breath rate sensing, a Variational Mode Decomposition (VMD)-Hilbert-Huang Transform (HHT) based algorithm for breath rate sensing is proposed in this paper. First, Wi-Fi links with high environmental sensitivity are selected to construct the Channel State Information (CSI) ratio model. Then, the subcarriers of the filtered CSI ratio time series are projected, and amplitude and phase information are combined to generate a candidate set of different breathing mode signals. Next, for each subcarrier, the candidate sequence with the highest short-term breath noise ratio is selected as the final breath pattern based on periodicity in the candidates. Then, a threshold is set to select subcarriers, followed by performing time-frequency analysis using VMD and HHT to remove modal components other than the human breath frequency, and reconstructing the remaining modal components. Subsequently, Principal Component Analysis (PCA) is employed to reduce the dimensionality of all reconstructed subcarriers, selecting principal components that contribute over 99% of the variance. The ReliefF algorithm is then used to reconstruct the breath signal as a fused signal. Finally, the breath rate is calculated using a peak detection algorithm on the fused signal. Experimental results show that the proposed detection method achieves the mean estimation accuracy of over 97% in both conference room and corridor scenarios, significantly enhancing robustness and overcoming the "blind spots" problem, outperforming other existing detection schemes.

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References(29)

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