Spectral Unmixing Successive Variational Mode Decomposition for Robust Vital Signs Detection Using UWB Radar

Qiao, Lihong; Wang, Z.; Xiao, Bin; Shu, Yucheng; Luan, Xiao; Shi, Yuhang; Li, Weisheng; Gao, Xinbo

doi:10.1109/tim.2023.3274171

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…Simulation results: To verify the effectiveness of ST-SVME in separating respiration and heartbeat signals with close frequencies, a typical chest movement signal simulation is presented here. Normally, the RR and HR of a healthy adult are in the range of 0.2-0.6 and 0.8-2.0 Hz, respectively [9]. Due to the activity of respiration and heartbeat, the human chest surface exhibits slight periodic displacement.…”

Section: (A) Simulation Of Chest Movement Signal (Snr=8 Db) (B) the V...mentioning

confidence: 99%

“…For example, empirical mode decomposition (EMD) and ensemble EMD (EEMD) are used to decompose the chest movement signal into a series of intrinsic mode functions (IMFs) that include respiration and heartbeat signals [6][7][8]. However, EMD and EEMD lack mathematical proof and suffer from mode mixing issues [9]. To solve these problems, a variational mode decomposition (VMD) algorithm based on Wiener filter was proposed, which was more robust to noise [10].…”

Section: Introductionmentioning

confidence: 99%

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A successive variational mode extraction method based on spectrum trend and its application in non‐contact vital sign detection

Zhao,

Sheng,

et al. 2024

Electronics Letters

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Detecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies.

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Section: (A) Simulation Of Chest Movement Signal (Snr=8 Db) (B) the V...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A successive variational mode extraction method based on spectrum trend and its application in non‐contact vital sign detection

Zhao,

Sheng,

et al. 2024

Electronics Letters

View full text Add to dashboard Cite

show abstract

“…(1) Applying peak detection or data clustering to the processing results of signal energy [20,21], standard deviation [22,23], variance [24], peak-to-peak value [24], correlation [25,26], and even entropy [27] from the obtained data matrix;…”

Section: Introductionmentioning

confidence: 99%

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Liang,

Jin,

Yang

et al. 2024

Remote Sensing

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Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.

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A robust multi-target localization algorithm for ISAC systems

Guo

2024

Physical Communication

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Spectral Unmixing Successive Variational Mode Decomposition for Robust Vital Signs Detection Using UWB Radar

Cited by 5 publications

References 46 publications

A successive variational mode extraction method based on spectrum trend and its application in non‐contact vital sign detection

A successive variational mode extraction method based on spectrum trend and its application in non‐contact vital sign detection

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

A robust multi-target localization algorithm for ISAC systems

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