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: 2024-12-11

Available Online: 2024-12-17

Abstract

Abstract

Objective Breathing rate is a vital physiological indicator of human health. Abnormal changes in this rate can signify diseases like chronic obstructive pulmonary disease, sleep apnea syndrome, and nocturnal hypoventilation syndrome. Timely and accurate detection of these changes can help identify health risks early, enable professional medical intervention, and optimize treatment timing, thereby improving overall health. However, current detection methods often face limitations due to noise interference and “blind spot” issues, which impact accuracy and robustness. To address these challenges, this paper employs Wi-Fi devices to measure indoor human breathing rates using Integrated Sensing And Communication (ISAC) technology. By combining Variational Modal Decomposition (VMD) and Hilbert-Huang Transform (HHT), a new breathing rate sensing algorithm is proposed. This approach aims to enhance detection accuracy and robustness, resolve the “blind spot” problem in existing technologies, and offer an efficient and reliable solution for health monitoring. Methods Wi-Fi links with high environmental sensitivity were selected to construct the Channel State Information (CSI) ratio model. Subcarriers of the filtered CSI ratio time series were projected, and amplitude and phase information were combined to generate a candidate set of breathing mode signals. For each subcarrier, the sequence with the highest short-term breath noise ratio, determined by periodicity, was identified as the final breath pattern. A threshold was then applied to select relevant subcarriers. Time-frequency analysis using VMD and HHT eliminated modal components unrelated to the human breath rate, and the remaining components were reconstructed. Principal Component Analysis (PCA) was applied for dimensionality reduction, selecting components accounting for over 99% of the variance. The ReliefF algorithm was subsequently used to reconstruct the breath signal into a fused signal, from which the breathing rate was calculated using a peak detection algorithm. Results and Discussions Experiments were conducted in two scenarios: a conference office and a corridor. In both setups, a pair of transceivers was deployed, with a 2-meter distance maintained between the transmitter and receiver. The transmitter used one omnidirectional antenna, and the receiver had three antennas positioned perpendicular to the ground. Participants were seated on the vertical bisector of the Line Of Sight (LOS) path, synchronizing their breathing with a metronome as CSI data were recorded. Each test lasted 1 minute, with a confirmed breathing rate of 16 bpm. System parameters used in the experiments are detailed in Table 1. In the conference office scenario, this paper collected data at various distances from the participant to the transceiver. As illustrated in Figure 9, the Mean Estimation Accuracy (MEA) of our algorithm remains above 97%, even when the participant is 5 meters away. In contrast, the MEA of the other two methods drops by 4% and 5%, respectively. As the sensing distance increases, the multipath effect intensifies, leading to a gradual weakening of the reflected signal and greater noise interference. This impact significantly challenges the breathing detection accuracy of the other methods. The algorithm presented in this paper incorporates a VMD-HHT time-frequency analysis step. This enhancement allows for effective signal decomposition and feature extraction, markedly improving the accuracy of detecting the target breathing signal. Moreover, the method exhibits strong adaptability and robustness, effectively addressing noise interference and multipath effects in complex environments, thus demonstrating more stable performance. In the corridor scenario, we evaluated the algorithm's performance at varying distances. The average absolute error of the algorithm was measured with distances ranging from 2 meters to 5 meters. At 2 meters, the Mean Absolute Error (MAE) recorded was 0.37 bpm, and even at 5 meters, the MAE only increased to 0.45 bpm, remaining below 0.5 bpm. As the distance between the target and transceiver increased from 3 to 5 meters, the MAE gradually rose. This trend is attributed to the further attenuation of the signal reflected from the human target, along with the escalating multipath and signal attenuation effects in the environment. Conclusions The experimental results indicate that the MEA of this sensing method exceeds 97% in both the conference office and corridor scenarios. This effectively addresses the "blind spot" issue present in current technologies. The enhanced accuracy and robustness of the algorithm outperform existing sensing schemes. Moreover, this method broadens the application of ISAC in breathing detection and opens new avenues for developing intelligent health management systems in the future.

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References(29)

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