基于Wi-Fi子载波互信息的人体呼吸感知系统

doi:10.11959/j.issn.1000-436x.2024009

摘要/Abstract

摘要：

不可预测环境变化使Wi-Fi信号存在较大的波动，很难对子载波中的静态分量和行为感知的动态分量进行量化，导致无法准确刻画出动态呼吸特征的波动形式。基于此，提出了一种基于子载波互信息的呼吸感知（SMIBP）系统。首先，提出了动态分量信息（DCI）的刻画形式，利用互信息理论提取子载波中代表呼吸的动态分量信息。然后，利用层次分析法组合各子载波以最大化呼吸信号的动态特征，得到重构的DCI感知基信号，最后联合小波变换和峰值检测法得到呼吸速率，揭示了代表人体呼吸动态分量的理论途径。仿真结果表明，所提系统能较好地刻画每个子载波中动态呼吸分量，且在不同场景下可显著提高Wi-Fi对于小尺度动作的感知精度与范围。

关键词: 动态分量信息, 信道状态信息, 无接触呼吸监测, 层次分析法

Abstract:

The unpredictable environmental changes cause large fluctuations in Wi-Fi signals, and it is difficult to quantify the static and behaviorally-aware dynamic components in the subcarriers, so it is not possible to accurately portray the fluctuating form of dynamic breathing characteristics.Based on this, a subcarrier mutual information breathing perception (SMIBP) system was proposed.Firstly, a form of inscribing dynamic component information (DCI) was proposed, and mutual information theory was utilized to extract the dynamic component information representing respiration in subcarriers.Then, analytic hierarchy process (AHP) was used to combine the subcarriers to maximize the dynamic characteristics of the respiratory signals to obtain the reconstructed DCI sensory base signals.Finally, the respiration rate was obtained by combining the wavelet transform and peak detection method, which revealed of a theoretical pathway representing the dynamic component of human respiration.Simulation results show that the proposed system can better characterize the dynamic breathing component in each subcarrier, and can significantly improve the sensing accuracy and range of Wi-Fi for small-scale actions in different scenarios.

Key words: dynamic component information, CSI, contactless respiratory monitoring, analytic hierarchy process

中图分类号:

TN911.6

刘影, 胡梦圆, 钱志鸿. 基于Wi-Fi子载波互信息的人体呼吸感知系统[J]. 通信学报, 2024, 45(2): 240-253.

Ying LIU, Mengyuan HU, Zhihong QIAN. Human breathing perception system based on Wi-Fi subcarrier mutual information[J]. Journal on Communications, 2024, 45(2): 240-253.

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方法	LOS（3 m）准确率	NLOS（3 m）准确率	朝向（0^o）准确率
单子载波^[18]	82%	82%	52%
有限子载波^[22]	80%	84%	65%
VMD^[36]	84%	83%	69%
SMIBP	86%	89%	75%

方法	LOS（3 m）准确率	NLOS（3 m）准确率	朝向（0^o）准确率
单子载波^[18]	69%	80%	44%
有限子载波^[22]	75%	79%	65%
VMD^[36]	84%	81%	68%
相位差^[37]	85%	89%	71%
SMIBP	91%	90%	77%