论文标题
使用基于主要组件的小波CNN获得Wi-Fi CSI数据的人类活动识别
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN
论文作者
论文摘要
人类活动认可（HAR）是一项新兴技术，在监视，安全和医疗保健领域中有多种应用。可以开发基于Wi-Fi通道状态信息（CSI）信号的无创HAR系统利用无处不在的Wi-Fi技术的快速增长以及CSI动力学与身体运动之间的相关性。在本文中，我们提出了基于主要组件的小波卷积神经网络（或PCWCNN） - 一种新颖的方法，可为实时实时应用提供鲁棒性和效率。我们提出的方法结合了两种有效的预处理算法 - 主成分分析（PCA）和离散小波变换（DWT）。我们采用一种适应性和计算轻度的自适应活性分割算法。此外，我们使用小波CNN进行分类，这是一个类似于研究重新网络和densenet网络的深卷积网络。我们从经验上表明，我们提出的PCWCNN模型在真实的数据集上表现出色，表现优于现有方法。
Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.
