  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.
