Human Activity Recognition from Wi-Fi CSI data using Principal Component-based Wavelet CNN

Abstract

Human Activity Recognition (HAR) is an emerging technology with applications in the surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the rapid 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 for HAR 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 Wavelet CNN – a deep convolutional network analogous to the well-studied ResNet and DenseNet networks, for activity classification. We evaluate the performance of our proposed PCWCNN algorithm on both line-of-sight data and non-line-of-sight data. We empirically show that our proposed PCWCNN algorithm performs very well on these real datasets, outperforming relevant state-of-the-art approaches.