Device-free single-user activity recognition using diversified deep ensemble learning

WiFi-based human activity recognition (HAR) aims to recognize human activities in an off-the-shelf manner that only relies on the commercial Wi-Fi devices already installed in environments. The recent trend in HAR research is to train classifiers on top of statistical or deep neural features extracted from channel state information (CSI) data. Unfortunately, existing methods only take into account the temporal-correlation within each CSI subcarrier, while ignoring the spatial-correlation between different subcarriers. This issue has not been fully exploited yet, resulting a limited performance. To address this issue, we propose WiAReS, a WiFi-based device-free activity recognition system that takes both temporal-correlation and spatial-correlation into account. WiAReS embarks on diversified deep ensemble methods 2̌for single-user activity recognition where one user performs a single activity at a given time. More specifically, it adopts convolutional neural network (CNN) to automatically extract features from CSI measurements with the preservation of the locality of both spatial patterns and temporal patterns. To further improve recognition accuracy upon CNN-extracted features, we propose a novel ensemble architecture that fuses a multiple layer perception (MLP), a random forest (RF) and a support vector machine (SVM). Our system obtains the CSI data in PHY layer of off-the-shelf WiFi devices by installing Atheros-CSI-Tool on AR9590 based WiFi network interface cards (NICs). Comprehensive experiments have been conducted in three real environments with environmental variation to evaluate the performance of the proposed WiAReS. The experimental results demonstrate that the proposed WiARes system significantly outperforms existing methods.