Efficient Residual Neural Network for Human Activity Recognition using WiFi CSI Signals

Abstract

The use of deep learning (DL) technology for the purpose of human activity recognition (HAR) is an important research area. Vision and sensor-based methods can provide good data but at the cost of privacy and convenience. Furthermore, Wi-Fi-based sensing has become popular for collecting human activity data, as it is ubiquitous, versatile, and performs well. The utilization of channel state information (CSI) obtained from Wi-Fi networks has the potential to facilitate the recognized activities. Traditional machine learning relies on hand-crafted features, but DL is more appropriate for automated feature extraction from raw CSI data. This work presented a generic HAR framework using CSI and studied various deep networks. We proposed a deep residual network that would automatically extract informative features from raw CSI. In this study, we conducted a comparative analysis of five fundamental deep networks; namely, convolutional neural network (CNN), long short-Term memory (LSTM), bidirectional LSTM, gated recurrent unit (GRU), and bidirectional GRU. Experiments on a publicly benchmark dataset named the CSI-HAR dataset showed that the proposed recognition model performed the best for CSI-based HAR with the highest accuracy of 98.60%, thus improving the accuracy by up to 3.60% over prior methods. Therefore, deep residual networks would be considered to be a suitable option for HAR tasks that would encompass Wi-Fi CSI data.