WiFi CSI-Based Human Activity Recognition Using Deep Recurrent Neural Network

Abstract

Human activity recognition based on channel state information (CSI) using commercial WiFi devices plays an increasingly important role in many applications, such as smart home and interactive games. In this paper, we propose a WiFi CSI based human activity recognition approach using deep recurrent neural network (HARNN). HARNN mainly exploits four key techniques to recognize different human activities. HARNN firstly constructs a novel two-level decision tree for using two environment variation statistics efficiently. Meanwhile, a linear regression method is also introduced to seek for the optimal parameter for the designed decision tree. Depending on this, the decision tree is used to sense indoor environment variation, and then detect whether there is any human activity occurring in a target area. In addition, a noise removal mechanism is devised to eliminate the influence of random noise derived from indoor environments. Then, to characterize various human activities, two representative features are extracted from different statistical profiles, including channel power variation (CPV) and time-frequency analysis (TFA). Finally, a recurrent neural network (RNN) model is utilized to recognize different human activities by leveraging the extracted representative features above. According to the above steps, the proposed HARNN could establish a robust relationship between human activities and WiFi CSI compared with most of the existing WiFi CSI based approaches. The proof-of-concept prototype of HARNN is implemented on a set of commercial WiFi devices, and its overall performance is evaluated in several typical indoor environments. The experimental results demonstrate that HARNN can achieve better recognition performance compared with some benchmark approaches.