Human Behaviour Recognition Using Wifi Channel State Information

Abstract

Device-Free Human Behaviour Recognition is automatically recognizing physical activity from a series of observations, without directly attaching sensors to the subject. Behaviour Recognition has applications in security, health-care, and smart homes. The ubiquity of WiFi devices has generated recent interest in Channel State Information (CSI) that describes the propagation of RF signals for behaviour recognition, leveraging the relationship between body movement and variations in CSI streams. Existing work on CSI based behaviour recognition has established the efficacy of deep neural network classifiers, yielding performance that surpasses traditional techniques. In this paper, we propose a deep Recurrent Neural Network (RNN) model for CSI based Behaviour Recognition that utilizes a Convolutional Neural Network (CNN) feature extractor with stacked Long Short-Term Memory (LSTM) networks for sequence classification. We also examine CSI de-noising techniques that allow faster training and model convergence. Our model has yielded significant improvement in classification accuracy, compared to existing techniques.