An End-to-End Deep Learning Framework for Recognizing Human-to-Human Interactions Using Wi-Fi Signals

Abstract

Channel state information (CSI)-based human activity recognition plays an essential role in various application domains, such as security, healthcare, and Internet of Things. Most existing CSI-based activity recognition approaches rely on manually designed features that are classified using traditional classification methods. Furthermore, the use of deep learning methods for CSI-based activity recognition is still at its infancy with most of the existing approaches focus on recognizing single-human activities. The current study explores the feasibility of utilizing deep learning methods to recognize human-to-human interactions (HHIs) using CSI signals. Particularly, we introduce an end-to-end deep learning framework that comprises three phases, which are the input, feature extraction, and recognition phases. The input phase converts the raw CSI signals into CSI images that comprise time, frequency, and spatial information. In the feature extraction phase, a novel convolutional neural network (CNN) is designed to automatically extract deep features from the CSI images. Finally, the extracted features are fed to the recognition phase to identify the class of the HHI associated with each CSI image. The performance of our proposed framework is assessed using a publicly available CSI dataset that was acquired from 40 different pairs of subjects while performing 13 HHIs. Our proposed framework achieved an average recognition accuracy of 86.3% across all HHIs. Moreover, the experiments indicate that our proposed framework enabled significant improvements over the results achieved using three state-of-the-art pre-trained CNNs as well as the results obtained using four different conventional classifiers that employs traditional handcrafted features.