WiFi-Based Robust Human and Non-human Motion Recognition With Deep Learning

Abstract

As WiFi becomes increasingly pervasive in communications, its role in sensing applications is likewise expanding. However, current WiFi-based sensing technologies often operate under the limit assumption that all detected motion originates from human activities, there by neglecting influences from non-human subjects. Being able to differentiate human motions from non-human ones is essential in many application use cases. This paper presents a deep learning framework that can accurately recognize human and various non-human moving subjects using single-pair WiFi devices, even through the walls. Utilizing environment-invariant features, the framework is tested across three settings with commodity WiFi devices and various deep neural network architectures for four-class recognition. Achieving an average validation accuracy of 95.57% and an average testing accuracy of 87.09% in unseen environments with a challenging dataset, our approach demonstrates its robustness and readiness for integration into intelligent IoT systems and applications.