WiWalk: Gait-Based Dual-User Identification Using WiFi Device

Abstract

The rapid development of the Internet of Things (IoT) boosts the spread of intelligent spaces. Biometrics-based user identification has gained great popularity recently, among which gait analysis offers a stable, user-friendly, and economical solution. Thanks to the advancement in wireless sensing technologies, capturing gait characteristics using WiFi signals has become a promising new paradigm. The identification process is contactless, insensitive to lighting conditions, and can reuse the incumbent WiFi infrastructure. In this article, we present a gait-based dual-user identification framework named WiWalk to tackle the difficulty where users walk closely together with mixed effects on WiFi signals. The core of WiWalk is to train a deep neural network that can separate and recover individual signals from the mixed ones. Since the separation process inevitably causes information loss, we carefully design a series of algorithms for interference elimination, segmentation, and feature extraction, to enhance the identification accuracy. We conduct extensive experiments to evaluate WiWalk at different locations and times with users of different ages, genders, clothing, and walking behaviors. WiWalk can reach an accuracy of 94.44%, which is suitable for smart homes or offices with a small user base.