Robust WiFi-Enabled Device-Free Gesture Recognition via Unsupervised Adversarial Domain Adaptation

Abstract

Accurate human gesture recognition is becoming a cornerstone for myriad emerging applications in human-computer interaction. Existing gesture recognition systems either require dedicated extra infrastructure or user's active cooperation. Although some WiFi-enabled gesture recognition systems have been proposed, they are vulnerable to environmental dynamics and rely on the tedious data re-labeling and expert knowledge each time being implemented in a new environment. In this paper, we propose a WiFi- enabled device-free adaptive gesture recognition scheme, WiADG, that is able to identify human gestures accurately and consistently under environmental dynamics via adversarial domain adaptation. Firstly, a novel OpenWrt-based IoT platform is developed, enabling the direct collection of Channel State Information (CSI) measurements from commercial IoT devices. After constructing an accurate source classifier with labeled source CSI data via the proposed convolutional neural network in the source domain (original environment), we design an unsupervised domain adaptation scheme to reduce the domain discrepancy between the source and the target domain (new environment) and thus improve the generalization performance of the source classifier. The domain- adversarial objective is to train a generator (target encoder) to map the unlabeled target data to a domain invariant latent feature space so that a domain discriminator cannot distinguish the domain labels of the data. In the phase of implementation, we utilize the trained target encoder to map the target CSI frame to the latent feature space and use the source classifier to identify various gestures performed by the user. We implement WiADG on commercial WiFi routers and conduct experiments in multiple indoor environments. The results validate that WiADG achieves 98% gesture recognition accuracy in the original environment. Furthermore, the proposed unsupervised adversarial domain adaptation is able to enhance the recognition accuracy of WiADG by 25% on average without the needs of labeled data collection and new classifier generation when implements it in new environments.