Joint Adversarial Domain Adaptation for Resilient WiFi-Enabled Device-Free Gesture Recognition

Abstract

Human gesture recognition plays a critical role in numerous applications of human-computer interaction. By analyzing how gesture alters the WiFi propagation among WiFi-enabled IoT devices to identify the gestures in a device-free manner could be a promising solution. However, existing methods require tedious data collection and labeling process each time being implemented in a new environment. The classifier constructed by SVM or random forest is vulnerable to spatial dynamics. In this paper, we proposed JADA, a novel unsupervised Joint adversarial domain adaptation (JADA) scheme that realizes accurate and resilient WiFi-enabled device-free gesture recognition without collecting and labeling training data in new environments. After constructing a source encoder and a source classifier in the source domain by convolutional neural network, JADA trains a target encoder and also fine-tunes the source encoder through adversarial learning to map both unlabeled target data and labeled source data to a domain-invariant feature space such that a domain discriminator cannot distinguish the domain labels of the data. After training a shared classifier with the labeled source data while fixing the parameters of the source encoder, we employ the trained target encoder to embed the test target samples into the domain-invariant feature space and infer its class using the shared classifier. We develop a novel Channel State Information (CSI) enabled IoT platform that could obtain fine-grained CSI time series data directly from IoT devices and transform them into CSI frames. Real-world experiments with COTS WiFi routers were conducted in 2 indoor environments. The experimental results demonstrate that JADA achieves 98.75% gesture recognition accuracy in the original environment. Moreover, when the environmental scenario is altered, it is able to reduce the domain discrepancy across domains without collecting any labeled data in the new context.