Device-Free Human Gesture Recognition With Generative Adversarial Networks

Recent advances in device-free wireless sensing have created the emerging technique of device-free human gesture recognition (DFHGR), which could recognize human gestures by analyzing their shadowing effect on surrounding wireless signals. DFHGR has many potential applications in the fields of human–machine interaction, smart home, intelligent space, etc. State-of-the-art work has achieved satisfactory recognition accuracy when there are a sufficient number of training samples. However, it is time consuming and labor intensive to collect samples, thus how to realize DFHGR under a small training sample set becomes an urgent problem to solve. Motivated by the excellent ability of the generative adversarial network in synthesizing samples, in this article, we explore and exploit the idea of leveraging it to realize virtual samples augmentation. Specifically, we first design a single scenario network with new architecture and better-designed loss function to generate virtual samples using a few number of real samples. Then, we further develop a scenario transferring network to generate virtual samples by utilizing the real samples not only from the current scenario but also from another available scenario as well, which could improve the quality of synthesized samples with the extra knowledge learned from another scenario. We design an mmWave-based DFHGR testbed to test the proposed networks, extensive experimental results demonstrate that the augmented virtual samples are of high quality and facilitate DFHGR systems to achieve better accuracy.