Cross-Scenario Device-Free Gesture Recognition Based on Self-Adaptive Adversarial Learning

Device-free gesture recognition (DFGR) is an emerging technique which could leverage the influence of human gestures on surrounding wireless signals to recognize gestures. It has gained widespread attention due to its promising prospect of empowering pervasive wireless devices with the sensing ability. Due to the inconsistency of the feature distribution in different scenarios, a well-trained DFGR system often fails to get satisfactory performance in cross-scenario conditions. Researchers have done valuable exploration on alleviating the feature distribution shift from a global distribution point of view. However, global feature distribution alignment could not solve the feature distribution shift problem completely. In this article, we develop a self-adaptive adversarial learning network which could further reduce the feature distribution shift through aligning the local feature distribution. Specifically, we design an adversarial network which is consisted of a feature extractor, a scenario discriminator, and two diverse classifiers. It could evaluate the degree of local feature distribution alignment by analyzing the prediction inconsistent of the classifiers. We design a self-adaptive adversarial loss which can be adjusted adaptively according to the degree of local alignment. If the features have been aligned locally, we reduce their impact on the loss to protect these aligned features. Otherwise, we increase their influence to accelerate the training process. The extensive experiments conducted on a designed mmWave testbed demonstrate that the proposed method could achieve an accuracy of at least 4% higher than those of existing cross-scenario DFGR methods, while the number of training iterations can be reduced by nearly half.