OCro: Open-Set Cross-Domain Human Activity Recognition Based on Radio Frequency

With the help of machine learning, models are trained to recognize human activity based on radio frequency signals, which are widely used in human-computer interaction, healthcare, etc. Cross-domain human activity recognition (HAR) aims to adapt a model trained in a specific source domain (including environment and user) for another target domain. Most existing cross-domain recognition methods are proposed under an ideal closed-set assumption, which means the training set and the testing set contain the same categories of human activities. However, when a model is applied in practice for HAR, it often encounters new categories of activities which are not contained in the training set. Under such open-set condition, the traditional closed-set cross-domain recognition model usually incorrectly identifies the new activity as a known activity, which decline the recognition accuracy. In this article, a model is proposed for open-set cross-domain HAR. The model is established based on generative adversarial network, and a unique generation module is designed to generate confusing samples whose features are similar to known classes. Thanks to such design, the proposed model can autonomously select appropriate unlabeled samples under the open-set condition to improve the open-set recognition ability of the model in the target domain. Extensive experiments are conducted based on four real data sets, one is collected by ourselves and the other three are public. The results show that the proposed model outperforms other state-of-the-art methods in the open-set and the cross-domain contexts.