A Dense Margin Network for Human Activity Recognition Based on Augmented Channel State Information

Human activity recognition based on channel state information (CSI) has received widespread attention in recent years due to its low cost and privacy protection. However, its accuracy can be significantly reduced when most of the current recognition approaches are applied to new users who have not participated in model training. To address this issue, a new passive human activity recognition method based on CSI is proposed in this work. A cycle-consistent generative adversarial network (CycleGAN) is used to map old user CSI activity data to new user CSI activity data. Multiple dense blocks from a dense convolutional network (DenseNet) and a large margin cosine (margin) module are then combined to automatically extract features with representative differences in different activities to identify multiple human activities. According to the results of the model on SignFi data and self-collected data on the eight daily activities of ten volunteers, the activity recognition accuracy of new users is found to reach 95.63% and 96.67%, respectively. The experimental results demonstrate that the proposed method can achieve better performance than the benchmark approaches.