Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

In this paper, a semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition by using the channel state information (CSI) of the WiFi signal. We propose a dynamic associate domain adaptation (DADA), by modifying the existing associate domain adaptation algorithm, while the target domain can dynamically provide a different ratio of labelled data set/unlabelled data set. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects under the different environments. We designed an attention-based DenseNet model (AD) as our training network, so our proposed scheme is simplified as DADA-AD scheme. The experimental results illustrate that the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.