Device-Free Human Activity Recognition With Identity-Based Transfer Mechanism

Abstract

Device-free human activity recognition based on WiFi signals has become a very popular research field. However, it still has one major problem that is activities of “unseen” humans cannot be accurately classified, which makes it infeasible in real-world application. To tackle this issue, in this paper, we present a human activity recognition (HAR) system based on identity (ID) transfer mechanism named CrossID, which can cross the boundaries of identity by taking the high-level personal characteristics of the source domain and target domain as IDs for training and transferring. Specifically, we employ the margin-based loss function to improve the training speed and accuracy. To fully evaluate the feasibility of the proposed approach for human activity recognition, a variety of the data samples have been taken at 16 locations conducted by six people performing four different types of activities. Through extensive experiments on our dataset, we verify the effectiveness, robustness, and generalization ability of proposed system. Our average recognition rate in the target domain is 95%, which is slightly lower than 98% in the source domain.