Cross-domain neural alignment (CDNA): a deep supervised domain adaptation in human activity recognition using device-free sensing

Wi-Fi sensor networks have grown rapidly due to their scalability and high data throughput, finding applications in tasks like tracking human motion in laboratory settings. These systems detect motion by analyzing fluctuations in radio signals caused by target movements, which generate identifiable activity patterns. However, their performance is influenced by factors such as environmental changes, unseen target subjects, multi-target tracking, data configurations, and the nature of target activities. These challenges lead to domain shifts between the training and testing phases, a common issue in real-world scenarios known as the domain-shifting problem in transfer learning. We propose a supervised domain alignment technique to address domain shifts in Wi-Fi sensor Channel State Information (CSI) datasets using minimal labeled target data. Our method outperforms state-of-the-art adversarial models trained on similar data, achieving superior cross-domain prediction accuracy. Evaluations on two public CSI datasets show consistent improvements, with an average Micro-F1 score of 90% for cross-user tasks and 67% for cross-user and cross-environment tasks using only 70 labeled target samples. This approach demonstrates its effectiveness in enhancing prediction accuracy under challenging domain shift scenarios.