Domain-generalization human activity recognition model based on CSI instance normalization - WANG Yang; XU Jiawei; WANG Ao; XIA Huijuan; ZHAO Chuanxin; JI Yimu (Tongxin xuebao)
To achieve Wi-Fi cross-domain human activity perception that was not dependent on target domain data, a domain-generalization human activity recognition model based on CSI instance normalization called INDG-Fi was proposed. The instance normalization standardization was utilized to remove domain information from the representation of CSI features by INDG-Fi. Then action classifiers and domain classifiers were constructed for shared feature extraction. By employing activity bias learning and adversarial domain learning, the model biased the features extracted from the encoding layer towards signal variations caused by human actions while moving away from domain signals. To enhance the model’s focus on subcarrier signals that were more significantly influenced by human actions, a subcarrier attention module was incorporated into the encoding layer. The implemented results demonstrate that the proposed INDG-Fi achieves perceptual accuracies of 97.99% and 92.73% for unseen users and locations, respectively, thus enabling robust cross-domain perception.