Deep Causal Domain Generalization Network for Human Action Recognition in Internet of Behaviors

The Internet of Behaviors (IoB) aims to leverage advanced sensing and computing technologies to understand human behaviors, and human action recognition is an essential task in IoB. However, recognizing human actions across varied and unseen domains remains a challenging problem, largely due to domain shifts that degrade the performance of conventional models. To address the aforementioned issue, a Deep Causal Domain Generalization Network (DCDGNet) is designed to enhance the generalizability of the human action recognition model. Specifically, a structured causal model for the data generation of human motion is constructed, where human motion signals are decoupled into action class-related causal factors and domain shift-related non-causal factors. DCDGNet integrates a human domain encoder, causal encoder, domain discriminator, motion reconstruction decoder, and action classifier, contributing to the model's ability to disentangle causal factors from non-causal ones from domain-specific features and achieve domain-invariant representations. Comprehensive experiments confirm that DCDGNet significantly outperforms existing state-of-the-art methods in terms of both accuracy and generalizability.