Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data

ArXi:2604.23281v1 Announce Type: new Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of multi-source sensors to capture complex and dynamic human activities. However, multimodal human activity sensing typically encounters highly heterogeneous data across modalities and label scarcity, resulting in an application gap between existing solutions and real-world needs.