Advancing Pain Recognition through Statistical Correlation-Driven Multimodal Fusion

This research presents a novel multimodal data fusion methodology for pain behavior recognition, integrating statistical correlation analysis with human-centered insights. Our approach introduces two key innovations:

1. integrating data driven statistical relevance weights into the fusion strategy to effectively utilize complementary information from heterogeneous modalities, and
2. incorporating human-centric movement characteristics into multimodal representation learning for detailed modeling of pain behaviors.