  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. Validated
across various deep learning architectures, our method demonstrates superior
performance and broad applicability. We propose a customizable framework that
aligns each modality with a suitable classifier based on statistical
significance, advancing personalized and effective multimodal fusion.
Furthermore, our methodology provides explainable analysis of multimodal data,
contributing to interpretable and explainable AI in healthcare. By highlighting
the importance of data diversity and modality-specific representations, we
enhance traditional fusion techniques and set new standards for recognizing
complex pain behaviors. Our findings have significant implications for
promoting patient-centered healthcare interventions and supporting explainable
clinical decision-making.
