  Automatic pain assessment plays a critical role for advancing healthcare and
optimizing pain management strategies. This study has been submitted to the
First Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment
(AI4PAIN). The proposed multimodal framework utilizes facial videos and fNIRS
and presents a modality-agnostic approach, alleviating the need for
domain-specific models. Employing a dual ViT configuration and adopting
waveform representations for the fNIRS, as well as for the extracted embeddings
from the two modalities, demonstrate the efficacy of the proposed method,
achieving an accuracy of 46.76% in the multilevel pain assessment task.
