Automated pain recognition is a fundamental challenge for the advancement of clinical care and empathetic human-computer interaction, due to the subjective and dynamic nature of pain. In this study, we propose a deep learning framework for objective pain assessment based on multimodal physiological signals from the AI4Pain dataset. The approach focuses on Blood Volume Pulse (BVP) and Respiration (RESP), capturing complementary aspects of autonomic responses to nociceptive stimulation. To explicitly model their temporal dynamics, the signals are transformed into structured trajectory representations using Hankel matrix embeddings, enabling the learning of dynamical patterns beyond conventional time-series representations. These embeddings are processed by modality-specific Fully Convolutional Networks, and their outputs are integrated through a late fusion strategy for three-class classification (No Pain, Low Pain, High Pain). Experimental results demonstrate that the proposed method achieves an accuracy of 67.09% and an F1-score of 66.41%, providing competitive performance compared to existing approaches, while relying on no handcrafted feature engineering. Multimodal fusion significantly outperformed either modality alone, and the Hankel embedding showed a trend towards further improvement. Further validation is required to establish generalisability to broader populations and clinical settings.

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