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arXiv · 2607.19716

A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities

Stefanos Gkikas·Christian Arzate Cruz·Valentina Becchetti·Muhammad Umar Khan·Alessandro Giuseppi·Raul Fernandez Rojas
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Abstract

Pain is a complex and pervasive phenomenon affecting a large percentage of the population, and accurate assessment is essential for effective clinical management and intervention. Computational pain recognition systems enable continuous monitoring, support clinical decision-making, and help mitigate pain-related distress and functional decline. This study introduces a unified tokenization framework for heterogeneous 3D modalities in pain recognition that provides a single processing pipeline across behavioral and brain-activity 3D data, without requiring separate architectures for each modality or handcrafted inductive biases. The framework preserves spatial, temporal, and time--frequency structure while mapping diverse inputs into a shared token space. Extensive experiments show that the proposed approach effectively processes facial videos and fNIRS data in both raw-signal and spectrogram-based representations. On the AI4Pain benchmark dataset, the proposed framework achieves state-of-the-art performance while maintaining high computational efficiency and enabling real-time assessment on both GPU and CPU hardware.

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Stefanos Gkikas, Christian Arzate Cruz, Valentina Becchetti, Muhammad Umar Khan, Alessandro Giuseppi, Raul Fernandez Rojas. 2026-07-22. A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities. https://doi.org/10.1145/3776574.3831236

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