统一处理面部与脑活动数据，实现疼痛实时识别
A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities
- 将不同3D模态数据映射到共享令牌空间，无需为每种数据设计独立架构
- 在AI4Pain数据集上达到当前最优性能，支持GPU和CPU实时运行
- 适用于临床疼痛监测，尤其适合需连续评估的场景
疼痛是影响大量人群的复杂现象，准确评估对临床管理至关重要。计算疼痛识别系统可实现持续监测，辅助临床决策，缓解疼痛相关痛苦与功能退化。本研究提出一种统一的异构3D模态疼痛识别分词框架，可在行为与脑活动3D数据间建立单一处理流程，无需为每种模态单独设计架构或手工设定归纳偏置。该框架保留了空间、时间及时频结构，并将多样输入映射至共享令牌空间。大量实验表明，该方法能有效处理原始信号与谱图表示的面部视频和fNIRS数据。在AI4Pain基准数据集上，所提方法达到当前最优性能，同时具备高计算效率，可在GPU和CPU上实现实时评估。
原文摘要 · Abstract (English)
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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