用呼吸信号实现高效疼痛识别，模型更小却更准。
Efficient Pain Recognition via Respiration Signals: A Single Cross-Attention Transformer Multi-Window Fusion Pipeline
- 用呼吸信号+跨注意力机制，设计轻量级多窗口融合流程。
- 在真实数据上表现优异，小型模型性能超越大型模型。
- 适合临床持续监测，对资源有限场景友好。
疼痛是一种影响广泛人群的复杂状态，准确一致的评估对患者管理与治疗策略发展至关重要。自动疼痛评估系统可实现连续监测，辅助临床决策，减轻痛苦并预防功能退化。本研究参与第二届下一代疼痛评估多模态传感挑战赛（AI4PAIN）。提出一种以呼吸信号为输入的高效管道，结合紧凑的跨注意力变换器与多窗口策略。大量实验表明，呼吸信号是疼痛评估的重要生理指标；同时，经优化的轻量级模型能实现强大性能，常优于更大模型。所提多窗口策略有效捕捉短期、长期及全局特征，增强模型表征能力。
原文摘要 · Abstract (English)
Pain is a complex condition that affects a large portion of the population. Accurate and consistent evaluation is essential for individuals experiencing pain and supports the development of effective and advanced management strategies. Automatic pain assessment systems provide continuous monitoring, aid clinical decision-making, and aim to reduce distress while preventing functional decline. This study has been submitted to the Second Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN). The proposed method introduces a pipeline that employs respiration as the input signal and integrates a highly efficient cross-attention transformer with a multi-windowing strategy. Extensive experiments demonstrate that respiration serves as a valuable physiological modality for pain assessment. Furthermore, results show that compact and efficient models, when properly optimized, can deliver strong performance, often surpassing larger counterparts. The proposed multi-window strategy effectively captures short-term and long-term features, along with global characteristics, enhancing the model's representational capacity.
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