将多种汗腺活动信号整合为一张图，提升疼痛自动识别准确率
Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image
- 把多类汗腺信号转成波形图，合成一张综合图像
- 在多个数据处理方法下表现稳定，部分优于传统融合方式
- 适合做生理信号融合的初学者和临床疼痛评估研究者
疼痛是影响大量人群的复杂现象。可靠一致的评估有助于缓解个体痛苦，并推动高效管理策略的发展。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。结合生理信号能提供客观精准的个体状态洞察。本研究参与了第二届下一代疼痛评估多模态传感挑战赛（AI4PAIN）。提出的方法以汗腺活动信号为输入，生成多种信号表示形式并可视化为波形图，再统一呈现于一张多表示图中。通过多种处理与滤波技术及不同表示组合的广泛实验，验证了该方法的有效性。其结果在多数情况下与传统融合方法相当，部分情形更优，展现出作为不同信号表示或模态融合稳健替代方案的潜力。
原文摘要 · Abstract (English)
Pain is a multifaceted phenomenon that affects a substantial portion of the population. Reliable and consistent evaluation supports individuals experiencing pain and enables the development of effective and advanced management strategies. Automatic pain-assessment systems provide continuous monitoring, guide clinical decision-making, and aim to reduce distress while preventing functional decline. Incorporating physiological signals allows these systems to deliver objective, accurate insights into an individual's condition. 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 electrodermal activity signals as the input modality. Multiple signal representations are generated and visualized as waveforms, which are then jointly presented within a unified multi-representation diagram. Extensive experiments using diverse processing and filtering techniques, along with various representation combinations, highlight the effectiveness of the approach. It consistently achieves comparable and, in several cases, superior results to traditional fusion methods, positioning it as a robust alternative for integrating different signal representations or modalities.
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