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arXiv · 2507.21881

Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image

Stefanos Gkikas·Ioannis Kyprakis·Manolis Tsiknakis
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Abstract

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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Stefanos Gkikas, Ioannis Kyprakis, Manolis Tsiknakis. 2025-07-29. Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image. https://arxiv.org/abs/2507.21881

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