arXiv:2608.14662v1 Announce Type: cross
Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability. In this study, we investigate self-supervised representation learning (SSL) methods applied to unimodal electrocardiogram (ECG), complemented by multimodal pretraining, including accelerometer (ACC) signals from the chest. We focus on classifying low versus medium pain levels on the X-ITE Pain dataset. Our results reveal that while ECG-based models show limited classification performance, multimodal pretraining improves learned representations by capturing cross-modal dependencies. Notably, we observe substantial inter-subject variability in model performance, suggesting that pain-related ECG patterns may be subject-specific. Visualizations indicate distinct subject-specific clustering but no clear separation by pain levels, highlighting the complexity of pain detection from ECG alone. We discuss limitations of unimodal input, label noise, and generalization across subjects and propose future directions. This work advances the understanding of physiological signal representation learning for pain recognition and sets the stage for more robust, clinically relevant wearable pain monitoring solutions.
Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability. In this study, we investigate self-supervised representation learning (SSL) methods applied to unimodal electrocardiogram (ECG), complemented by multimodal pretraining, including accelerometer (ACC) signals from the chest. We focus on classifying low versus medium pain levels on the X-ITE Pain dataset. Our results reveal that while ECG-based models show limited classification performance, multimodal pretraining improves learned representations by capturing cross-modal dependencies. Notably, we observe substantial inter-subject variability in model performance, suggesting that pain-related ECG patterns may be subject-specific. Visualizations indicate distinct subject-specific clustering but no clear separation by pain levels, highlighting the complexity of pain detection from ECG alone. We discuss limitations of unimodal input, label noise, and generalization across subjects and propose future directions. This work advances the understanding of physiological signal representation learning for pain recognition and sets the stage for more robust, clinically relevant wearable pain monitoring solutions.
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