Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology

Abstract:Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the rating. We introduce a rater-aware, event-aligned framework that converts sparse, rater-specific pain ratings into discrete pain-change events and aligns continuous wearable physiological signals to these events, preserving rater identity throughout. Applied to multimodal wearable data collected during spine-related pain procedures, the framework identifies substantial disagreement across rater groups and provides preliminary, exploratory evidence of rater-dependent physiological differences preceding reported pain increases. These findings suggest that pain-physiology relationships may not be rater-invariant, and that aggregating assessments across raters may mask meaningful physiological patterns. A rater-aware, event-aligned perspective is therefore a promising direction for interpreting wearable data in real-world clinical pain assessment.

6 pages, 3 figures. Accepted at IEEE EMBC 2026 (Toronto, Canada, July 26-30, 2026)

Applications (stat.AP); Artificial Intelligence (cs.AI)

arXiv:2606.23705 [stat.AP]

(or arXiv:2606.23705v2 [stat.AP] for this version)

https://doi.org/10.48550/arXiv.2606.23705 Focus to learn more arXiv-issued DOI via DataCite

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