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Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology
[Submitted on 11 Jun 2026] · 2026-06-24 · via cs.AI updates on arXiv.org

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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.

Submission history

From: Saba Azizabadi Farahani [view email]
[v1] Thu, 11 Jun 2026 05:05:59 UTC (2,299 KB)

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