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

2026-06-11

arXiv.org

AI Summary

研究通过引入评估者感知的事件对齐框架，解决多评估者疼痛评估不一致问题，利用连续可穿戴生理信号与特定评估者的疼痛变化事件对齐，揭示了评估者依赖的生理差异。

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.

Problem

• Pain Assessment
• Rater Disagreement
• Wearable Physiology
• Event-Aligned Analysis
• Rater-Aware Framework

Innovation

• rater-aware
• event-aligned
• wearable physiology
• pain assessment
• multimodal data