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.
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BibTeX
@misc{farahani2026eventaligned,
title = {{Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology}},
author = {Saba A. Farahani and Elahe Khatibi and Thomas D. Hughes and Ariana M. Nelson and Hung Cao and Amir M. Rahmani},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2606.23705},
eprint = {2606.23705},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.23705},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology
AU - Saba A. Farahani
AU - Elahe Khatibi
AU - Thomas D. Hughes
AU - Ariana M. Nelson
AU - Hung Cao
AU - Amir M. Rahmani
PY - 2026
DA - 2026/08/31
PB - arXiv
DO - 10.48550/arxiv.2606.23705
UR - https://arxiv.org/abs/2606.23705
AB - 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.
ER -