  In this work, we address the importance of affect in automated pain
assessment and the implications in real-world settings. To achieve this, we
curate a new physiological dataset by merging the publicly available bioVid
pain and emotion datasets. We then investigate pain level recognition on this
dataset simulating participants' naturalistic affective behaviors. Our findings
demonstrate that acknowledging affect in pain assessment is essential. We
observe degradation in recognition performance when simulating the existence of
affect to validate pain assessment models that do not account for it.
Conversely, we observe a performance boost in recognition when we account for
affect.
