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Accounting for Affect in Pain Level Recognition

2020-11-15 00:23:31
Md Taufeeq Uddin, Shaun Canavan, Ghada Zamzmi
arXiv_CV
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2011.07421

PDF

https://arxiv.org/pdf/2011.07421.pdf

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