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arXiv 2011.074212020-11-15cs.CV

Accounting for Affect in Pain Level Recognition

University of South Florida（南佛罗里达大学）；National Institutes of Health（美国国立卫生研究院）

Md Taufeeq Uddin, Shaun Canavan, Ghada Zamzmi

备注：: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract

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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.

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