论文标题
在疼痛水平识别中考虑影响
Accounting for Affect in Pain Level Recognition
论文作者
论文摘要
在这项工作中，我们解决了情感在自动疼痛评估中的重要性以及现实环境中的含义。为了实现这一目标，我们通过合并公开可用的Biovid疼痛和情感数据集来策划新的生理数据集。然后，我们研究该数据集上的疼痛水平识别，以模拟参与者的自然情感行为。我们的发现表明，在疼痛评估中承认情感至关重要。当模拟情感的存在以验证不解释其疼痛评估模型时，我们会观察到识别性能的降解。相反，当我们计算影响时，我们会观察到表现的提升。
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.
