Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4163-4168. doi: 10.1109/EMBC46164.2021.9631056.
Pain is a personal, subjective experience, and the current gold standard to evaluate pain is the Visual Analog Scale (VAS), which is self-reported at the video level. One problem with the current automated pain detection systems is that the learned model doesn't generalize well to unseen subjects. In this work, we propose to improve pain detection in facial videos using individual models and uncertainty estimation. For a new test video, we jointly consider which individual models generalize well generally, and which individual models are more similar/accurate to this test video, in order to choose the optimal combination of individual models and get the best performance on new test videos. We show on the UNBC-McMaster Shoulder Pain Dataset that our method significantly improves the previous state-of-the-art performance.
疼痛是一种个人的、主观的体验，目前评估疼痛的金标准是视觉模拟评分（VAS），它是在视频水平上自我报告的。目前的自动疼痛检测系统存在一个问题，即学习到的模型不能很好地推广到未见过的对象。在这项工作中，我们提出使用个体模型和不确定性估计来改进面部视频中的疼痛检测。对于新的测试视频，我们同时考虑哪些个体模型通常具有良好的泛化能力，以及哪些个体模型与该测试视频更相似/更准确，以选择个体模型的最佳组合，并在新的测试视频上获得最佳性能。我们在 UNBC-McMaster 肩部疼痛数据集上的结果表明，我们的方法显著提高了先前的最先进性能。
