Managing post-surgical pain is critical for successful surgical outcomes. One of the challenges of pain management is accurately assessing the pain level of patients. Self-reported numeric pain ratings are limited because they are subjective, can be affected by mood, and can influence the patient's perception of pain when making comparisons. In this paper, we introduce an approach that analyzes 2D and 3D facial keypoints of post-surgical patients to estimate their pain intensity level. Our approach leverages the previously unexplored capabilities of a smartphone to capture a dense 3D representation of a person's face as input for pain intensity level estimation. Our contributions are adata collection study with post-surgical patients to collect ground-truth labeled sequences of 2D and 3D facial keypoints for developing a pain estimation algorithm, a pain estimation model that uses multiple instance learning to overcome inherent limitations in facial keypoint sequences, and the preliminary results of the pain estimation model using 2D and 3D features with comparisons of alternate approaches.

中文翻译：

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使用 2D 和 3D 面部关键点从移动视频估计疼痛强度

管理术后疼痛对于成功的手术结果至关重要。疼痛管理的挑战之一是准确评估患者的疼痛程度。自我报告的数字疼痛评级是有限的，因为它们是主观的，会受到情绪的影响，并且会在进行比较时影响患者对疼痛的感知。在本文中，我们介绍了一种分析术后患者的 2D 和 3D 面部关键点以估计其疼痛强度水平的方法。我们的方法利用智能手机以前未开发的功能来捕获人脸的密集 3D 表示，作为疼痛强度水平估计的输入。