论文标题
使用2D和3D面部关键点对移动视频的疼痛强度估算
Pain Intensity Estimation from Mobile Video Using 2D and 3D Facial Keypoints
论文作者
论文摘要
管理后手术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水平。自我报告的数字疼痛等级受到限制，因为它们是主观的，可能会受到情绪的影响，并且会影响患者进行比较时对疼痛的看法。在本文中，我们介绍了一种方法，该方法分析了术后患者的2D和3D面部关键点，以估计其疼痛强度水平。我们的方法利用智能手机以前未开发的功能来捕获一个人脸的密集3D表示，作为疼痛强度水平估计的输入。我们的贡献是对手术后患者的ADATA收集研究，以收集以下标记为2D和3D面部关键的序列，以开发疼痛估计算法，这是一个疼痛估计模型，该模型使用多个实例学习来克服面部关键点序列中的固有限制，以及使用2D和3D功能的疼痛估计模型的预测结果。
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.
