Ashraf Ahmed Bilal, Lucey Simon, Cohn Jeffrey F, Chen Tsuhan, Ambadar Zara, Prkachin Kenneth M, Solomon Patricia E
Image Vis Comput. 2009 Oct;27(12):1788-1796. doi: 10.1016/j.imavis.2009.05.007.
Pain is typically assessed by patient self-report. Self-reported pain, however, is difficult to interpret and may be impaired or in some circumstances (i.e., young children and the severely ill) not even possible. To circumvent these problems behavioral scientists have identified reliable and valid facial indicators of pain. Hitherto, these methods have required manual measurement by highly skilled human observers. In this paper we explore an approach for automatically recognizing acute pain without the need for human observers. Specifically, our study was restricted to automatically detecting pain in adult patients with rotator cuff injuries. The system employed video input of the patients as they moved their affected and unaffected shoulder. Two types of ground truth were considered. Sequence-level ground truth consisted of Likert-type ratings by skilled observers. Frame-level ground truth was calculated from presence/absence and intensity of facial actions previously associated with pain. Active appearance models (AAM) were used to decouple shape and appearance in the digitized face images. Support vector machines (SVM) were compared for several representations from the AAM and of ground truth of varying granularity. We explored two questions pertinent to the construction, design and development of automatic pain detection systems. First, at what level (i.e., sequence- or frame-level) should datasets be labeled in order to obtain satisfactory automatic pain detection performance? Second, how important is it, at both levels of labeling, that we non-rigidly register the face?
疼痛通常通过患者的自我报告来评估。然而，自我报告的疼痛难以解读，并且可能受到影响，或者在某些情况下（即幼儿和重症患者）甚至无法进行。为了规避这些问题，行为科学家已经确定了可靠且有效的疼痛面部指标。迄今为止，这些方法需要由训练有素的人类观察者进行手动测量。在本文中，我们探索了一种无需人类观察者即可自动识别急性疼痛的方法。具体而言，我们的研究仅限于自动检测患有肩袖损伤的成年患者的疼痛。该系统采用患者移动患侧和未患侧肩部时的视频输入。考虑了两种类型的地面真值。序列级地面真值由技术熟练的观察者给出的李克特式评分组成。帧级地面真值是根据先前与疼痛相关的面部动作的存在/不存在和强度计算得出的。主动外观模型（AAM）用于在数字化面部图像中分离形状和外观。针对来自AAM的几种表示以及不同粒度的地面真值，比较了支持向量机（SVM）。我们探讨了与自动疼痛检测系统的构建、设计和开发相关的两个问题。第一，为了获得令人满意的自动疼痛检测性能，数据集应在什么级别（即序列级或帧级）进行标注？第二，在两个标注级别上，对面部进行非刚性配准有多重要？
