Pain Expressions in Dementia: Validity of Observers' Pain Judgments as a Function of Angle of Observation
Pain Expressions in Dementia: Validity of Observers' Pain Judgments as a Function of Angle of Observation
复制标题
DOI：
10.1007/s10919-019-00303-4
复制
发表时间：
2019-09-01
影响因子：
2.1
通讯作者：
Taati, Babak
中科院分区：
文献类型：
--
作者：
Browne, M. Erin;Hadjistavropoulos, Thomas;Taati, Babak
疼痛的面部表情在评估患有痴呆症和严重沟通障碍的个体时很重要。虽然假设面部的正面视图允许进行最有效和最可靠的观察评估，但视角的影响尚不清楚。我们使用摄像机拍摄不同的观察角度（例如，前视图与侧视图）在设计用于识别疼痛区域的理疗检查期间以及在基线期期间。面部反应编码使用细粒度面部动作编码系统，以及系统的临床观察方法。编码分别进行全景（包括左，右，前视图），和面部的轮廓视图。未经训练的观察者也在实验室环境中对视频进行了评判。训练编码器的可靠性是令人满意的轮廓和全景。未经训练的观察者的判断，从侧面视图相比，前视图更准确，并占更多的差异，区分非疼痛的情况。这些发现为疼痛的沟通模型增加了特异性（澄清了影响观察者解码疼痛信息能力的因素）。也许更重要的是，这些发现对计算机视觉算法和视觉技术的发展具有影响，这些算法和技术旨在监测和解释疼痛背景下的面部表情。也就是说，这种自动化系统的性能受到这些人类注释可以被提供的可靠性的严重影响，因此，从多个观察角度对人类观察者的可靠性的评估对机器学习开发工作具有影响。
Facial expressions of pain are important in assessing individuals with dementia and severe communicative limitations. Though frontal views of the face are assumed to allow for the most valid and reliable observational assessments, the impact of viewing angle is unknown. We video-recorded older adults with and without dementia using cameras capturing different observational angles (e.g., front vs. profile view) both during a physiotherapy examination designed to identify painful areas and during a baseline period. Facial responses were coded using the fine-grained Facial Action Coding System, as well as a systematic clinical observation method. Coding was conducted separately for panoramic (incorporating left, right, and front views), and a profile view of the face. Untrained observers also judged the videos in a laboratory setting. Trained coder reliability was satisfactory for both the profile and panoramic view. Untrained observer judgments from a profile view were substantially more accurate compared to the front view and accounted for more variance in differentiating non-painful from painful situations. The findings add specificity to the communications models of pain (clarifying factors influencing observers' ability to decode pain messages). Perhaps more importantly, the findings have implications for the development of computer vision algorithms and vision technologies designed to monitor and interpret facial expressions in a pain context. That is, the performance of such automated systems is heavily influenced by how reliably these human annotations could be provided and, hence, evaluation of human observers' reliability, from multiple angles of observation, has implications for machine learning development efforts.