论文标题
ICU数据的面部动作单元检测疼痛评估
Facial Action Unit Detection on ICU Data for Pain Assessment
论文作者
论文摘要
当前的疼痛评估方法依赖于患者自我报告或像重症监护病房（ICU）护士这样的观察者。患者的自我报告对个人是主观的，并且由于召回率不佳而受到遭受痛苦。通过手动观察评估的疼痛评估受到每天管理的数量和员工工作量的限制。先前的研究表明，通过检测面部作用单位（AUS）来自动疼痛评估的可行性。观察到疼痛与某些面部动作单位（AUS）有关。这种疼痛评估方法可以克服当今疼痛评估技术的陷阱。所有以前的研究都仅限于受控环境数据。在这项研究中，我们评估了OpenFace在实际ICU数据上的开源面部行为分析工具和AU R-CNN的性能。辅助呼吸设备的存在，ICU的可变照明，相对于相机的患者取向显着影响模型的性能，尽管这些表现出了面部行为分析任务的最新结果。在这项研究中，我们显示了对自动疼痛评估系统的需求，该系统接受了现实世界中的ICU数据培训，以实现临床上可接受的疼痛评估系统。
Current day pain assessment methods rely on patient self-report or by an observer like the Intensive Care Unit (ICU) nurses. Patient self-report is subjective to the individual and suffers due to poor recall. Pain assessment by manual observation is limited by the number of administrations per day and staff workload. Previous studies showed the feasibility of automatic pain assessment by detecting Facial Action Units (AUs). Pain is observed to be associated with certain facial action units (AUs). This method of pain assessment can overcome the pitfalls of present-day pain assessment techniques. All the previous studies are limited to controlled environment data. In this study, we evaluated the performance of OpenFace an open-source facial behavior analysis tool and AU R-CNN on the real-world ICU data. Presence of assisted breathing devices, variable lighting of ICUs, patient orientation with respect to camera significantly affected the performance of the models, although these showed the state-of-the-art results in facial behavior analysis tasks. In this study, we show the need for automated pain assessment system which is trained on real-world ICU data for clinically acceptable pain assessment system.
