Abstract
Movement disorders are typically diagnosed by consensus-based expert evaluation of clinically acquired patient videos. However, such broad sharing of patient videos poses risks to patient privacy. Face blurring can be used to de-identify videos, but this process is often manual and time-consuming. Available automated face blurring techniques are subject to either excessive, inconsistent, or insufficient facial blurring - all of which can be disastrous for video assessment and patient privacy. Furthermore, assessing movement disorders in these videos is often subjective. The extraction of quantifiable kinematic features can help inform movement disorder assessment in these videos, but existing methods to do this are prone to errors if using pre-blurred videos. We have developed an open-source software called SecurePose that can both achieve reliable face blurring and automated kinematic extraction in patient videos recorded in a clinic setting using an iPad. SecurePose, extracts kinematics using a pose estimation method (OpenPose), tracks and uniquely identifies all individuals in the video, identifies the patient, and performs face blurring. The software was validated on gait videos recorded in outpatient clinic visits of 116 children with cerebral palsy. The validation involved assessing intermediate steps of kinematics extraction and face blurring with manual blurring (ground truth). Moreover, when SecurePose was compared with six selected existing methods, it outperformed other methods in automated face detection and achieved ceiling accuracy in 91.08% less time than a robust manual face blurring method. Furthermore, ten experienced researchers found SecurePose easy to learn and use, as evidenced by the System Usability Scale. The results of this work validated the performance and usability of SecurePose on clinically recorded gait videos for face blurring and kinematics extraction.
Abstract (translated)
通常，运动障碍的诊断是通过基于共识的专家对临床获得的病人视频进行评估得出的。然而，如此广泛的共享病人视频会对患者隐私造成风险。可以采用面部模糊来删除视频中的身份信息，但这种过程通常是手动且耗时费力的。已有的自动面部模糊技术要么过度模糊，要么缺乏足够的模糊，这些都可能对视频评估和患者隐私造成灾难性的影响。此外，评估这些视频中的运动障碍通常是主观的。计算运动特征以帮助告知这些视频中的运动障碍评估是一种可行的方法，但现有的方法在使用预模糊视频时容易出错。我们开发了一个名为SecurePose的免费开源软件，可以在患者视频中使用iPad进行 clinic 环境下记录的可靠面部模糊和自动运动特征提取。SecurePose使用姿态估计方法（OpenPose）提取运动学，跟踪并唯一标识视频中所有的人，识别患者，并执行面部模糊。该软件在116名脊髓性截瘫儿童的外科诊所访问中记录的步态视频上进行了验证。验证包括使用手动模糊的中间步骤评估运动学提取和面部模糊（真实值）。此外，当SecurePose与其他六个选择的方法进行比较时，它在自动面部检测方面超过了其他方法，用时减少了91.08%。此外，十名有经验的研究人员发现SecurePose易于学习和使用，正如System Usability Scale所证明的。本工作的结果证实了SecurePose在临床上记录的步态视频中的面部模糊和运动学提取的性能和可用性。
URL
https://arxiv.org/abs/2402.14143
