There is a growing interest in using pose estimation algorithms for video-based assessment of Bradykinesia in Parkinson's Disease (PD) to facilitate remote disease assessment and monitoring. However, the accuracy of pose estimation algorithms in videos from video streaming services during Telehealth appointments has not been studied. In this study, we used seven off-the-shelf hand pose estimation models to estimate the movement of the thumb and index fingers in videos of the finger-tapping (FT) test recorded from Healthy Controls (HC) and participants with PD and under two different conditions: streaming (videos recorded during a live Zoom meeting) and on-device (videos recorded locally with high-quality cameras). The accuracy and reliability of the models were estimated by comparing the models' output with manual results. Three of the seven models demonstrated good accuracy for on-device recordings, and the accuracy decreased significantly for streaming recordings. We observed a negative correlation between movement speed and the model's accuracy for the streaming recordings. Additionally, we evaluated the reliability of ten movement features related to bradykinesia extracted from video recordings of PD patients performing the FT test. While most of the features demonstrated excellent reliability for on-device recordings, most of the features demonstrated poor to moderate reliability for streaming recordings. Our findings highlight the limitations of pose estimation algorithms when applied to video recordings obtained during Telehealth visits, and demonstrate that on-device recordings can be used for automatic video-assessment of bradykinesia in PD.

翻译：近年来，利用姿态估计算法对帕金森病（PD）患者的运动迟缓进行视频化评估，以实现远程疾病评估与监测的研究日益增多。然而，在远程医疗会诊中，视频流媒体服务所获取的视频是否仍能保证姿态估计算法的准确性，目前尚未得到充分研究。在本研究中，我们采用七种现成的手部姿态估计模型，分别对健康对照组（HC）和PD参与者的手指敲击（FT）测试视频中拇指和食指的运动进行估计。视频采集在两种条件下进行：流媒体模式（通过实时Zoom会议录制的视频）和本地设备模式（使用高质量摄像机本地录制的视频）。通过将模型输出与人工标注结果进行对比，评估各模型的准确性与可靠性。结果显示，七种模型中有三种在本地设备录制视频中表现出良好的准确性，而在流媒体录制视频中准确性显著下降。我们观察到，在流媒体录制模式下，运动速度与模型准确性呈负相关。此外，我们还评估了从PD患者FT测试视频中提取的十项与运动迟缓相关的运动特征的可靠性。尽管大多数特征在本地设备录制视频中表现出极佳的可靠性，但在流媒体录制视频中，多数特征的可靠性仅为较差至中等水平。本研究揭示了姿态估计算法在应用于远程医疗会诊视频时的局限性，并表明本地设备录制的视频可用于PD运动迟缓的自动化视频评估。