The onset and progression of Parkinson’s disease (PD) gradually affect the patient’s motor functions and quality of life. The PD motor symptoms are usually assessed using the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated MDS-UPDRS assessment has been recently required as an invaluable tool for PD diagnosis and telemedicine, especially with the recent novel coronavirus pandemic outbreak. This paper proposes a novel vision-based method for automated assessment of the arising-from-chair task, which is one of the key MDS-UPDRS components. The proposed method is based on a self-supervised metric learning scheme with a graph convolutional network (SSM-GCN). Specifically, for human skeleton sequences extracted from videos, a self-supervised intra-video quadruplet learning strategy is proposed to construct a metric learning formulation with prior knowledge, for improving the spatial-temporal representations. Afterwards, a vertex-specific convolution operation is designed to achieve effective aggregation of all skeletal joint features, where each joint or feature is weighted differently based on its relative factor of importance. Finally, a graph representation supervised mechanism is developed to maximize the potential consistency between the joint and bone information streams. Experimental results on a clinical dataset demonstrate the superiority of the proposed method over the existing sensor-based methods, with an accuracy of 70.60% and an acceptable accuracy of 98.65%. The analysis of discriminative spatial connections makes our predictions more clinically interpretable. This method can achieve reliable automated PD assessment using only easily-obtainable videos, thus providing an effective tool for real-time PD diagnosis or remote continuous monitoring.

中文翻译：

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利用图卷积网络对帕金森病患者进行椅子起身评估的自监督度量学习框架


帕金森病（PD）的发病和进展逐渐影响患者的运动功能和生活质量。 PD 运动症状通常使用运动障碍协会赞助的统一帕金森病评定量表 (MDS-UPDRS) 修订版进行评估。最近需要自动化 MDS-UPDRS 评估作为 PD 诊断和远程医疗的宝贵工具，特别是在最近新型冠状病毒大流行爆发的情况下。本文提出了一种基于视觉的新颖方法，用于自动评估从椅子上起身的任务，这是 MDS-UPDRS 的关键组成部分之一。所提出的方法基于具有图卷积网络（SSM-GCN）的自监督度量学习方案。具体来说，对于从视频中提取的人体骨骼序列，提出了一种自监督的视频内四元组学习策略，以利用先验知识构建度量学习公式，以改善时空表示。然后，设计特定于顶点的卷积运算以实现所有骨骼关节特征的有效聚合，其中每个关节或特征根据其相对重要性因素进行不同的加权。最后，开发了图形表示监督机制，以最大限度地提高关节和骨骼信息流之间的潜在一致性。临床数据集上的实验结果表明，该方法相对于现有的基于传感器的方法具有优越性，准确度为 70.60%，可接受的准确度为 98.65%。对判别性空间联系的分析使我们的预测更具临床解释性。 该方法只需使用易于获取的视频即可实现可靠的自动化局放评估，从而为实时局放诊断或远程连续监测提供有效工具。