Parkinson's Disease is associated with gait movement disorders, such as postural instability, stiffness, and tremors. Today, some approaches implemented learning representations to quantify kinematic patterns during locomotion, supporting clinical procedures such as diagnosis and treatment planning. These approaches assumes a large amount of stratified and labeled data to optimize discriminative representations. Nonetheless, these considerations may restrict the operability of approaches in real scenarios during clinical practice. This work introduces a self-supervised generative representation, under the pretext of video reconstruction and anomaly detection framework. This architecture is trained following a one-class weakly supervised learning to avoid inter-class variance and approach the multiple relationships that represent locomotion. For validation 14 PD patients and 23 control subjects were recorded, and trained with the control population only, achieving an AUC of 86.9%, homoscedasticity level of 80% and shapeness level of 70% in the classification task considering its generalization.

翻译：帕金森病常伴随步态运动障碍，如姿势不稳、僵硬和震颤。当前，部分方法通过构建学习表征来量化行走过程中的运动学模式，为诊断与治疗规划等临床操作提供支持。这些方法通常依赖大量分层标注数据以优化判别性表征，但在临床实践中，此类条件可能限制方法在真实场景中的可操作性。本研究提出一种基于视频重构与异常检测框架的自监督生成性表征，采用单类弱监督学习策略进行训练，以避免类间方差并捕捉表征步态的多重关联。为验证模型性能，采集14例帕金森患者与23名对照受试者的数据，仅使用对照组数据进行训练，在泛化分类任务中达到86.9%的AUC、80%的方差齐性水平及70%的锐度水平。