Abstract
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.
Abstract (translated)
帕金森病与步态运动障碍有关,例如姿势不稳定、僵硬和颤抖。如今,一些方法实现了学习表示,以量化步态期间的机械运动模式,支持诊断和治疗规划等临床程序。这些方法假设了大量的分类数据和标签数据,以优化分类表示。然而,这些考虑可能限制在临床实践中方法的真实可行性。这项工作引入了自我监督生成表示,以视频重构和异常检测框架为借口。该架构采用一维弱监督学习,以避免不同类别之间的差异,并接近代表步态的多个关系。为验证性,记录了14名PD患者和23名控制参与者,仅使用控制群体进行训练,在分类任务中取得了AUC为86.9%,同态分布水平为80%和形状水平为70%。
URL
https://arxiv.org/abs/2301.11418
