  Parkinson's Disease (PD) is associated with gait movement disorders, such as
bradykinesia, stiffness, tremors and postural instability, caused by
progressive dopamine deficiency. Today, some approaches have 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 approaches to be operable in real scenarios during clinical practice. This
work introduces a self-supervised generative representation to learn
gait-motion-related patterns, under the pretext of video reconstruction and an
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. The proposed approach was
validated with 14 PD patients and 23 control subjects, and trained with the
control population only, achieving an AUC of 95%, homocedasticity level of 70%
and shapeness level of 70% in the classification task considering its
generalization.
