Parkinson gait modelling from an anomaly deep representation
This work addresses gait analysis for Parkinson's disease diagnosis, offering a method that avoids the need for large labeled datasets, though it is incremental in applying existing anomaly detection techniques to this domain.
The paper tackled the problem of modeling Parkinson's gait by introducing a self-supervised generative representation for anomaly detection, achieving an AUC of 95% in classification with validation on 14 patients and 23 controls.
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.