Parkinson gait modelling from an anomaly deep representation

TL;DR

This paper presents a self-supervised generative model for Parkinson's gait analysis, enabling effective anomaly detection with limited labeled data, validated on a clinical dataset with high accuracy.

Contribution

It introduces a novel weakly supervised, one-class learning framework for gait anomaly detection in Parkinson's Disease using video reconstruction.

Findings

Achieved 95% AUC in classification

Validated on 14 PD patients and 23 controls

Demonstrated effective generalization with limited data

Abstract

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.