Parkinsonian gait modelling from an anomaly deep representation

Parkinson’s Disease (PD) is associated with gait movement disorders, such as bradykinesia, stiffness, tremors and postural instability. Hence, a kinematic gait analysis for PD characterization is key to support diagnosis and to carry out an effective treatment planning. Nowadays, automatic classification and characterization strategies are based on deep learning representations, following supervised rules, and assuming large and stratified data. Nonetheless, such requirements are far from real clinical scenarios. Additionally, supervised rules may introduce bias into architectures from expert’s annotations. This work introduces a self-supervised generative representation to learn gait-motion-related patterns, under the pretext task of video reconstruction. Following an anomaly detection framework, the proposed architecture can avoid inter-class variance, learning hidden and complex kinematics locomotion relationships. In this study, the proposed model was trained and validated with an owner dataset (14 Parkinson and 23 control). Also, an external public dataset (16 Parkinson, 30 control, and 50 Knee-arthritis) was used only for testing, measuring the generalization capability of the method. During training, the method learns from control subjects, while Parkinson subjects are detected as anomaly samples. From owner dataset, the proposed approach achieves a ROC-AUC of 95% in classification task. Regarding the external dataset, the architecture evidence generalization capabilities, achieving a 75% of ROC-AUC (shapeness and homoscedasticity of 66.7%), without any additional training. The proposed model has remarkable performance in detecting gait parkinsonian patterns, recorded in markerless videos, even competitive results with classes non-observed during training.