Gait Spatiotemporal Signal Analysis for Parkinson's Disease Detection and Severity Rating

Deep learning models are used to process and fuse raw data of gait-induced ground reaction force (GRF) for Parkinson's disease (PD) patients and healthy subjects with the aim to categorize PD severity. This is achieved by learning automatically, end-to-end, the spatiotemporal GRF signals, resulting in an effective PD severity classification with mean performance F1-score of 95.5% and F1-score standard errors of 0.28%. Layer-wise relevance propagation (LRP) is used to interpret the models' output and provide insight into which features in the spatiotemporal gait GRF signals are most significant for the models' predictions. This allows their assignment to gait events, implying that while for the classification of healthy gait the heel strike and body balance are the most indicative gait elements, foot landing and body balance are those most affected in advanced stages of PD. The proposed models are resilient to noise and are computationally efficient for processing and classification of large longitudinal GRF signal datasets, therefore they can be useful for detecting deterioration in the postural balance and rating PD severity.