Abstract
BACKGROUND: Gait impairment is an essential symptom of Parkinson's disease (PD). OBJECTIVE: This paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis. METHODS: Four hundred and fifty-six videos were recorded from 19 PD patients using an RGB camera during clinical gait assessment. Gait performance in each video was rated by a neurologist using the unified Parkinson's disease rating scale for gait examination (UPDRS-gait). The proposed algorithm detects and tracks the silhouette of the test subject in the video to generate a height signal. Gait features were extracted from the height signal. Feature analysis was performed using the Kruskal-Wallis rank test. A support vector machine was trained using the features to classify the severity levels according to UPDRS-gait in 10-fold cross-validation. RESULTS: Features significantly (p< 0.05) differentiated between median-ranks of UPDRS-gait levels. The SVM classified the levels with a promising area under the ROC of 80.88%. CONCLUSION: Findings support the feasibility of this model for Parkinson's gait assessment in the home environment.