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BackgroundGait impairment is an essential symptom of Parkinson's disease (PD).ObjectiveThis paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis.MethodsFour 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.ResultsFeatures significantly (pConclusionFindings support the feasibility of this model for Parkinson's gait assessment in the home environment.
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