Gait Analysis Based Approach for Parkinson's Disease Modeling with Decision Tree Classifiers

Motion mass based approach is adopted in this paper to describe gait movements of patients with Parkinson's disease. In spite of the recent advances in the areas of motion capture and motion analysis, medical community remains sceptical about applying computer aided systems to support modelling and diagnostics of the Parkinson's disease. The set of measurable parameters is limited by the time, step lengths and in some cases angles between the limbs. To complement these data, motion mass parameters describing amount and smoothness of the motions, are computed for each step. Decision tree classifiers a trained to distinguish between the gait motions of patients with diagnosed Parkinson's disease and healthy individuals of the same age. Moreover it is demonstrated that inclusion of the time does not increase model quality.