Parkinsonian Gait Characterization From Regional Kinematic Trajectories

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a set of progressive motor disabilities knows as shuffling gait patterns. The diagnosis and treatment of parkinsonian patients at different stages is typically supported by a Kinematic analysis. In clinical routine, such analysis is related with the quantitative and qualitative description of body segment displacements, computed from a reduced set of markers. Nevertheless, classical markers-based analysis has strong limitations to capture local and regional dynamic relationships associated with shuffling gait patterns. Particularly, the sparse set of markers lost sensitivity to detect progression of disease and commonly this kinematic characterization is restricted only to advanced stages. This work introduces a new hierarchical parkinsonian gait descriptor that coded kinematics at local and regional levels. At local level, a Spatial Kinematic Pattern (SKP) is computed as circular binary occurrence vectors, along trajectories. Regionally, such local vectors are grouped to describe body segments motions. Each of these regions coarsely correspond to the head, trunk and limbs. From each independent region is possible to describe kinematic patterns associated with the disease. The proposed approach was validated into a classification scheme to differentiate among regional parkinsonian patterns w.r.t to control patterns. Hence, each coding region descriptor was mapped to a support vector machine model. The proposed method was evaluated from a set of 84 gait videos of control and parkinsonian patients, achieving an average accuracy of 84, 52%.