Kernel-Based Object Tracking for Cerebral Palsy Detection

Cerebral palsy is a chronic conditions affecting body movements, posture and muscle coordination. It is caused by damage to one or more specific areas of the brain, usually occurring during fetal development or infancy. The General Movement Assessment procedure consists of observation and clinical classification of movement patterns, and the absence of normal movement qualities between 2-4 months post term age has been shown to be a strong predictor of later cerebral palsy. In this paper we present a method for estimating motion trajectories of infant limbs and head in order to assess general movements. We extract the motion information from video captured from infants. For extracting motion data, a combined Bayesian filtering and kernel-based tracker is used and the appropriate modification applied on the previous methods. Result of tracking experiments shows high performance of the proposed method.