An Optical Flow-Based Method to Predict Infantile Cerebral Palsy

Cerebral palsy (CP) is a perinatally acquired nonprogressive brain damage resulting in motor impairment affecting mobility and posture. Early identification of infants with CP is desired to target early interventions and follow-up. During early infancy, distinct motion patterns occur which are highly predictive for later disability. These motor patterns can be observed and recorded. In this paper, a method to predict later CP based on early video recordings of the infants´ spontaneous movements, applying optical flow and statistical pattern recognition, is presented. We extract motion information from video recordings of young infants using a total variation related optical flow method. By using wavelet analysis features from motion trajectories of points initiated on a regular grid were extracted and classified using a support vector machine.