Vision-Based Detection of Simultaneous Kicking for Identifying Movement Characteristics of Infants At-Risk for Neuro-Disorders

Abstract

Neuro disorders such as Cerebral Palsy (CP) and Infantile Spasms (IS) in infants can cause a wide range of developmental coordination disorders (DCD). Simultaneous, non-complex, kicking patterns that persist in infants of 4-7 months of age is highly suggestive of a neuro-disorder. Early establishment of risk levels for infants at risk for neuro-disorders is beneficial for early intervention. To provide a method to track early infant kicking movements for determining risk-level, an automated method is established to track and classify periods of simultaneous (SM), non-simultaneous movements (NSM) and no movement (NM) during infant kicking actions. In this paper, a computer vision algorithm uses KAZE points to track infant kicking and collect kinematic data. Each movement type is classified by computing unique feature criterion and using a support vector machine (SVM) for learning a movement model. We discuss the significance of the classifier as well as analyze the percentage break down of movement types for typical infants and infants with IS.