Automatically Measure the Quality of Infants’ Spontaneous Movement via Videos to Predict the Risk of Cerebral Palsy

Abstract

Objective: Early brain development assessment of infants is very important for infant growth and development. Timely diagnosis of cerebral palsy can reduce its negative effects. In this paper, a novel automatic measurement method was proposed for early prediction of infant cerebral palsy with videos towards Prechtl’s General Movements Assessment (GMA). Firstly, we adopted the-state-of-the-art human pose estimation method to obtain the 2D or 3D posture of the lying infant. After a series of interpolation and filtering, continuous infant’s limbs movements curves were obtained. Through appropriate feature extraction, the infant’s limbs movements complexity and the movements correlation between the limbs were integrated, and finally a comprehensive evaluation score for evaluating the complexity of the infant’s spontaneous movements was obtained. To verify our score, we did some experiments on a public dataset consists of 12 real infants’ movements. Our method achieved the state-of-the-art with sensitivity of 100%, specificity of 87.5%, and accuracy of 91.67% on the public dataset whether in 2D or 3D posture input. We also collected another 47 cases of infant movement color videos ourselves. And achieved an accuracy of 91.5%, sensitivity of 100%, and specificity of 87.8% on all 59 (12+47) subjects. The results show that our score method has a great prospect in assessing the infant’s early brain development. The proposed method can be widely used in the screening of babies in homes and hospitals.