Involuntary gesture recognition for predicting cerebral palsy in high-risk infants

Abstract

In this paper we describe a system that leverages accelerometers to recognize a particular involuntary gesture in babies that have been born preterm. These gestures, known as cramped-synchronized general movements are highly correlated with a diagnosis of Cerebral Palsy. In order to test our system we recorded data from 10 babies admitted to the newborn intensive care unit at the UCI Medical Center. We applied machine learning techniques to features based on their data and were able to obtain accuracies between 70% and 90% depending on the relative cost of false positives and false negatives. Validated video observation annotations were utilized as ground truth. Finally, we conducted an analysis to understand the basis of the algorithmic predictions.