  Infants with a variety of complications at or before birth are classified as
being at risk for developmental delays (AR). As they grow older, they are
followed by healthcare providers in an effort to discern whether they are on a
typical or impaired developmental trajectory. Often, it is difficult to make an
accurate determination early in infancy as infants with typical development
(TD) display high variability in their developmental trajectories both in
content and timing. Studies have shown that spontaneous movements have the
potential to differentiate typical and atypical trajectories early in life
using sensors and kinematic analysis systems. In this study, machine learning
classification algorithms are used to take inertial movement from wearable
sensors placed on an infant for a day and predict if the infant is AR or TD,
thus further establishing the connection between early spontaneous movement and
developmental trajectory.
