Predicting Infant Motor Development Status using Day Long Movement Data from Wearable Sensors
This work addresses the challenge of early detection of developmental delays in at-risk infants, which is incremental as it builds on prior research linking movement to developmental outcomes.
The study tackled the problem of early prediction of infant developmental delays by using machine learning to classify infants as at-risk or typically developing based on day-long movement data from wearable sensors, establishing a connection between early spontaneous movement and developmental trajectory.
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.