  Infants' spontaneous and voluntary movements mirror developmental integrity
of brain networks since they require coordinated activation of multiple sites
in the central nervous system. Accordingly, early detection of infants with
atypical motor development holds promise for recognizing those infants who are
at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy,
autism spectrum disorders). Previously, novel wearable technology has shown
promise for offering efficient, scalable and automated methods for movement
assessment in adults. Here, we describe the development of an infant wearable,
a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope
data collection during movements. Using this suit, we first recorded play
sessions of 22 typically developing infants of approximately 7 months of age.
These data were manually annotated for infant posture and movement based on
video recordings of the sessions, and using a novel annotation scheme
specifically designed to assess the overall movement pattern of infants in the
given age group. A machine learning algorithm, based on deep convolutional
neural networks (CNNs) was then trained for automatic detection of posture and
movement classes using the data and annotations. Our experiments show that the
setup can be used for quantitative tracking of infant movement activities with
a human equivalent accuracy, i.e., it meets the human inter-rater agreement
levels in infant posture and movement classification. We also quantify the
ambiguity of human observers in analyzing infant movements, and propose a
method for utilizing this uncertainty for performance improvements in training
of the automated classifier. Comparison of different sensor configurations also
shows that four-limb recording leads to the best performance in posture and
movement classification.
