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Infants' spontaneous and voluntary movements mirror developmental integrityof brain networks since they require coordinated activation of multiple sitesin the central nervous system. Accordingly, early detection of infants withatypical motor development holds promise for recognizing those infants who areat risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy,autism spectrum disorders). Previously, novel wearable technology has shownpromise for offering efficient, scalable and automated methods for movementassessment in adults. Here, we describe the development of an infant wearable,a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscopedata collection during movements. Using this suit, we first recorded playsessions of 22 typically developing infants of approximately 7 months of age.These data were manually annotated for infant posture and movement based onvideo recordings of the sessions, and using a novel annotation schemespecifically designed to assess the overall movement pattern of infants in thegiven age group. A machine learning algorithm, based on deep convolutionalneural networks (CNNs) was then trained for automatic detection of posture andmovement classes using the data and annotations. Our experiments show that thesetup can be used for quantitative tracking of infant movement activities witha human equivalent accuracy, i.e., it meets the human inter-rater agreementlevels in infant posture and movement classification. We also quantify theambiguity of human observers in analyzing infant movements, and propose amethod for utilizing this uncertainty for performance improvements in trainingof the automated classifier. Comparison of different sensor configurations alsoshows that four-limb recording leads to the best performance in posture andmovement classification.

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors