<p><strong>BACKGROUND: </strong>Early detection of delay or impairment in motor function is important to guide clinical management and inform prognosis during a critical window for the development of motor control in children. The purpose of this study was to investigate the ability of biomechanical measures of early postural control to distinguish infants with future impairment in motor control from their typically developing peers.</p>

<p><strong>METHODS: </strong>We recorded postural control from infants lying in supine in several conditions. We compared various center of pressure metrics between infants grouped by birth status (preterm and full term) and by future motor outcome (impaired motor control and typical motor control).</p>

<p><strong>RESULTS: </strong>One of the seven postural control metrics-path length-was consistently different between groups for both group classifications and for the majority of conditions.</p>

<p><strong>CONCLUSIONS: </strong>Quantitative measures of early spontaneous infant movement may have promise to distinguish early in life between infants who are at risk for motor impairment or physical disability and those who will demonstrate typical motor control. Our observation that center of pressure path length may be a potential early marker of postural instability and motor control impairment needs further confirmation and further investigation to elucidate the responsible neuromotor mechanisms.</p>

<p><strong>IMPACT: </strong>The key message of this article is that quantitative measures of infant postural control in supine may have promise to distinguish between infants who will demonstrate future motor impairment and those who will demonstrate typical motor control. One of seven postural control metrics-path length-was consistently different between groups. This metric may be an early marker of postural instability in infants at risk for physical disability.</p>

<p>An infant's risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as videos recorded on a mobile device. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N = 19). For each infant, we calculate how much they deviate from a group of healthy infants (N = 85 online videos) using a Naïve Gaussian Bayesian Surprise metric. After pre-registering our Bayesian Surprise calculations, we find that infants who are at high risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open-source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.</p>

<p>The World Health Organization estimates that 15 million infants are born preterm every year [1]. This is of concern because these infants have a significant chance of having neuromotor or cognitive developmental delays due to cerebral palsy or other developmental issues [2]. Our long-term goal is to determine the roles emotion and movement play in the diagnosis of atypical infants. In this paper, we examine how automated emotion assessment may have potential to classify typically and atypically developing infants. We compare a custom supervised machine learning algorithm that utilizes individual and grouped facial features for infant emotion classification with a state-of-the-art neural network. Our results show that only three concavity features are needed for the concavity algorithm, and the custom algorithm performed with relatively similar performance to the neural network. Automatic sentiment labels used in tandem with infant movement kinematics would be further investigated to determine if emotion and movement are interdependent and predictive of an infant's neurodevelopmental delay in disorders such as cerebral palsy.</p>