<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>

<p><strong>PURPOSE: </strong>To investigate foot and ankle somatosensory function in children with cerebral palsy (CP).</p>

<p><strong>METHODS: </strong>Ten children with spastic diplegia (age 15 ± 5 y; GMFCS I-III) and 11 typically developing (TD) peers (age 15 ± 10 y) participated in the study. Light touch pressure and two-point discrimination were assessed on the plantar side of the foot by using a monofilament kit and an aesthesiometer, respectively. The duration of vibration sensation at the first metatarsal head and medial malleolus was tested by a 128 Hz tuning fork. Joint position sense and kinesthesia in the ankle joint were also assessed.</p>

<p><strong>RESULTS: </strong>Children with CP demonstrated significantly higher light touch pressure and two-point discrimination thresholds compared to their TD peers. Individuals with CP perceived the vibration stimulus for a longer period compared to the TD participants. Finally, the CP group demonstrated significant impairments in joint position sense but not in kinesthesia of the ankle joints.</p>

<p><strong>CONCLUSIONS: </strong>These findings suggest that children with CP have foot and ankle tactile and proprioceptive deficits. Assessment of lower extremity somatosensory function should be included in clinical practice as it can guide clinicians in designing more effective treatment protocols to improve functional performance in CP.</p>