Evaluation of Overhand Throwing Among College Students With and Without Autism Spectrum Disorder.

Children with autism spectrum disorder (ASD) typically demonstrate deficits in gross motor skills such as the overhand throw. It has not been determined whether such deficits persist into adulthood. Therefore, the purpose of this study was to examine the kinematics and developmental level of overhand throws among young adults with and without ASD. Three-dimensional motion-capture data were collected during overhand throwing trials performed by 20 college students (10 students with ASD). Individuals with ASD demonstrated similar throw duration, stride length, and step width but a longer acceleration phase and slower ball velocity than individuals without ASD. Young adults with ASD also performed the overhand throw with less developmental proficiency than those without ASD. Specifically, individuals with ASD exhibited developmental deficits in the backswing and composite throwing score. Motor skill interventions for individuals with ASD should address throwing skills, with a particular focus on the preparatory phase of the overhand throw.

Automated identification of postural control for children with autism spectrum disorder using a machine learning approach.

It is unclear whether postural sway characteristics could be used as diagnostic biomarkers for autism spectrum disorder (ASD). The purpose of this study was to develop and validate an automated identification of postural control patterns in children with ASD using a machine learning approach. 50 children aged 5-12 years old were recruited and assigned into two groups: ASD (n = 25) and typically developing groups (n = 25). Participants were instructed to stand barefoot on two feet and maintain a stationary stance for 20 s during two conditions: (1) eyes open and (2) eyes closed. The center of pressure (COP) data were collected using a force plate. COP variables were computed, including linear displacement, total distance, sway area, and complexity. Six supervised machine learning classifiers were trained to classify the ASD postural control based on these COP variables. All machine learning classifiers successfully identified ASD postural control patterns based on the COP features with high accuracy rates (>0.800). The naïve Bayes method was the optimal means to identify ASD postural control with the highest accuracy rate (0.900), specificity (1.000), precision (1.000), F1 score (0.898) and satisfactory sensitivity (0.826). By increasing the sample size and analyzing more data/features of postural control, a better classification performance would be expected. The use of computer-aided machine learning to assess COP data is efficient, accurate, with minimum human intervention and thus, could benefit the diagnosis of ASD.

Complexity of Center of Pressure in Postural Control for Children With Autism Spectrum Disorders Was Partially Compromised.

The purpose of this study was to compare the complexity of postural control between children with autism spectrum disorder (ASD) and typical developing children during altered visual and somatosensory conditions using the multiscale entropy. Eleven children with ASD and 11 typical developing children were tested during quiet standing under 4 conditions: (1) eyes open and standing on a stable surface, (2) eyes open and standing on a compliant surface, (3) eyes closed and standing on a stable surface, and (4) eyes closed and standing on a compliant surface. The center of pressure data were collected, and multiscale entropy and sway area of center of pressure were calculated. The ASD group exhibited lower complexity in mediolateral sway compared with typical developing children with a large effect size (partial η2 = .21). However, based on the different postural control modes, the anteroposterior sway complexity did not demonstrate a similar decrease for children with ASD. The altered visual or somatosensory conditions alone did not significantly affect the postural sway complexity. The authors concluded that the complexity of postural control for children with ASD was partially compromised. Reduced mediolateral sway complexity could potentially increase the risks of fall.