Using deep learning to classify developmental differences in reaching and placing movements in children with and without autism spectrum disorder.

Autism Spectrum Disorder (ASD) is among the most prevalent neurodevelopmental disorders, yet the current diagnostic procedures rely on behavioral analyses and interviews, without objective screening methods to support the diagnostic process. This study seeks to address this gap by integrating upper limb kinematics and deep learning methods to identify potential biomarkers that could be validated in younger age groups in the future to enhance the identification of ASD. Forty-one school-age children, with and without an ASD diagnosis (mean age ± SE: TD group: 10.3 ± 0.8, 8 males and 7 females; ASD group: 10.3 ± 0.5, 21 males and 5 females), participated in the study. A single Inertial Measurement Unit (IMU) was affixed to the child's wrist as they engaged in a continuous reaching and placing task. Deep learning techniques were employed to classify children with and without ASD. Our findings suggest differential movement kinematics in school-age children compared to healthy adults. Compared to TD children, children with ASD exhibited poor feedforward/feedback control of arm movements as seen by greater number of movement units, more movement overshooting, and prolonged time to peak velocity/acceleration. Unique movement strategies such as greater velocity and acceleration were also seen in the ASD group. More importantly, using Multilayer Perceptron (MLP) model, we demonstrated an accuracy of ~ 78.1% in classifying children with and without ASD. These findings underscore the potential use of studying upper limb movement kinematics during goal-directed arm movements and deep learning methods as valuable tools for classifying and, consequently, aiding in the diagnosis and early identification of ASD upon further validation of their specificity among younger children.