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

ABSTRACT: 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

SUBMITTER: Su WC 

PROVIDER: S-EPMC11618337 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature