Body Posture-Based Detection of Autism Spectrum Disorder in Children

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behaviors. Diagnosing and intervening in ASD is challenging, yet early intervention is crucial for the development of affected children. We collected data from 261 participants, including 140 typically developing (TD) children and 121 ASD children, who participated in three experimental paradigms: human/object preference, joint attention, and sound response. Using HRNet, we extracted skeletal keypoint coordinates from video clips and applied the PoTion algorithm to generate joint motion trajectory maps, further analyzing the body movement characteristics of ASD and TD children. The study revealed significant differences in the body movement patterns of ASD children, characterized by a wider range of motion and irregular movement patterns. Additionally, by classifying the motion trajectory maps using a CNN network, we found that the joint attention paradigm achieved the highest classification accuracy at 87%, indicating that it contains more distinguishing information. This study proposed a framework based on MTCNN and HRNet algorithms for accurately extracting children’s body movement features in multiperson scenarios. The introduction of the PoTion algorithm for visualizing joint motion trajectories provided a more intuitive and precise method for analyzing the body movement characteristics of ASD and TD children. Our in-depth analysis revealed significant differences in joint distances, angles, and velocities, offering new insights into the relationship between cognition and movement in ASD children.