Artificial Intelligence for Movement Analysis in Autism Spectrum Disorder: A Scoping Review of Gait, Stereotyped Motor Movements, and Motor Behaviors

Motor differences are increasingly recognized as relevant early markers of autism spectrum disorder (ASD), although they are not part of the core diagnostic criteria. Recent advances in computer vision, pose estimation, and machine learning have enabled the quantitative analysis of movement patterns, particularly stereotyped motor movements (SMMs) and gait deviations. This scoping review examines recent artificial intelligence approaches for analyzing motor and movement patterns in individuals with ASD, with particular emphasis on stereotyped motor movements (SMMs) and gait-related differences. The reviewed studies used 2D and 3D pose estimation, conventional machine learning algorithms, and deep learning models to extract movement features from video, skeletal, depth, and sensor-based data. The included studies addressed different outcomes, including ASD versus TD classification, SMM and repetitive-behavior detection, gait abnormality classification or characterization, motor phenotype assessment, and movement measurement or validation. Because these studies examined different tasks and used different performance metrics, their results cannot be directly compared. However, small or private datasets, heterogeneous protocols, and limited external validation restrict cross-study comparison and clinical generalizability. Overall, the current evidence supports technical movement analysis rather than validated clinical ASD screening or diagnosis.