A Systematic Review on Computer Vision in Play-Based Research and Interventions for Autistic Children

Play holds a prominent role in Autism Research and practice, serving not only as a medium for assessment, providing insights into developmental progress and diagnostic indicators, but also as a foundational element in targeted interventions designed to support specific skills, education, and therapeutic outcomes. Additionally, free, unstructured play offers autistic children vital opportunities for self-expression, exploration, and emotional regulation. Recent advancements in computer vision techniques have significantly expanded the possibilities in Autism Research by capturing nuanced motor behaviors and facilitating interactive environments tailored to enhance social engagement and responsiveness. This paper investigates the application of computer vision techniques within diverse play contexts involving autistic children. A systematic literature review was conducted across six major databases, identifying 39 studies published between January 2014 and January 2026 that met the inclusion criteria. The collected data included the type of computer vision techniques used, participant demographics, and purpose of play and its environment. Most of the included studies used structured play scenarios, employing methods such as facial expression recognition, eye tracking, and pose estimation to objectively assess social communication, emotion recognition, and motor coordination in autistic children. Some studies positioned their approach as targeted interventions, while others utilized play-based tasks for early screening and diagnostic insights. The findings highlight the usefulness of computer vision in enhancing child-centered play and autism-related assessments. However, small sample sizes, methodological inconsistencies, and limited real-world applicability remain significant constraints. Future research should focus on inclusive designs, larger and more diverse participant samples, and longitudinal studies to validate the long-term effectiveness of these systems. By addressing these limitations, computer vision-enhanced play could become a crucial component of both personalized interventions and diagnostic frameworks for autistic individuals.