Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by difficulties in social communication and interaction, as well as restricted or repetitive behaviors. Early detection of autism is crucial for effective intervention, yet existing diagnostic methods often rely on subjective assessments and can be time-consuming, costly, and inaccessible to many families. The proposed research aims to develop an innovative, objective, and scalable digital phenotyping tool that uses advanced computer vision and machine learning techniques to identify early signs of autism in young children. By leveraging a combination of engaging stimuli and interactive games presented on a mobile application, this project seeks to transform autism screening by making it more efficient, accurate, and accessible.
National Institute Of Technology Kurukshetra
pratishthaverma@nitkkr.ac.in
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