"Early Detection of Autism Spectrum Disorder Through Video Based Movement and Posture Analysis Using Deep Learning"

Autism Spectrum Disorder (ASD) is a developmental condition that affects communication, behavior, and social interactions. Early detection of ASD is crucial because it helps in providing timely support and interventions for children. Traditional diagnosis methods often rely on behavioral assessments, which can be time-consuming and may miss early signs. Recently, advances in technology, especially deep learning and video analysis, have opened up new possibilities for detecting ASD earlier and more accurately.

Video-based movement and posture analysis is used to identify patterns linked to ASD. By analyzing how children move and behave in videos, deep learning models can detect signs of autism that may not be obvious to human observers. The goal is to explore how effectively deep learning models can help in early ASD detection by focusing on video data. The use of such advanced technology could revolutionize the way autism is diagnosed, allowing for quicker and more precise identification, which can lead to better outcomes for individuals on the spectrum.