Multimodal Machine Learning for Early Detection of Autism in Children: Integrating Gestures and Mood Analysis

autism spectrum disorder (ASD) is characterized by neurological disorders and challenges with interpersonal communication, communication, and schedule behaviour. Early distinguishing proof of ASD is vital to optimize the intervention and progress long-term results. This paper analyses the potential of numerous machine learning procedures for early location of ASD in children. We propose a novel approach that combines gesture assessment with cognitive evaluation to capture a comprehensive picture of a child’s behaviour. Side effect analysis will focus on highlights such as body language, hand movements, and eye contact, which can give profitable bits of knowledge into the social and communication designs of children with ASD. Therapy will utilize facial expressions, tone of voice, and body language to survey a child’s emotional state. The combination of these strategies empowers the machine learning show to recognize complex structures that will be characteristic of ASD. This consider has the potential to move forward the exactness and productivity of ASD diagnosis in children. The proposed strategy can be connected in different situations, such as during routine testing or through a portable application. Early and precise conclusion can clear the way for convenient intervention and back for children with ASD, eventually driving to positive long-term results.