Playsense: Game-driven Diagnostic Tool for Autism Spectrum Disorder (ASD)

Author(s)	Ms. YAMUNA T, Prof. MANI A
Country	India
Abstract	This study presents a new framework for predicting the likelihood of Autism Spectrum Disorder (ASD) in children. It uses a multifaceted approach that includes personalized gaming, quizzing, and MRI analysis. The process starts with participants entering their details and playing specially designed puzzle games. These games assess cognitive abilities and reveal behavioral patterns. After completing the games, participants move on to a quiz that focuses on social interaction and communication skills, which allows for a broader assessment of behavior. Next, the system analyzes brain MRI images using the VGG16 algorithm to extract features related to ASD. By combining the results of behavioral assessments with neuroimaging data, the framework aims to provide a more detailed prediction of ASD likelihood. This offers important insights for early detection and intervention. This integrated method not only improves prediction accuracy but also highlights the importance of considering both behavioral and neurological factors in understanding and identifying ASD in children.
Keywords	Autism Spectrum Disorder (ASD), Game-Based Assessment, Behavioral Analysis, Early Diagnosis, Gamification, Machine Learning, Cognitive Evaluation, Digital Health Tool, Human–Computer Interaction, Play Therapy.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-19
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.61783

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