Multi-Modal Behavioral AI for Autism Care: A Federated-Edge Framework with Speech, Motion and Physiological Signal Integration

Autism spectrum disorder (ASD) shows various behavioral implications which in most cases progress without prompt treatment. The latest developments in the field of artificial intelligence (AI) and Internet of Things (IoT) provide the possibility of proactive monitoring, but there are issues related to privacy, latency, and multi-modal data integration. In this work, a federated-edge AI system is proposed, which integrates speech recognition, motion detection, and physiological data into a single behavioral analytics pipeline. The framework uses low-latency anomaly detection using edge intelligence, sharing, and securing data with federated learning with the help of differential privacy, and explainable dashboards to gain clinician trust. Accuracy increases of 12% and latency-cut of 58% and more clinician usability ratings are shown by experimental evaluation with synthetic multi-modal datasets, over cloud-only baselines. Clinically relevant, scalable, and trustworthy Multi-modal autism tracking by linking federated-edge AI and multi-modal autism monitoring can enable behavioral health, which this work provides.