Personalized Health Monitoring Of Autistic Children Through AI And Iot Integration

Abstract

Autism spectrum disorder (ASD) has complex and variable traits that require ongoing and flexible monitoring. Traditional assessments of health, which are based upon observation by caregivers and periodic clinical visits, are not able to capture day-to-day variation in physiology and behavior. This paper proposes a personalized health monitoring system that combines the artificial intelligence (AI) and Internet of Things (IoT) devices to support autistic children. The system combines wearable sensors, anomaly detection models and privacy-preserving learning to detect behavioral and physiological patterns of stress in real time. Simulation findings indicate increased accuracy of anomaly detection, improved response of caregivers and fewer false alarms when compared to baseline monitoring systems. The study concludes that the integration of AI with IoT holds a good opportunity for personalized care to support both kids and caregivers while remaining ethical and compliant with regulations.