Autism Monitoring in Children and Elders Using IOT and Machine Learning with Real-Time Safety Alerts for Caregivers.

Autism Spectrum Disorder (ASD) presents unique challenges in monitoring behavioral patterns, emotional responses, and daily activities, particularly among children and elderly individuals who may struggle to communicate effectively. Traditional caregiving methods often rely on manual observation, which can lead to delayed responses in critical situations such as wandering, emotional distress, or sudden health fluctuations. To address these limitations, this project proposes an IoT- and Machine Learning-based Autism Monitoring System that continuously observes and analyzes the physiological and behavioral parameters of autistic individuals in real time. The proposed system integrates various IoT sensors including heart rate, body temperature, motion, and GPS tracking modules to collect continuous data from wearable or environmental devices. This data is transmitted to a cloud-based platform for processing, where Machine Learning algorithms are employed to detect anomalies, recognize emotional or behavioral patterns, and predict potential risks such as stress or agitation. By combining sensor data with intelligent analytics, the system ensures accurate monitoring of both physical and behavioral health indicators. A key feature of the system is its real-time safety alert mechanism for caregivers and healthcare professionals. When the system detects abnormal activities such as erratic movement, elevated heart rate, or prolonged inactivity instant notifications are sent via mobile or web applications, enabling immediate intervention. This intelligent monitoring framework not only enhances the safety and independence of autistic individuals but also provides caregivers with actionable insights, ensuring proactive care and improved quality of life through continuous, data-driven support.