An edge-AI enabled wearable platform for real-time epileptic seizure detection with geolocated alerting

Epilepsy remains a major global health concern, particularly in regions where continuous medical monitoring is difficult to implement. This study introduces a wearable system powered by edge-based artificial intelligence, designed to detect epileptic seizures in real time. The device integrates multiple sensors-accelerometers for motion tracking, photoplethysmography (PPG) for cardiovascular monitoring, and GPS for location detection-to enhance reliability through sensor fusion. Multiple machine learning models, including support vector machines (SVM), neural networks (NN), and random forest classifiers, were deployed and assessed directly on the device. Among these, the optimized random forest algorithm achieved the highest accuracy and fastest response time. The fusion of sensor data significantly improved specificity and maintained a very low false alarm rate. When a seizure is detected, the system instantly sends SMS alerts with precise location details to assigned caregivers, enabling prompt medical intervention. Simulation results demonstrate that this cost-effective and self-contained platform offers strong potential for improving patient safety and facilitating rapid response in low-resource settings where conventional monitoring tools are unavailable.