International Journal of Science, Engineering and TechnologyIOT – Based Fall Detection System
Authors- Associate Professor Y V Nagesh Meesala, Perumalla Harini, Buddala Sravani, Jagupilla Ganesh, Muchu Madhan Mohan
Abstract-This paper presents an IoT-enabled system for fall detection and epilepsy monitoring, integrating accelerometers, Arduino Uno, and NodeMCU to ensure real-time health tracking. The system detects falls and seizures through movement pattern analysis, triggering instant alerts via the Blynk IoT platform. It provides an LCD display for status updates and a buzzer for immediate user alerts. By combining fall detection and seizure monitoring in a single system, this approach enhances safety, enables timely intervention, and simplifies care management for individuals at risk, demonstrating the transformative potential of IoT in healthcare monitoring.
DOI: /10.61463/ijset.vol.13.issue2.197
