Cloud-Connected IoT Framework for Real-Time Fall Detection and Assisted Living Support

The rapid growth of aging populations worldwide has increased the demand for intelligent, reliable, and real-time healthcare monitoring solutions. This research proposes a Cloud-Connected IoT Framework for Real-Time Fall Detection and Assisted Living Support, designed to enhance safety, autonomy, and quality of life for elderly individuals. The proposed system integrates wearable and ambient IoT sensors, including accelerometers, gyroscopes, and vital-sign monitoring devices, to continuously capture motion and physiological data. A lightweight edge-processing module performs preliminary fall detection using machine learning algorithms, while cloud infrastructure enables large-scale data storage, advanced analytics, and remote access for caregivers and healthcare professionals. The framework employs sensor fusion techniques to improve fall detection accuracy and reduce false alarms. Real-time alerts are transmitted via secure communication protocols to caregivers, emergency contacts, and healthcare centers upon detecting abnormal motion patterns or confirmed fall events. The cloud layer supports longitudinal health data analysis, predictive risk assessment, and adaptive model updates through continuous learning mechanisms. The system also incorporates data encryption and authentication methods to ensure patient privacy and cybersecurity compliance. Simulation and prototype evaluation demonstrate reduced response time, high detection accuracy, and improved reliability compared to conventional standalone monitoring systems. The proposed cloud-connected IoT architecture provides a scalable, energy-efficient, and robust solution for smart assisted living environments, contributing significantly to next-generation elderly healthcare and remote patient monitoring systems.