Distributed Edge Computing Architecture for Fall Detection in Senior Care Facilities

The integration of high-reliability monitoring systems in senior care facilities has become a critical engineering priority. This paper explores the development of an edge-cloud orchestrated framework designed for real-time fall detection. Traditional centralized systems often face latency bottlenecks and bandwidth constraints that can delay emergency responses during critical incidents. Our research proposes a decentralized architecture where initial data processing occurs at the "edge"—utilizing on-site gateways and wearable sensors—to enable near-instantaneous anomaly detection. The framework was implemented across multiple testbed facilities using a combination of tri-axial accelerometers and infrared occupancy sensors. This study evaluates the trade-off between local processing power and central data storage, focusing on reducing false positives while optimizing the battery life of low-power devices. Findings demonstrate that edge-based inference reduces response latency significantly compared to conventional architectures. This work provides a technical blueprint for smart healthcare infrastructure that prioritizes localized intelligence and resident privacy.