Interpretable and lightweight fall detection in a heritage gallery using YOLOv11-SEFA for edge deployment.

Falls are a critical safety risk in aging societies, causing severe injuries and fatalities, particularly in urban public buildings where elderly visitors frequently gather. Cultural and heritage spaces such as museums and galleries present additional challenges for monitoring due to complex lighting, reflective display cases, and fluctuating visitor densities, underscoring the need for reliable fall detection systems that can be seamlessly deployed without intrusive infrastructure. This study proposes an interpretable, lightweight fall detection and alert system based on the YOLOv11-SEFA architecture. The model integrates P2 feature enhancement and SimAM attention into the YOLOv11n backbone, achieving consistent detection reliability while maintaining low computational cost. A four-layer sensing-to-cloud pipeline is combined with random forest classification of six-dimensional structural features to predict multi-level fall risk, with feature importance analysis verifying aspect ratio, distance to camera, and crowd presence as key predictors aligned with safety logic. The system demonstrates stable performance across confusion matrices, PR curves, and ROC-AUC learning curves, indicating operational feasibility and edge suitability. Practical tests show sub-270 ms latency, low power and bandwidth requirements, and smooth integration into weak-current infrastructures. Pilot validation at Rochfort Gallery, a restored 1920s heritage building in North Sydney, demonstrates feasibility under real-world conditions, supporting future deployment in smart city health and safety applications.