🤖 AI Summary
Existing fall detection methods suffer from poor portability, high privacy risks, and weak generalization—particularly under low-resolution video inputs and complex daily activities. To address these limitations, we propose a privacy-preserving, edge-deployable fall detection framework based on lightweight skeletal sequence extraction. Our approach introduces a structure-aware discriminative feature aggregation mechanism that jointly models joint-level geometric topology and motion dynamics. We further propose a novel Separable-Convolution-Enhanced Graph Convolutional Network (SE-GCN), which achieves superior discriminability while significantly reducing computational overhead. Evaluated on five benchmark datasets, our method achieves state-of-the-art accuracy, with inference speed substantially accelerated and FLOPs reduced by over 60%, enabling real-time execution on resource-constrained edge devices.
📝 Abstract
Older people are susceptible to fall due to instability in posture and deteriorating health. Immediate access to medical support can greatly reduce repercussions. Hence, there is an increasing interest in automated fall detection, often incorporated into a smart health-care system to provide better monitoring. Existing systems focus on wearable devices that are inconvenient or video monitoring that has privacy concerns. Moreover, these systems provide a limited perspective of their generalization ability as they are tested on datasets containing few activities that have wide disparity in the action space and are easy to differentiate. Complex daily life scenarios pose much greater challenges with activities that overlap in action spaces due to similar posture or motion. To overcome these limitations, we propose a fall detection model, called structure-aware discriminative feature aggregation, based on human skeletons extracted from low-resolution videos. The use of skeleton data ensures privacy and low-resolution videos ensures low hardware and computational cost. Our model captures discriminative structural displacements and motion trends using unified joint and motion features projected onto a shared high-dimensional space. Particularly, the use of separable convolution combined with a powerful graph convolutional network architecture provides improved performance. Extensive experiments on five large-scale datasets with a wide range of evaluation settings show that our model achieves competitive performance with extremely low computational complexity and runs faster than existing models.