02067nas a2200241   4500000000100000000000100001008004100002260001200043653002000055653002300075653001900098653003600117653002600153100001200179700001400191700001500205245008300220856005800303300000900361490001300370520142800383022001401811       9998                            d  c09/202410aComputer vision10aElderly Protection10aFall Detection10aGraph Convolution Network (GCN)10aHuman Pose Estimation1 aLei Liu1 aYeguo Sun1 aXianlei Ge00aA Hybrid Multi-Person Fall Detection Scheme Based on Optimized YOLO and ST-GCN  uhttps://www.ijimai.org/journal/bibcite/reference/3491  a1-130 vIn press3 aHuman falls are a serious health issue for elderly and disabled people living alone. Studies have shown that if fallers could be helped immediately after a fall, it would greatly reduce their risk of death and the percentage of them requiring long-term treatment. As a real-time automatic fall detection solution, vision-based human fall detection technology has received extensive attention from researchers. In this paper, a hybrid model based on YOLO and ST-GCN is proposed for multi-person fall detection application scenarios. The solution uses the ST-GCN model based on a graph convolutional network to detect the fall action, and enhances the model with YOLO for accurate and fast recognition of multi-person targets. Meanwhile, our scheme accelerates the model through optimization methods to meet the model's demand for lightweight and real-time performance. Finally, we conducted performance tests on the designed prototype system and using both publicly available single-person datasets and our own multi-person dataset. The experimental results show that under better environmental conditions, our model possesses high detection accuracy compared to state-of-the-art schemes, while it significantly outperforms other models in terms of inference speed. Therefore, this hybrid model based on YOLO and ST-GCN, as a preliminary attempt, provides a new solution idea for multi-person fall detection for the elderly.  a1989-1660