# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 5 Discussion
The results indicated that YOLOv9e demonstrated the best balance between speed and accuracy, offering superior performance compared with earlier versions. This version effectively resolves the trade-off between detection speed and accuracy, making it the most suitable for real-time fall detection. The focus of this study was to develop a network that integrates transformer-based Swin attention with selected YOLO networks, and train this network using a fused fall detection dataset. Building on prior research, we identified effective methods for incorporating a Swin Transformer into the YOLO network architecture. Subsequent optimization efforts involve adjusting the window size specifically for single-class training on the fall detection dataset and determining the most effective types and positions of attention blocks within the network. These optimizations resulted in high accuracy during the experimental trials, demonstrating the potential of the combined YOLO-Swin model for precise and efficient fall detection. During the experiments, researchers initially hypothesized that the latest algorithms would deliver the best performance. However, they found that the most recent network architectures did not necessarily provide the highest accuracy, and that deeper networks did not guarantee better fall detection results. Furthermore, the CBAM, which is commonly regarded as effective in enhancing network performance, unexpectedly led to a decline in the accuracy of the fall detection dataset. In contrast, when the modified GAM was applied, this attention block performed better and improved the detection accuracy. These findings highlight the importance of tailoring network structures and attention mechanisms specifically to the dataset and task at hand rather than relying solely on the latest or most complex architectures. The proposed FD-YOLO network achieved the highest accuracy and lowest FN for fall detection, demonstrating its strong potential for effectively identifying fall incidents in real time. This suggests that it is possible to establish a highly efficient fall detection system without additional hardware installation, even in general CCTV environments. The adaptability and efficiency of this system make it applicable to a wide range of fields, including elder-care safety management systems in aging societies, pedestrian protection systems in public spaces, and fall prevention systems in hospitals and nursing facilities. The scalability of the FD-YOLO network makes it a valuable tool for enhancing safety and intervention in various critical environments. FD-YOLO, which proposes a fall detection network, is specifically designed to maintain high performance not only within a controlled environment but also across diverse real-world settings. This adaptability allows the network to be seamlessly integrated into widely deployed CCTV systems, facilitating the continuous monitoring of fall incidents. Furthermore, the ability of a network to transmit detected events rapidly is expected to be of significant value in various applications, ensuring timely responses and interventions. This capability highlights the potential of the FD-YOLO network as an essential tool to enhance safety and prevent falls in real-world environments. However, this study is limited by its retrospective nature because AI training and network optimization were conducted using specific datasets. In the future, we aim to address this limitation by obtaining data from a variety of environments through multiple experiments conducted in real-world settings and by acquiring prospective data via Institutional Review Board (IRB) approval. This will allow the development of a more robust and generalizable fall detection network. In addition, we plan to conduct follow-up research to assess the practical applicability of the proposed system in real-world scenarios. We also observed that other studies achieved high performance by using data preprocessing techniques to analyze skeletal structures and incorporating this information into AI models.