# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 1 Introduction
Consequently, there was an increasing need to develop novel technological approaches to address the limitations inherent in sensor-based systems. To overcome the limitations of sensor-based fall detection systems, research on contactless vision-based fall detection had been actively pursued. The rapid advancements in computer vision had significantly enhanced the accuracy and applicability of such systems. Past computer vision techniques for fall detection had been grounded in mathematical models that captured and analyzed body movements and the spatial relationship between the body and its environment. These approaches included methods for analyzing changes in body posture [11; 12], tracking head position [13], and using three-dimensional (3D) technology to assess vertical body distribution [14]. However, despite their potential, these methods failed to capture specific fall movements or exhibit reduced accuracy under certain environmental conditions. To address these limitations, researchershad explored the use of Kinect cameras, which could provide depth information to detect falls more accurately [15]. The depth-sensing capability of the Kinect camera offered the advantage of improved detection of interactions between the body and the environment, enhancing the precision of fall detection. Nonetheless, this approach presented practical challenges, such as the need for additional hardware installation, high costs, and limitations in the range of accurate depth measurement, which made real-world applications difficult. Similar to the trajectory of sensor-based fall detection research, vision-based studies that increasingly incorporated AI algorithms in the vision base were developed to mitigate these limitations and improve accuracy. These algorithms analyzed complex body movements with enhanced precision, and deep learning techniques demonstrated strong capabilities for processing unstructured data effectively. However, most AI algorithms for vision-based fall detection were developed for cloud-based platforms [16; 17] or integrated into embedded systems [18; 19]. Although cloud-based systems provided high computational power and accuracy, they also incurred additional costs and introduced challenges such as transmission delays and significant concerns regarding data privacy. Conversely, leveraging existing closed-circuit television (CCTV) systems that were already widely deployed for crime prevention and monitoring presented a practical alternative. By utilizing these systems, the need for new hardware installation was mitigated, thereby expanding the potential applicability of AI vision-based fall detection systems in real-world environments. This approach substantially reduced costs and simplified implementation while still allowing for the integration of advanced AI techniques to improve detection accuracy. This study aims to develop an AI network that can be implemented in CCTV systems, which are more cost-effective alternatives to cloud-based platforms, while enhancing fall detection performance. By leveraging advanced neural network techniques, such as attention blocks, this study seeks to address the limitations of existing sensor and vision methods, enabling more accurate and real-time fall detection and transmission. The proposed system not only detects falls in real time but also immediately transmits alerts of fall incidents via CCTV networks, facilitating rapid responses in emergency situations. Consequently, this approach is expected to contribute significantly to reducing fall injuries and protecting life and safety through timely intervention. Furthermore, the development of AI technological advancements will serve as a critical tool for mitigating the risks associated with falls, particularly in the context of an aging society. By improving both the accuracy and efficiency of fall detection, this system has the potential to play a pivotal role in ensuring individual health and safety, promoting swift responses, and ultimately enhancing the quality of life of vulnerable populations.