# Fall Recognition Based on Time-Level Decision Fusion Classification
## 1 Introduction  
As perceptions of safety in society change and legal regulations for safety become stronger, the demand for safety management is increasing. However, there is currently a shortage of personnel for safety management, and efficiency is not at its optimum level. An efficient safety management system is essential for addressing these challenges. A recent and increasing trend in research focuses on the incorporation of artificial intelligence technologies into safety management systems [1; 2; 3; 4]. By utilizing artificial intelligence, it is possible to detect and respond to accidents involving onsite workers and facility users in real time, thereby minimizing additional damage. In South Korea, accidents caused by falls are the most common type of industrial disasters [5]. Therefore, in this study, we propose an algorithm for real-time fall detection using state-of-the-art deep learning models for a smart safety management system. In parallel with our research, a range of investigations are underway to detect falls using diverse sensor technologies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. These include wearable devices, accelerometers, radars, and video sensors. Furthermore, researchers are actively exploring the possibility of fall detection in multimodal scenarios by utilizing a combination of different sensors [23; 24; 25; 26; 27]. However, when employing a wearable sensor [6; 7; 8; 9], an individual must wear the sensor for fall monitoring, which is inconvenient, and concerns about battery life arise. Moreover, additional equipment is required to use accelerometers or radar sensors [10; 11; 12; 13], and, especially in the radar case, the falls of objects may be mistaken as human falls. Utilizing multimodal approaches to detect falls can enhance performance; however, a potential drawbackis processing time degradation owing to the additional tasks required for aligning data from multiple sensors. Currently, numerous cameras are installed in various locations. Hence, adopting video sensors for fall detection allows seamless integration with already established camera systems, eliminating the requirement for additional hardware and ensuring cost-effectiveness. Moreover, this approach eliminates the need for users to fulfill specific conditions, resulting in reduced user resistance. Therefore, we aim to detect falls using video sensors. Recently, with the emergence of deep learning, significant advancements have been made in computer vision technology, leading to an increasing number of attempts to solve various issues using computer vision techniques. In particular, the utilization of video data has made it possible to detect falls, and several studies have shown promising outcomes [14; 15; 16; 17; 18; 19; 20; 21; 22]. In line with this trend, we propose an algorithm for fall detection using deep learning-based computer vision technology. The proposed fall detection system comprises four modules: object detection, pose estimation, action recognition, and result fusion. The first module, the object detection module, determines the presence of a person and, if detected, provides an output regarding the person's location in the video. In the second module, the pose estimation module obtains the output from the first module and processes the image section containing the person (rather than the entire image). This process generates skeleton data for pose estimation. The third module, the action recognition module, recognizes the pattern of changes in the joint points of the individual in the video to output the fall detection results. In the fourth module, the result fusion module, the results from the third module are fused using various methods to enhance the robustness and performance, ultimately producing the final result. The main contributions of this study are as follows:  
1. Using a time-level ensemble, we aggregate the continuous outcomes of the modules. Thus, the system has enhanced robustness and overcomes challenges under difficult conditions.