# Fall Recognition Based on Time-Level Decision Fusion Classification
## 3 Fall Detection System  
### Person Detection  
The complete process of the proposed system is depicted in Figure 1. The initial step in the proposed fall detection method is person detection. The inference speed of each module is important for real-time fall detection. Therefore, in this study, the YOLO model [28; 29; 33], a highly regarded real-time detector, was employed for person detection. This allows for the rapid transfer of person detection results to the pose-estimation module. YOLO [33] was designed to overcome the slow inference speed of two-stage detectors. It performs bounding-box regression and classification of the feature maps obtained by passing the CNN without requiring separate region proposals. This approach addresses the low detection speed observed in previous models. In this system, YOLO, which is known for its fast detection speed, was used to construct a real-time fall detection system. A pre-trained YOLOv3 was first employed to detect persons. However, the pre-trained YOLOv3 struggled to effectively detect fallen persons. Consequently, YOLOv7, an enhanced version, was used to conduct additional experiments. YOLOv3 [28] employs a residual connection-based backbone for feature extraction. It regresses the bounding box onto feature maps of various resolutions. YOLOv7 [29] employs a concatenation-based backbone that reuses features learned from previous layers, thus increasing the parameter efficiency. YOLOv7 also embraces the neural architecture search (NAS) to achieve an efficient model through scaling. YOLOv7 exhibits a better Pareto front than YOLOv3. The results of person detection using YOLOv3 and YOLOv7 are shown in Figure 2. Only the region containing the participants was defined as a region of interest, and person detection was performed in this area, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Pros** & **Cons** & **Dataset** \\ \hline \multirow{6}{*}{[PERSON], 2021 [20]} & The authors trained an encoder–decoder model using action features extracted from the pre-trained ST-GCN. By using only data on activities of daily living (ADL) as the dataset for training the encoder–decoder model, it can address the scarcity of fall data. & It is trained on ADL data, not fall data, and if the likelihood of an action as an ADL is below a certain level, it is classified as a fall. So there is a possibility of classifying actions as falls that are neither falls nor ADL. & PRECIS HAR, HAR-UP UR-Fall \\ \hline \multirow{2}{*}{[PERSON], 2023 [19]} & Using the ST-GCN model as a classifier, spatial–temporal information can be considered. & Person detection that precedes the extraction of skeleton data does not work well. & \\ \hline \multirow{6}{*}{[PERSON], 2023 [22]} & The proposed image fusion method converts multiple frames into a richly informative image to detect falls. & Since it is based on segmentation, a person’s clothes and belongings can be reflected in the silhouette and post-processing is required for incorrect segmentation & Le2i fall detection dataset \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._Figure 1: The complete process of the proposed system.  
Figure 2: Results of person detection using YOLOv3 and YOLOv7: (**a**) detection result of YOLOv3 for normal activity; (**b**) detection result of YOLOv3 for fall activity; (**c**) detection result of YOLOv7 for normal activity; and (**d**) detection result of YOLOv7 for fall activity.