# Fall Recognition Based on Time-Level Decision Fusion Classification
## 4 Experimental Results
### Dataset and Experimental Setup  
The HAR-UP dataset [27] was used to test the proposed system. The HAR-UP dataset consists of 11 categorized actions, each performed by 17 subjects in three trials and captured from two camera viewpoints. Among the 11 actions, 1-5 represent falls, whereas 6-11 depict different actions. In this study, the remaining seven actions, excluding falls, were categorized as normal actions, and the experiments were conducted accordingly. Furthermore, the sixth action in the dataset involves subjects moving out of the camera's field of view and returning, making it challenging to define the region of interest (  
Figure 8 shows the results obtained using YOLOv3 for person detection, and Figure 9 shows the results obtained using YOLOv7. It can be observed that using both YOLOv3 and YOLOv7, the performance improved compared to that before fusion. With the use of YOLOv3 and YOLOv7, we observed instances where the fusion model exhibited superior performance compared to the baseline model. Specifically, the application of time-level fusion contributed to performance enhancement. When AlphaPose was used, the enhancement in the performance of the fusion model was significantly more substantial compared to the utilization of ViTPose. This signifies the efficacy of the fusion technique in effectively mitigating fluctuations in the skeleton sequence or classifier results arising from certain inaccuracies in AlphaPose estimation. Notably, the performance was consistently higher with the following fusion methods: probabilistic fusion, average, maximum, and voting. When YOLOv3 was used, the combination of ViTPose and probabilistic fusion with the number of classifiers being 10 exhibited the highest performance. However, when YOLOv7 was employed, the best performance was achieved using ViTPose and probabilistic fusion with the number of classifiers being five. YOLOv7 performed better than YOLOv3, and ViTPose performed better than AlphaPose. In general, accuracy tended to decrease when the number of classifiers exceeded 15. The results for all 1016 videos using the optimal numbers of classifiers for each fusion method are presented in Tables 5 and 6. In a binary classification problem, true (T) and false (F) indicate whether the predicted result is correct or not. Positive (P) and negative (N) indicate cases in which the model classifies the state as falling and normal, respectively. Precision, recall, and accuracy, presented in Tables 5 and 6, are the most widely used indices for data learning of classifiers and are defined as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{21}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{22}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN} \tag{23}\]  
Figure 9: Accuracy graph of each fusion method using YOLOv7. With the utilization of YOLOv3, the optimal precision for detecting fall cases was achieved by combining ViTPose with the voting method. Conversely, for normal cases, the baseline performance with ViTPose demonstrated the highest precision. Regarding recall, the baseline with ViTPose exhibited superior performance for fall cases, while the optimal result for normal cases was obtained by employing ViTPose and the voting method. In the context of YOLOv7 utilization, the highest precision for fall cases was observed with ViTPose and the voting method, whereas for normal cases, the highest precision was attained through the combination of ViTPose and the averaging method. Concerning recall, ViTPose combined with the averaging method resulted in the highest performance for fall cases, while ViTPose combined with the voting method proved to be the most effective strategy for normal cases. Table 7 presents a comparison of the recognition results for each action between ViTPose and AlphaPose when using the probabilistic fusion method and YOLOv7, which is the most effective.