## 4 Results
### Fall Detection  
To evaluate the proposed fall detection system, bounding boxes and motion features were extracted from video frames, as detailed in Section 3.3. The training of the proposed 1D residual causal CNN model utilized videos exclusively from the \"office\" scene of the LE2I dataset. The average pose velocity and its temporal variation for a video containing a fall and another without a fall are shown in Figure 12a and Figure 12b, respectively. Given that there were only 33 videos for the \"office\" scene, which is insufficient for training a deep learning model effectively, this study incorporated a data augmentation technique. This approach rolled the feature data along the time-series axis to increase the dataset. Specifically, the time-series data underwent a shift of 25 frames along the time axis for each video frame. Iterating this process and adjusting the frame shift by a factor of 25 frames, the size of the training dataset was substantially increased by 100 times. To illustrate this method, Figure 13 shows (a) the original average pose velocity, (b) the data after a 25-frame shift to the right, and (c) the data following a 25-frame shift to the left, demonstrating the augmentation's effect on the dataset's variation in fall location for the time series. Using this augmentation method, the model was trained for 100 epochs, and the model's weights were saved for inference from the rest of the dataset. This study utilized the two benchmark datasets mentioned in Section 4.1 to evaluate the proposed model. The model trained on the \"coffee\" scene of the LE2I dataset was tested on the rest of the LE2I dataset scenes and also on the URFD dataset. Given the binary classification nature of the fall detection, the evaluation metrics employed in this research are detailed below [33]:  
* Accuracy: The percentage of items correctly classified, calculated as (TP + TN)/(TP + TN + FP + FN). * Precision: The proportion of true-positive identifications among all positive predictions, calculated as TP/(TP + FP). Figure 11: Subject anonymization process in “home” scene with person sitting on a sofa: (**a**) original frame, (**b**) foreground removal, (**c**) skeleton representation, and (**d**) 2D avatar. * Recall/sensitivity: The true-positive rate or the model's ability to identify positive instances accurately, calculated as TP/(TP + FN). * Specificity: Often used together with recall, specificity measures the true-negative rate or the model's ability to correctly identify negative instances, calculated as TN/(TN + FP). * F1-Score: This denotes the harmonic mean between precision and recall, calculated as 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall)  
Table 1 presents the frame-wise evaluation results for the LE2I and URFD datasets using the proposed fall detection method. For the LE2I dataset, the method achieved an accuracy of 98.86%, a precision of 90.02%, a sensitivity of 91.11%, a specificity of 99.35%, and an F1-Score of 0.905. In comparison, on the URFD dataset, the method attained an accuracy of 96.23%, a precision of 97.18%, a sensitivity of 92.56%, a specificity of 98.40%, and an F1-Score of 0.948. These results indicate that the proposed method performs accurately across different datasets, excelling particularly in specificity, reducing false alarms while maintaining high accuracy and precision. The results of the proposed method are comparable to those of the methods of the original datasets [80,81] and other state-of-the-art methods [33,34] evaluated with these datasets. Furthermore, when evaluating the system on a video-wise basis, both accuracy and precision reached 100% for the tested datasets. This suggests that, while frame-wise metrics provide insight into the method's performance at a granular level, the video-wise evaluation showcases the system's ability to accurately classify fall or non-fall events in entire video sequences with perfect precision.