## 4 Experiments
### Comparative Studies of the Effectiveness and Limitations of the Proposed System  
[PERSON], 2013 [24] proposed an approach to detect falling events in a simulated home environment. The processes of the system are motion detection and tracking using background subtraction. The extracted binary image is used to construct the coordinates of the bounding box, aspect ratios, and ellipse orientation. Then, \(SVM\) and the Adaboost-based method are utilized to classify falls. This system is robust regarding the location changes and taking into account a tolerance on the instant of detection.  
The proposed system by [PERSON], 2018 [25] extracts four visual features: motion history image (_MHI_), aspect ratio, orientation angles using ellipse approximation, and the thresholds below the referenced line. After that, these features are inputted into five different machine learning algorithms (i.e., _SVM_, _K-NN_, _SGD_, _GB_ and _DT_) to recognize falling problems. The _DT_ provides the optimal result to confirm the effectiveness of the system. The limitations of the applied methods are not widely discussed and the deep learning models could be utilized to improve the detection rates in future work.  
The system proposed by [PERSON], [PERSON], 2019 [26] investigated the effectiveness of motion information by using _tMHI_, the variations of shape which are fixed in the approximated ellipse around the silhouette body and the standard deviation of the difference for both horizontal and vertical histograms. Then, the neural network is applied to detect falls. The limitations are that the performance of the system depends heavily on multiple fixed thresholds. Thus, it is essential to judge the thresholds which are the best for detecting falls. In addition, these threshold parameters are needed to observe the adaptation for different persons in the monitoring system.  
In our proposed system, background subtraction using _MOG_ and foreground refinement using graph cut are performed to obtain the silhouette images with low loss of useful information. Then, the concept of _VGP_ and the related visual features are properly presented to retrieve significant abnormal and normal action patterns. In the analysis component, we emphasize that detection of a falling point within the falling period depends on the modified difference of moving average. Finally, these features are put into _SVM_ and _PD_. The proposed system shows the effectiveness of the system with high detection rates. The system scope is limited to attaining a real-time monitoring system due to the time-consuming object detection techniques that provide good foreground images. The current research works focuses on day-time visual abnormal and normal event detection. However, providing a 24-h service requires extending the monitoring period to include night-time monitoring. Moreover, a better understanding of the environment and of human-object interaction should be developed to create an improved home-care monitoring system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **Accuracy** \\ \hline [PERSON] 2013 [24] & 95.91\% & 92.15\% & 99.42\% \\ [PERSON] 2018 [25] & 95\% & 94\% & 95\% \\ [PERSON] 2019 [26] & 100\% & 95.27\% & 99.82\% \\ Ours (_SVM_) & 93.33\% & 100\% & 100\% \\ Ours (_PD_) & 93.33\% & 93.33\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with existing approaches using the same dataset.